Cost
$0.21per task
Cost per task. One Intelligence Index task at list price, every token counted. Run at max effort.
Claude Haiku model by Anthropic
Claude Haiku 5.5
Anthropic's small model for high-volume, latency-sensitive tasks such as classification, extraction, and routing. API model claude-haiku-5-5, 1M-token context.
Where it sits in Anthropic's lineup
- Claude HaikuAnthropic's small, fast Claude model family for high-volume, latency-sensitive work.This model
- Claude SonnetAnthropic's balanced Claude model family.Claude Sonnet 5.5
- Claude OpusAnthropic's high-capability Claude model family.Claude Opus 5.5
- Claude FableAnthropic's Claude family for demanding reasoning and long-horizon agentic work.Claude Fable 5.1
- Stack role
- Foundation Model / Reasoning Engine
- Offered in
- 6 products
- Made by
- Anthropic
- Family
- Claude Haiku, version 5.5
- Released
- Oct 7, 2026 Current
- In its family
- The newest of 2 Claude Haiku releases.
Strengths and weaknesses
Compare with other models Up to 3 beside Claude Haiku 5.5
- Claude Haiku 5.5
Suggested comparisons
The models a buyer weighs against Claude Haiku 5.5, picked on list price and on Artificial Analysis's Intelligence Index of overall quality. Add one to draw it beside Claude Haiku 5.5.
Claude Haiku 5.5
Among the 40 current releases on this chart, Claude Haiku 5.5 ranks 8th of 25 on Answers in full and 13th of 38 on Long context. On Computer use, Anthropic's own figures on OSWorld 2.1 put Claude Haiku 5.5 14.8 points under Claude Opus 5.5, 11.5 points under Claude Sonnet 5.5 and 23.5 points above GPT-6 Luna. It ranks lowest on Science, 22nd of 24 and Tool use, 33rd of 38. Multimodal: not yet published by Anthropic or any independent evaluator, checked Nov 2, 2026.
Each axis is one public benchmark at one setting, on its own fixed 0 to 100 scale, so adding a model never moves another. A dotted bridge crosses a missing result, which is never a zero. A hollow mark is a result from another source: the maker's own figure or another lab's run, where the benchmark's usual source has not tested the model yet. The readout names it.
On the chart
- Claude Haiku 5.5This pageResults on 11 of 12 axes$0.10 / $0.50For prompts up to 100K tokens; $0.50 / $2.50 above.241 tokens/sFirst token in 311 s
Prices and speeds read between Oct 26, 2026 and Oct 28, 2026.
Claude Haiku 5.5 on each axis11 of 12 with a result
The share of Terminal-Bench 4.0's long terminal tasks the model finishes on its own: services, kernels, proofs and data pipelines, graded on the final artifact with no partial credit.
Terminal-Bench 4.0, pass@1 averaged over 3 runs, mini-swe-agent harness
- Claude Haiku 5.536.4%
The share of SciCode's scientific programming subproblems the model's code solves on the first attempt.
SciCode, subproblem accuracy, pass@1, highest effort run
- Claude Haiku 5.555%
The share of FrontierMath's held-out research-grade problems (tiers 1 to 3) the model answers correctly.
FrontierMath Tiers 1-3, accuracy, pass@1, highest effort run
- Claude Haiku 5.575.1%
The share of GPQA Diamond's graduate-level biology, chemistry and physics questions the model answers correctly.
GPQA Diamond, accuracy, pass@1, highest effort run
- Claude Haiku 5.589.6%
The share of Humanity's Last Exam's 2,158 text-only questions, expert-written at the edge of human knowledge, the model answers correctly without tools.
Humanity's Last Exam, accuracy, text-only questions, no tools
- Claude Haiku 5.544.4%
The share of each AutomationBench task's objectives the model completes by calling REST APIs across simulated business apps (Gmail, Sheets, Slack, Salesforce, Zendesk, Jira, HubSpot), averaged over tasks. A task that breaks one of its policy guardrails scores zero.
AutomationBench-AA, score, Artificial Analysis run
- Claude Haiku 5.535.4%
How much of each OSWorld 2.1 workflow the model completes by operating a real Ubuntu desktop through screenshots, mouse and keyboard: all 108 long tasks across apps, files, mail and browsers, on the September 2026 v2.1 release of the task files. A point is the mean share of each task's weighted checks the model passes (the partial score, as a percentage). The OSWorld team's own runs come first. Where the team has not run a release, the point is another lab's run or the maker's own published figure, marked with who ran it and the benchmark it ran: OSWorld 2.1 in the maker's harness, OSWorld 2.0's offline set or the earlier OSWorld-Verified. Those points are drawn so the release has a figure, and they are not ranked against the team's runs.
OSWorld 2.1, partial score, full 108-task set, v2.1 release. Where it has no run of a model, the maker's own figure or another lab's run stands in, marked with its benchmark.
- Claude Haiku 5.572.4%reported by AnthropicBenchmark: OSWorld 2.1, partial score, offline 82-task subset, v2.1 release, where the axis reads full 108-task set. From another source, so it is drawn and marked but not ranked with the others.Anthropic's own run at max effort in its computer-use harness, averaged over five attempts, on the 82 tasks that need no internet. It fully completed 37.1% of them.
The share of AA-LCR's questions the model answers correctly by reasoning across long sets of real documents, about 100K tokens each.
AA-LCR v1.1, accuracy, Artificial Analysis run
- Claude Haiku 5.582.7%
The share of MMMU-Pro's college-level questions over charts, diagrams and photos the model answers correctly.
MMMU-Pro, overall accuracy, no tools
- Claude Haiku 5.5Not yet published by Anthropic or any independent evaluator, checked Nov 2, 2026
The share of AA-Omniscience's 6,000 knowledge questions across 42 economically relevant topics the model answers correctly from memory, without search.
AA-Omniscience Accuracy, accuracy, Artificial Analysis run
- Claude Haiku 5.534.7%Hallucination rate 34.2% in the same run (lower is better)
The share of SpeechMap's 2,120 sensitive and controversial but legal requests the model answers in full: arguments, essays and explanations on contested themes in politics, religion, race, sex, history and similar topics, four wordings per theme across 530 themes. SpeechMap's judge rates each response complete, evasive (hedged or redirected), a denial, or an error (the provider's API blocked the request). Only complete answers count, so the point is the share answered in full: 100 minus the share hedged, refused or blocked by the provider. Higher means more lawful requests answered in full. The share blocked at the provider is printed apart in the table, because an API error is not the model declining. Where SpeechMap runs a release both with reasoning off and with reasoning on (a -reasoning or -thinking entry), the reasoning-on entry is used; a release with one entry uses that entry.
SpeechMap, % Complete over 2,120 contested requests, reasoning-on entry
- Claude Haiku 5.578.8%SpeechMap tested it at medium thinking and counted 18.3% of requests denied and 2.5% evaded.0.5% of requests failed at the provider and count against it
The share of MultiChallenge's multi-turn conversations in which the model keeps every instruction it was given earlier in the conversation.
MultiChallenge, accuracy, Scale AI run
- Claude Haiku 5.567.7%On Oct 28, 2026, Scale AI ranked it 10th. Scale does not say which reasoning setting it tested.Plus or minus 5.34 points (95% interval)
Cost, speed, capacity, quality and accuracy for Claude Haiku 5.5, with where each figure sits among the 40 models GILD tracks. These measures stand beside the chart and never feed it.
Speed
234tokens a second
Output speed. How fast it writes once the answer starts. Run at max effort.
- First token
- 335 s
- Whole answer
- 337 s
Capacity
1Mtokens
Context window. The most one request can hold, as the maker states it.
- Max output
- 128K
- Reads
- Text, Images
Quality
43.4of 100
Intelligence Index. One composite score across independent evaluations. Run at max effort.
- GDPval knowledge work
- 1,619 Elo
- Terminal-Bench 4.0
- 32.8%
- Epoch Capabilities Index
- 159
Hallucination
34.2%wrong when it should say “I don’t know”
Hallucination rate. Of the questions it can't get right, how often it answers anyway. Lower is better. Run at max effort.
- Knowledge accuracy
- 34.7%
Where you can get it
12of 19 routes
Routes offering it. Of the clouds, gateways and coding products GILD tracks, those offering it today.
- Open weights
- No
- Licence
- Commercial Terms of Service
Figures from Artificial Analysis, OpenRouter, Epoch AI, GILD's route matrix and Anthropic, read Oct 26 to Nov 1, 2026. Each measure's definition and source open under . Not yet published for Claude Haiku 5.5: Cached input.
Every value with its benchmark, run and source
| Axis | Benchmark and setting | Claude Haiku 5.5 | Run | Source | Date |
|---|---|---|---|---|---|
| Agentic coding | Terminal-Bench 4.0Pass@1 averaged over 3 runs, mini-swe-agent harness | 36.4% | Claude Haiku 5.5Effort: not published. Harness: mini-swe-agent. | Vals.ai | Oct 24, 2026 |
| Code | SciCodeSubproblem accuracy, pass@1, highest effort run | 55% | Claude Haiku 5.5 (Max)Effort: max. Harness: none. | Artificial Analysis | Oct 28, 2026 |
| Math | FrontierMath Tiers 1-3Accuracy, pass@1, highest effort run | 75.1% | Effort: max. Harness: none. | Epoch AI | Oct 7, 2026 |
| Science | GPQA DiamondAccuracy, pass@1, highest effort run | 89.6% | Effort: max. Harness: none. | Epoch AI | Oct 7, 2026 |
| Hard reasoning | Humanity's Last ExamAccuracy, text-only questions, no tools | 44.4% | Claude Haiku 5.5 (Max)Effort: max. Harness: none. | Artificial Analysis | Oct 28, 2026 |
| Tool use | AutomationBench-AAScore, Artificial Analysis run | 35.4% | Claude Haiku 5.5 (Max)Effort: max. Harness: Artificial Analysis AutomationBench harness. | Artificial Analysis | Oct 28, 2026 |
| Computer use | OSWorld 2.1Partial score, offline 82-task subset, v2.1 release. Anthropic's own run at max effort in its computer-use harness, averaged over five attempts, on the 82 tasks that need no internet. It fully completed 37.1% of them.Stands in for OSWorld 2.1, partial score, full 108-task set, v2.1 release, which has no run of Claude Haiku 5.5 yet. | 72.4% | Claude Haiku 5.5 (max)Effort: max. Harness: Anthropic computer-use harness. | AnthropicReported by Anthropic | Oct 7, 2026 |
| Long context | AA-LCR v1.1Accuracy, Artificial Analysis run | 82.7% | Claude Haiku 5.5 (Max)Effort: max. Harness: none. | Artificial Analysis | Oct 28, 2026 |
| Multimodal | MMMU-ProOverall accuracy, no tools | Not yet published by Anthropic or any independent evaluator, checked Nov 2, 2026 | |||
| Factuality | AA-Omniscience AccuracyAccuracy, Artificial Analysis run | 34.7%Hallucination rate 34.2% in the same run (lower is better) | Claude Haiku 5.5 (Max)Effort: max. Harness: none. | Artificial Analysis | Oct 28, 2026 |
| Answers in full | SpeechMap% Complete over 2,120 contested requests, reasoning-on entry. SpeechMap tested it at medium thinking and counted 18.3% of requests denied and 2.5% evaded. | 78.8%0.5% of requests failed at the provider and count against it | Effort: medium. Harness: none. | SpeechMap.AI | Oct 27, 2026 |
| Instruction following | MultiChallengeAccuracy, Scale AI run. On Oct 28, 2026, Scale AI ranked it 10th. Scale does not say which reasoning setting it tested. | 67.7%Plus or minus 5.34 points (95% interval) | Claude Haiku 5.5Effort: not published. Harness: none. | Scale AI SEAL | Oct 28, 2026 |
Price and speed
- Claude Haiku 5.5 lists at $0.10 per million input tokens and $0.50 per million output tokens. For prompts up to 100K tokens; $0.50 / $2.50 above. Anthropic pricing, Oct 26, 2026.
- Claude Haiku 5.5, at max effort, writes 240.7 output tokens per second on Anthropic's API, and its first token arrives 311 s after the request. Artificial Analysis, Oct 28, 2026.
- Fastest on the chart is the shortest time to a 500-token answer: time to first token plus 500 tokens at the median output speed.
Every model with a result, axis by axis
57 models. A maker's own figure and another lab's run are marked with their benchmark; a missing result reads "Not yet published", never zero.
| Model | Agentic coding | Code | Math | Science | Hard reasoning | Tool use | Computer use | Long context | Multimodal | Factuality | Answers in full | Instructions |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Claude Haiku 5.5 | 36.4% | 55% | 75.1% | 89.6% | 44.4% | 35.4% | 72.4%Reported by AnthropicOSWorld 2.1 | 82.7% | Not yet published | 34.7% | 78.8% | 67.7% |
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| Claude Sonnet 5.5 | 64.1% | 61% | 88.8% | 95.6% | 55% | 71.8% | 80.1%Reported by Anthropic | 82.7% | Not yet published | 51.5% | 81.6% | Not yet published |
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| Claude Opus 5.5 | 65.2% | 66.9% | 91.2% | 90.6% | 61.4% | 69.5% | 81.8%Reported by Anthropic | 84.7% | 87.7% | 65.5% | 82.8% | Not yet published |
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| Claude Fable 5.1 | 58.1% | 63.1% | 90.2% | 93.4%Run by Vals.ai | 59.1% | 59.4% | 80.7%Reported by Anthropic | 85.3% | 86.3%Run by Moonshot AI | 68.4% | 72.4% | Not yet published |
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| Claude Opus 5 | 53.5% | 56.4% | 85.6% | 93.9% | 54.9% | 56.6% | 77.7% | 79.3% | 84.7% | 60.9% | 73.7% | Not yet published |
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| Claude Sonnet 5 | 10.6% | 54.3% | 65.6% | 80.3% | 41.3% | 36.5% | 57%Reported by Anthropic | 82% | 77.3% | 40.1% | Not yet published | Not yet published |
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| Claude Fable 5 | 41.4% | 61% | 87% | 85.9% | 55.5% | 54.1% | Not yet published | 82.3% | Not yet published | 65.4% | 64.8% | 63.5%Run by Artificial AnalysisIFBench |
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| Claude Opus 4.8 | 23.2% | 54.4% | 80% | 91% | 48.7% | 45.6% | 54.8%Run by OSWorld teamOSWorld 2.0 | 77.7% | Not yet published | 48.8% | 67.4% | 62.2%Run by Artificial AnalysisIFBench |
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| Claude Opus 4.7 | Not yet published | 54.5% | 70.2% | 86.4% | 42.3% | Not yet published | 48.9%Run by OSWorld teamOSWorld 2.0 | 78.7% | 78.8% | 48.9% | 70.2% | 58.6%Run by Artificial AnalysisIFBench |
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| Claude Sonnet 4.6 | 3%Run by Artificial AnalysisTerminal-Bench 4.0 | 50.1% | 71.1%Run by Epoch AIOTIS Mock AIME 2024-2025 | 78.8% | 33.6% | 20.1% | 41.5%Run by OSWorld teamOSWorld 2.0 | 80% | 73.3% | 40.9% | 35.8% | 56.6%Run by Artificial AnalysisIFBench |
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| Claude Opus 4.6 | Not yet published | Not yet published | 66% | 88.4% | 39.9% | Not yet published | Not yet published | 78% | 75.4% | 47% | 59.9% | 37.2% |
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| Claude Opus 4.5 | Not yet published | Not yet published | 34.4% | 86% | 30.1% | Not yet published | Not yet published | 77.3% | 74% | 46.6% | 57.5% | 59% |
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| Claude Haiku 4.5 | Not yet published | 42.2% | 66.7%Run by Epoch AIOTIS Mock AIME 2024-2025 | 71.2% | 10.4% | 3.2% | Not yet published | 74.3% | 58.6% | 18% | 24.7% | 50.5% |
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| Claude Sonnet 4.5 | Not yet published | 45.7% | 23.9% | 82.3% | 17.8% | 14.1% | Not yet published | 72.3% | 68.7% | 32.9% | 31.4% | 55.3% |
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| GPT-6.1 Terra | 49.5% | 55.1% | 90.9% | 94.3% | 46.2% | 60.2% | 74.2%Reported by OpenAIOSWorld 2.1 | 82.7% | 84.7% | 47.6% | Not yet published | 74.2% |
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| GPT-6.1 Sol | 55.1% | 54.2% | 93.7% | 95.4% | 52.9% | 64.9% | 78%Reported by OpenAIOSWorld 2.1 | 83% | 86% | 61.8% | 69.5% | 82% |
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| GPT-6 Luna | 13.6% | 54.6% | 78.9% | 90.5% | 38.5% | 53.2% | 48.9%Run by AnthropicOSWorld 2.1 | 83.3% | 79.7% | 39.6% | 63.4% | Not yet published |
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| GPT-6 Astra | 59.6% | 56.5% | 93.7% | 95.8% | 54.7% | 68.5% | 75.8%Run by Meta | 80.7% | 86.9% | 61.6% | 68.2% | Not yet published |
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| GPT-5.6 Sol | 37.9% | 57.1% | 89.1% | 93.5% | 49.5% | 60.1% | 62.7%Run by OSWorld teamOSWorld 2.0 | 84% | 83.4% | 69.7%Run by Epoch AISimpleQA Verified | Not yet published | 72.7%Run by Artificial AnalysisIFBench |
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| GPT-5.6 Terra | 22.7% | 55% | 86% | 93.3% | 42.9% | 59.6% | Not yet published | 83% | 80.7% | 42.3% | Not yet published | 71.2%Run by Artificial AnalysisIFBench |
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| GPT-5.6 Luna | 11.6% | 53.6% | 82.1% | 91.6% | 39.5% | 50.2% | Not yet published | 83.7% | 78.6% | 41%Run by Epoch AISimpleQA Verified | Not yet published | Not yet published |
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| GPT-5.5 | 14.6%Run by Artificial AnalysisTerminal-Bench 4.0 | 55.8% | 85.3% | 93.5%Run by Artificial Analysis | 45.8% | 47.3% | 49.5%Run by OSWorld teamOSWorld 2.0 | 84.3% | 79.9% | 63%Run by Epoch AISimpleQA Verified | Not yet published | 75.9%Run by Artificial AnalysisIFBench |
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| GPT-5.4 | Not yet published | Not yet published | 78.6% | 93.3% | 43.7% | Not yet published | Not yet published | 82% | 78.4% | Not yet published | Not yet published | 73.9%Run by Artificial AnalysisIFBench |
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| gpt-oss-120b | 14.2%Run by Terminal-Bench (via Epoch AI)Terminal-Bench 2.0 | 34% | 88.9%Run by Epoch AIOTIS Mock AIME 2024-2025 | 75.8% | 19.6% | 0.2% | Not yet published | 52% | Not yet published | 16.8%Reported by OpenAISimpleQA | 36.3% | 45.3% |
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| GPT-6 Sol | 44.4% | 57.6% | 89.8% | 94.3% | 47.9% | 61.6% | 64.4%Reported by OpenAIOSWorld 2.0 | 83.7% | 82.9% | 54.5% | 64% | Not yet published |
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| GPT-5.3-Codex | Not yet published | Not yet published | Not yet published | 91.5%Run by Artificial Analysis | 42.5% | Not yet published | Not yet published | 83.3% | 78.5% | Not yet published | Not yet published | 75.4%Run by Artificial AnalysisIFBench |
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| Gemini 4 Argon | 57.6% | 61.8% | 86% | 96.4% | 57.1% | 77.5% | 69.2%Reported by GoogleOSWorld 2.0 | 79.7% | Not yet published | 48.3% | 87% | 78.7% |
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| Gemini 3.8 Flash | 19.7% | 56.6% | 68.4% | 95.4% | 47.8% | 59.9% | 59%Reported by GoogleOSWorld 2.0 | 81.3% | 85.6% | 50.5% | 86.1% | Not yet published |
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| Gemma 4 31B | Not yet published | 45.5% | Not yet published | 85.7%Run by Artificial AnalysisGPQA Diamond | 23.6% | 4.9% | Not yet published | 69.7% | 73.4% | 10.4%Run by Epoch AISimpleQA Verified | 82.3% | 75.6%Run by Artificial AnalysisIFBench |
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| Gemini 3.1 Pro Preview | 2.5% | 58.7% | 59.6% | 94.4% | 47% | 35.4% | Not yet published | 82% | 82.4% | 54.9% | 59.9% | 71.4% |
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| Gemini 3.6 Flash | 7.1%Run by Artificial AnalysisTerminal-Bench 4.0 | 53.4% | 58.9% | 94.1% | 40.8% | 53% | 83%Reported by GoogleOSWorld-Verified | 80% | 83.2% | 66.2%Run by Epoch AISimpleQA Verified | 87.2% | Not yet published |
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| DeepSeek V4.1 Flash | 31.8% | 55.9% | 71.2% | 93.2% | 43.3% | 66.6% | 69.3%Run by Moonshot AIOSWorld 2.1 | 82.7% | 80.6% | 39.6% | Not yet published | 74.9% |
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| DeepSeek V4 Pro | 14.1% | 51% | 64.6% | 91.7% | 41% | 56.7% | Not yet published | 80.3% | Not yet published | 45.2% | 72% | Not yet published |
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| Kimi K3.1 Pro | 63.6% | 60.8% | Not yet published | 94.2%Reported by Moonshot AI | 52.5% | 73.3% | 79.6%Reported by Moonshot AIOSWorld 2.1 | 86.3% | 83.9% | 48.9% | Not yet published | Not yet published |
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| Kimi K3 | 17.2% | 59.5% | 72.2% | 93.1% | 46.9% | 58.3% | Not yet published | 88.7% | 80.5% | 50.6%Run by Epoch AISimpleQA Verified | 65.9% | Not yet published |
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| Kimi K2.7 Code | 1%Run by Artificial AnalysisTerminal-Bench 4.0 | 47.8% | 54% | 87.9% | 35% | 24.5% | Not yet published | 79.3% | Not yet published | 36.5%Run by Epoch AISimpleQA Verified | Not yet published | 63.1%Run by Artificial AnalysisIFBench |
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| Mistral Large 4 | 24.7%Run by Artificial AnalysisTerminal-Bench 4.0 | Not yet published | Not yet published | Not yet published | Not yet published | 57.8% | Not yet published | Not yet published | Not yet published | 26% | Not yet published | Not yet published |
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| Mistral Medium 3.5 | 0%Run by Artificial AnalysisTerminal-Bench 4.0 | 40.2% | Not yet published | 74.8%Run by Artificial AnalysisGPQA Diamond | 13.8% | 6.3% | Not yet published | 69.3% | 64.9% | 23% | 70.5% | 68.8%Run by Artificial AnalysisIFBench |
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| Grok 4.7 | 28.8% | 57.4% | 53% | 92.7% | 43.1% | 65.6% | Not yet published | 76.7% | Not yet published | 42.7% | 64.1% | Not yet published |
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| Grok 4.6 | 17.2% | 56.5% | 66% | 93.2% | 42.9% | 66.7% | Not yet published | 80.3% | Not yet published | 48.9%Run by Epoch AISimpleQA Verified | 83.6% | Not yet published |
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| Grok 4.5 | 8.6% | 55% | 57.2% | 93.4% | 42.7% | 57.9% | Not yet published | 79.3% | 80.4% | 48.3%Run by Epoch AISimpleQA Verified | 93.1% | Not yet published |
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| MiniMax M3.1 Flash | 25.3% | 53.4% | Not yet published | Not yet published | 48% | 72.5% | 61.7%Reported by MiniMaxOSWorld 2.0 | 79.7% | 79.1% | 45.1% | Not yet published | Not yet published |
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| Mercury 3 | 3% | Not yet published | Not yet published | Not yet published | Not yet published | Not yet published | Not yet published | Not yet published | Not yet published | Not yet published | Not yet published | Not yet published |
| GLM-5.3-Flash | 37.4% | 51.6% | 55.8% | 90.2% | 39.9% | 60.4% | 59.1%Reported by Z.aiOSWorld 2.0 | 80% | 86%Run by Vals.aiMMMU-Pro | 28.2% | 65.4% | Not yet published |
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| GLM-5.3 | 38.9% | 59% | 68.8% | 90.9% | 42.3% | 62.2% | Not yet published | 79.7% | Not yet published | 32.3% | 71.5% | Not yet published |
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| GLM-5.2 | 1%Run by Artificial AnalysisTerminal-Bench 4.0 | 51.2% | 59.2% | Not yet published | 41.1% | 28.4% | Not yet published | 78.3% | Not yet published | 34.2%Run by Epoch AISimpleQA Verified | Not yet published | 73.3%Run by Artificial AnalysisIFBench |
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| Muse Spark Open | 6.6% | 58.4% | 69.8% | 88.6% | 43.9% | 49.7% | 80.4%Reported by MetaOSWorld-Verified | 78.3% | 80.9% | 42% | Not yet published | 74.2% |
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| Muse Wonder | 49.5% | 60.3% | 82.1% | Not yet published | 50.1% | 68.1% | 72.6%Reported by Meta | 80.7% | Not yet published | 38% | 42.7% | Not yet published |
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| Muse Spark 1.3 | 10.6% | 58.8% | 74% | 93.5%Run by Artificial AnalysisGPQA Diamond | 48.7% | 57.9% | 69.6% | 83% | 82% | 38% | 25.8% | Not yet published |
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| Llama 4 Scout | Not yet published | 21.3% | 7.8%Run by Epoch AIOTIS Mock AIME 2024-2025 | 51.8% | 3.8% | 0.2% | Not yet published | 27.7% | 52.9% | Not yet published | 70.1% | 39.5%Run by Artificial AnalysisIFBench |
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| Llama 4 Maverick | Not yet published | 31.7% | 20.6%Run by Epoch AIOTIS Mock AIME 2024-2025 | 67% | 4.9% | 0.2% | Not yet published | 50% | 62.1% | Not yet published | 58.3% | 43%Run by Artificial AnalysisIFBench |
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| Qwen3.8 27B | 5.6%Run by Artificial AnalysisTerminal-Bench 4.0 | 46.6% | Not yet published | 90.5%Run by Artificial AnalysisGPQA Diamond | 33.9% | 48.2% | 84.3%Reported by AlibabaOSWorld-Verified | 82% | 76.3% | 17.3% | Not yet published | 79.5%Reported by AlibabaIFBench |
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| Qwen3.8 Max | 38.9% | 52.1% | 65.6% | 92.3% | 43.1% | 56.2% | Not yet published | 80.3% | 82.8% | 29.5% | 65.3% | Not yet published |
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| MiMo-V2.6-Pro | 31.3% | 60.9% | Not yet published | Not yet published | 49.4% | 58.6% | 82%Reported by XiaomiOSWorld-Verified | 86.3% | Not yet published | 34.8% | 66.5% | Not yet published |
| ||||||||||||
| MiMo-V2.6-Flash | 24.2% | 51.3% | Not yet published | Not yet published | 35.1% | 64.1% | 80.8%Reported by XiaomiOSWorld-Verified | 74.3% | 73.1% | 27.2% | 66.9% | Not yet published |
| ||||||||||||
| Ling 3.1 Flash | 32.8%Run by Artificial AnalysisTerminal-Bench 4.0 | 56.8% | Not yet published | Not yet published | 43.5% | 67.5% | Not yet published | 82.3% | Not yet published | 26.5% | Not yet published | Not yet published |
| ||||||||||||
| Step 5 Preview | 31.8% | 58.9% | Not yet published | 93.5%Reported by StepFunGPQA Diamond | 46.5% | 51% | Not yet published | 88.3% | 76.4% | 36.4% | 59.7% | Not yet published |
| ||||||||||||
Agentic codingTerminal-Bench 4.0
- Claude Opus 5.565.2%
- Claude Sonnet 5.564.1%
- Kimi K3.1 Pro63.6%
- GPT-6 Astra59.6%
- Claude Fable 5.158.1%
- Gemini 4 Argon57.6%
- GPT-6.1 Sol55.1%
- Claude Opus 553.5%
- GPT-6.1 Terra49.5%
- Muse Wonder49.5%
- GPT-6 Sol44.4%
- Claude Fable 541.4%
- GLM-5.338.9%
- Qwen3.8 Max38.9%
- GPT-5.6 Sol37.9%
- GLM-5.3-Flash37.4%
- Claude Haiku 5.536.4%
- DeepSeek V4.1 Flash31.8%
- Step 5 Preview31.8%
- MiMo-V2.6-Pro31.3%
- Grok 4.728.8%
- MiniMax M3.1 Flash25.3%
- MiMo-V2.6-Flash24.2%
- Claude Opus 4.823.2%
- GPT-5.6 Terra22.7%
- Gemini 3.8 Flash19.7%
- Kimi K317.2%
- Grok 4.617.2%
- DeepSeek V4 Pro14.1%
- GPT-6 Luna13.6%
- GPT-5.6 Luna11.6%
- Claude Sonnet 510.6%
- Muse Spark 1.310.6%
- Grok 4.58.6%
- Muse Spark Open6.6%
- Mercury 33%
- Gemini 3.1 Pro Preview2.5%
- Ling 3.1 Flash32.8%Artificial Analysis's run, Terminal-Bench 4.0
- Mistral Large 424.7%Artificial Analysis's run, Terminal-Bench 4.0
- GPT-5.514.6%Artificial Analysis's run, Terminal-Bench 4.0
- gpt-oss-120b14.2%Terminal-Bench's run, Terminal-Bench 2.0
- Gemini 3.6 Flash7.1%Artificial Analysis's run, Terminal-Bench 4.0
- Qwen3.8 27B5.6%Artificial Analysis's run, Terminal-Bench 4.0
- Claude Sonnet 4.63%Artificial Analysis's run, Terminal-Bench 4.0
- Kimi K2.7 Code1%Artificial Analysis's run, Terminal-Bench 4.0
- GLM-5.21%Artificial Analysis's run, Terminal-Bench 4.0
- Mistral Medium 3.50%Artificial Analysis's run, Terminal-Bench 4.0
The other models in the grid have no result on Terminal-Bench 4.0 yet.
CodeSciCode
- Claude Opus 5.566.9%
- Claude Fable 5.163.1%
- Gemini 4 Argon61.8%
- Claude Sonnet 5.561%
- Claude Fable 561%
- MiMo-V2.6-Pro60.9%
- Kimi K3.1 Pro60.8%
- Muse Wonder60.3%
- Kimi K359.5%
- GLM-5.359%
- Step 5 Preview58.9%
- Muse Spark 1.358.8%
- Gemini 3.1 Pro Preview58.7%
- Muse Spark Open58.4%
- GPT-6 Sol57.6%
- Grok 4.757.4%
- GPT-5.6 Sol57.1%
- Ling 3.1 Flash56.8%
- Gemini 3.8 Flash56.6%
- GPT-6 Astra56.5%
- Grok 4.656.5%
- Claude Opus 556.4%
- DeepSeek V4.1 Flash55.9%
- GPT-5.555.8%
- GPT-6.1 Terra55.1%
- Claude Haiku 5.555%
- GPT-5.6 Terra55%
- Grok 4.555%
- GPT-6 Luna54.6%
- Claude Opus 4.754.5%
- Claude Opus 4.854.4%
- Claude Sonnet 554.3%
- GPT-6.1 Sol54.2%
- GPT-5.6 Luna53.6%
- Gemini 3.6 Flash53.4%
- MiniMax M3.1 Flash53.4%
- Qwen3.8 Max52.1%
- GLM-5.3-Flash51.6%
- MiMo-V2.6-Flash51.3%
- GLM-5.251.2%
- DeepSeek V4 Pro51%
- Claude Sonnet 4.650.1%
- Kimi K2.7 Code47.8%
- Qwen3.8 27B46.6%
- Claude Sonnet 4.545.7%
- Gemma 4 31B45.5%
- Claude Haiku 4.542.2%
- Mistral Medium 3.540.2%
- gpt-oss-120b34%
- Llama 4 Maverick31.7%
- Llama 4 Scout21.3%
The other models in the grid have no result on SciCode yet.
MathFrontierMath Tiers 1-3
- GPT-6.1 Sol93.7%
- GPT-6 Astra93.7%
- Claude Opus 5.591.2%
- GPT-6.1 Terra90.9%
- Claude Fable 5.190.2%
- GPT-6 Sol89.8%
- GPT-5.6 Sol89.1%
- Claude Sonnet 5.588.8%
- Claude Fable 587%
- GPT-5.6 Terra86%
- Gemini 4 Argon86%
- Claude Opus 585.6%
- GPT-5.585.3%
- GPT-5.6 Luna82.1%
- Muse Wonder82.1%
- Claude Opus 4.880%
- GPT-6 Luna78.9%
- GPT-5.478.6%
- Claude Haiku 5.575.1%
- Muse Spark 1.374%
- Kimi K372.2%
- DeepSeek V4.1 Flash71.2%
- Claude Opus 4.770.2%
- Muse Spark Open69.8%
- GLM-5.368.8%
- Gemini 3.8 Flash68.4%
- Claude Opus 4.666%
- Grok 4.666%
- Claude Sonnet 565.6%
- Qwen3.8 Max65.6%
- DeepSeek V4 Pro64.6%
- Gemini 3.1 Pro Preview59.6%
- GLM-5.259.2%
- Gemini 3.6 Flash58.9%
- Grok 4.557.2%
- GLM-5.3-Flash55.8%
- Kimi K2.7 Code54%
- Grok 4.753%
- Claude Opus 4.534.4%
- Claude Sonnet 4.523.9%
- gpt-oss-120b88.9%Epoch AI's run, OTIS Mock AIME 2024-2025
- Claude Sonnet 4.671.1%Epoch AI's run, OTIS Mock AIME 2024-2025
- Claude Haiku 4.566.7%Epoch AI's run, OTIS Mock AIME 2024-2025
- Llama 4 Maverick20.6%Epoch AI's run, OTIS Mock AIME 2024-2025
- Llama 4 Scout7.8%Epoch AI's run, OTIS Mock AIME 2024-2025
The other models in the grid have no result on FrontierMath Tiers 1-3 yet.
ScienceGPQA Diamond
- Gemini 4 Argon96.4%
- GPT-6 Astra95.8%
- Claude Sonnet 5.595.6%
- GPT-6.1 Sol95.4%
- Gemini 3.8 Flash95.4%
- Gemini 3.1 Pro Preview94.4%
- GPT-6.1 Terra94.3%
- GPT-6 Sol94.3%
- Gemini 3.6 Flash94.1%
- Claude Opus 593.9%
- GPT-5.6 Sol93.5%
- Grok 4.593.4%
- GPT-5.6 Terra93.3%
- GPT-5.493.3%
- DeepSeek V4.1 Flash93.2%
- Grok 4.693.2%
- Kimi K393.1%
- Grok 4.792.7%
- Qwen3.8 Max92.3%
- DeepSeek V4 Pro91.7%
- GPT-5.6 Luna91.6%
- Claude Opus 4.891%
- GLM-5.390.9%
- Claude Opus 5.590.6%
- GPT-6 Luna90.5%
- GLM-5.3-Flash90.2%
- Claude Haiku 5.589.6%
- Muse Spark Open88.6%
- Claude Opus 4.688.4%
- Kimi K2.7 Code87.9%
- Claude Opus 4.786.4%
- Claude Opus 4.586%
- Claude Fable 585.9%
- Claude Sonnet 4.582.3%
- Claude Sonnet 580.3%
- Claude Sonnet 4.678.8%
- gpt-oss-120b75.8%
- Claude Haiku 4.571.2%
- Llama 4 Maverick67%
- Llama 4 Scout51.8%
- Kimi K3.1 Pro94.2%Moonshot AI's own figure
- GPT-5.593.5%Artificial Analysis's run
- Muse Spark 1.393.5%Artificial Analysis's run, GPQA Diamond
- Step 5 Preview93.5%StepFun's own figure, GPQA Diamond
- Claude Fable 5.193.4%Vals.ai's run
- GPT-5.3-Codex91.5%Artificial Analysis's run
- Qwen3.8 27B90.5%Artificial Analysis's run, GPQA Diamond
- Gemma 4 31B85.7%Artificial Analysis's run, GPQA Diamond
- Mistral Medium 3.574.8%Artificial Analysis's run, GPQA Diamond
The other models in the grid have no result on GPQA Diamond yet.
Hard reasoningHumanity's Last Exam
- Claude Opus 5.561.4%
- Claude Fable 5.159.1%
- Gemini 4 Argon57.1%
- Claude Fable 555.5%
- Claude Sonnet 5.555%
- Claude Opus 554.9%
- GPT-6 Astra54.7%
- GPT-6.1 Sol52.9%
- Kimi K3.1 Pro52.5%
- Muse Wonder50.1%
- GPT-5.6 Sol49.5%
- MiMo-V2.6-Pro49.4%
- Claude Opus 4.848.7%
- Muse Spark 1.348.7%
- MiniMax M3.1 Flash48%
- GPT-6 Sol47.9%
- Gemini 3.8 Flash47.8%
- Gemini 3.1 Pro Preview47%
- Kimi K346.9%
- Step 5 Preview46.5%
- GPT-6.1 Terra46.2%
- GPT-5.545.8%
- Claude Haiku 5.544.4%
- Muse Spark Open43.9%
- GPT-5.443.7%
- Ling 3.1 Flash43.5%
- DeepSeek V4.1 Flash43.3%
- Grok 4.743.1%
- Qwen3.8 Max43.1%
- GPT-5.6 Terra42.9%
- Grok 4.642.9%
- Grok 4.542.7%
- GPT-5.3-Codex42.5%
- Claude Opus 4.742.3%
- GLM-5.342.3%
- Claude Sonnet 541.3%
- GLM-5.241.1%
- DeepSeek V4 Pro41%
- Gemini 3.6 Flash40.8%
- Claude Opus 4.639.9%
- GLM-5.3-Flash39.9%
- GPT-5.6 Luna39.5%
- GPT-6 Luna38.5%
- MiMo-V2.6-Flash35.1%
- Kimi K2.7 Code35%
- Qwen3.8 27B33.9%
- Claude Sonnet 4.633.6%
- Claude Opus 4.530.1%
- Gemma 4 31B23.6%
- gpt-oss-120b19.6%
- Claude Sonnet 4.517.8%
- Mistral Medium 3.513.8%
- Claude Haiku 4.510.4%
- Llama 4 Maverick4.9%
- Llama 4 Scout3.8%
The other models in the grid have no result on Humanity's Last Exam yet.
Tool useAutomationBench-AA
- Gemini 4 Argon77.5%
- Kimi K3.1 Pro73.3%
- MiniMax M3.1 Flash72.5%
- Claude Sonnet 5.571.8%
- Claude Opus 5.569.5%
- GPT-6 Astra68.5%
- Muse Wonder68.1%
- Ling 3.1 Flash67.5%
- Grok 4.666.7%
- DeepSeek V4.1 Flash66.6%
- Grok 4.765.6%
- GPT-6.1 Sol64.9%
- MiMo-V2.6-Flash64.1%
- GLM-5.362.2%
- GPT-6 Sol61.6%
- GLM-5.3-Flash60.4%
- GPT-6.1 Terra60.2%
- GPT-5.6 Sol60.1%
- Gemini 3.8 Flash59.9%
- GPT-5.6 Terra59.6%
- Claude Fable 5.159.4%
- MiMo-V2.6-Pro58.6%
- Kimi K358.3%
- Grok 4.557.9%
- Muse Spark 1.357.9%
- Mistral Large 457.8%
- DeepSeek V4 Pro56.7%
- Claude Opus 556.6%
- Qwen3.8 Max56.2%
- Claude Fable 554.1%
- GPT-6 Luna53.2%
- Gemini 3.6 Flash53%
- Step 5 Preview51%
- GPT-5.6 Luna50.2%
- Muse Spark Open49.7%
- Qwen3.8 27B48.2%
- GPT-5.547.3%
- Claude Opus 4.845.6%
- Claude Sonnet 536.5%
- Claude Haiku 5.535.4%
- Gemini 3.1 Pro Preview35.4%
- GLM-5.228.4%
- Kimi K2.7 Code24.5%
- Claude Sonnet 4.620.1%
- Claude Sonnet 4.514.1%
- Mistral Medium 3.56.3%
- Gemma 4 31B4.9%
- Claude Haiku 4.53.2%
- gpt-oss-120b0.2%
- Llama 4 Scout0.2%
- Llama 4 Maverick0.2%
The other models in the grid have no result on AutomationBench-AA yet.
Computer useOSWorld 2.1
- Claude Opus 577.7%
- Muse Spark 1.369.6%
- Qwen3.8 27B84.3%Alibaba's own figure, OSWorld-Verified
- Gemini 3.6 Flash83%Google's own figure, OSWorld-Verified
- MiMo-V2.6-Pro82%Xiaomi's own figure, OSWorld-Verified
- Claude Opus 5.581.8%Anthropic's own figure
- MiMo-V2.6-Flash80.8%Xiaomi's own figure, OSWorld-Verified
- Claude Fable 5.180.7%Anthropic's own figure
- Muse Spark Open80.4%Meta's own figure, OSWorld-Verified
- Claude Sonnet 5.580.1%Anthropic's own figure
- Kimi K3.1 Pro79.6%Moonshot AI's own figure, OSWorld 2.1
- GPT-6.1 Sol78%OpenAI's own figure, OSWorld 2.1
- GPT-6 Astra75.8%Meta's run
- GPT-6.1 Terra74.2%OpenAI's own figure, OSWorld 2.1
- Muse Wonder72.6%Meta's own figure
- Claude Haiku 5.572.4%Anthropic's own figure, OSWorld 2.1
- DeepSeek V4.1 Flash69.3%Moonshot AI's run, OSWorld 2.1
- Gemini 4 Argon69.2%Google's own figure, OSWorld 2.0
- GPT-6 Sol64.4%OpenAI's own figure, OSWorld 2.0
- GPT-5.6 Sol62.7%OSWorld team's run, OSWorld 2.0
- MiniMax M3.1 Flash61.7%MiniMax's own figure, OSWorld 2.0
- GLM-5.3-Flash59.1%Z.ai's own figure, OSWorld 2.0
- Gemini 3.8 Flash59%Google's own figure, OSWorld 2.0
- Claude Sonnet 557%Anthropic's own figure
- Claude Opus 4.854.8%OSWorld team's run, OSWorld 2.0
- GPT-5.549.5%OSWorld team's run, OSWorld 2.0
- Claude Opus 4.748.9%OSWorld team's run, OSWorld 2.0
- GPT-6 Luna48.9%Anthropic's run, OSWorld 2.1
- Claude Sonnet 4.641.5%OSWorld team's run, OSWorld 2.0
The other models in the grid have no result on OSWorld 2.1 yet.
Long contextAA-LCR v1.1
- Kimi K388.7%
- Step 5 Preview88.3%
- Kimi K3.1 Pro86.3%
- MiMo-V2.6-Pro86.3%
- Claude Fable 5.185.3%
- Claude Opus 5.584.7%
- GPT-5.584.3%
- GPT-5.6 Sol84%
- GPT-5.6 Luna83.7%
- GPT-6 Sol83.7%
- GPT-6 Luna83.3%
- GPT-5.3-Codex83.3%
- GPT-6.1 Sol83%
- GPT-5.6 Terra83%
- Muse Spark 1.383%
- Claude Haiku 5.582.7%
- Claude Sonnet 5.582.7%
- GPT-6.1 Terra82.7%
- DeepSeek V4.1 Flash82.7%
- Claude Fable 582.3%
- Ling 3.1 Flash82.3%
- Claude Sonnet 582%
- GPT-5.482%
- Gemini 3.1 Pro Preview82%
- Qwen3.8 27B82%
- Gemini 3.8 Flash81.3%
- GPT-6 Astra80.7%
- Muse Wonder80.7%
- DeepSeek V4 Pro80.3%
- Grok 4.680.3%
- Qwen3.8 Max80.3%
- Claude Sonnet 4.680%
- Gemini 3.6 Flash80%
- GLM-5.3-Flash80%
- Gemini 4 Argon79.7%
- MiniMax M3.1 Flash79.7%
- GLM-5.379.7%
- Claude Opus 579.3%
- Kimi K2.7 Code79.3%
- Grok 4.579.3%
- Claude Opus 4.778.7%
- GLM-5.278.3%
- Muse Spark Open78.3%
- Claude Opus 4.678%
- Claude Opus 4.877.7%
- Claude Opus 4.577.3%
- Grok 4.776.7%
- Claude Haiku 4.574.3%
- MiMo-V2.6-Flash74.3%
- Claude Sonnet 4.572.3%
- Gemma 4 31B69.7%
- Mistral Medium 3.569.3%
- gpt-oss-120b52%
- Llama 4 Maverick50%
- Llama 4 Scout27.7%
The other models in the grid have no result on AA-LCR v1.1 yet.
MultimodalMMMU-Pro
- Claude Opus 5.587.7%
- GPT-6 Astra86.9%
- GPT-6.1 Sol86%
- Gemini 3.8 Flash85.6%
- Claude Opus 584.7%
- GPT-6.1 Terra84.7%
- Kimi K3.1 Pro83.9%
- GPT-5.6 Sol83.4%
- Gemini 3.6 Flash83.2%
- GPT-6 Sol82.9%
- Qwen3.8 Max82.8%
- Gemini 3.1 Pro Preview82.4%
- Muse Spark 1.382%
- Muse Spark Open80.9%
- GPT-5.6 Terra80.7%
- DeepSeek V4.1 Flash80.6%
- Kimi K380.5%
- Grok 4.580.4%
- GPT-5.579.9%
- GPT-6 Luna79.7%
- MiniMax M3.1 Flash79.1%
- Claude Opus 4.778.8%
- GPT-5.6 Luna78.6%
- GPT-5.3-Codex78.5%
- GPT-5.478.4%
- Claude Sonnet 577.3%
- Step 5 Preview76.4%
- Qwen3.8 27B76.3%
- Claude Opus 4.675.4%
- Claude Opus 4.574%
- Gemma 4 31B73.4%
- Claude Sonnet 4.673.3%
- MiMo-V2.6-Flash73.1%
- Claude Sonnet 4.568.7%
- Mistral Medium 3.564.9%
- Llama 4 Maverick62.1%
- Claude Haiku 4.558.6%
- Llama 4 Scout52.9%
- Claude Fable 5.186.3%Moonshot AI's run
- GLM-5.3-Flash86%Vals.ai's run, MMMU-Pro
The other models in the grid have no result on MMMU-Pro yet.
FactualityAA-Omniscience Accuracy
- Claude Fable 5.168.4%
- Claude Opus 5.565.5%
- Claude Fable 565.4%
- GPT-6.1 Sol61.8%
- GPT-6 Astra61.6%
- Claude Opus 560.9%
- Gemini 3.1 Pro Preview54.9%
- GPT-6 Sol54.5%
- Claude Sonnet 5.551.5%
- Gemini 3.8 Flash50.5%
- Claude Opus 4.748.9%
- Kimi K3.1 Pro48.9%
- Claude Opus 4.848.8%
- Gemini 4 Argon48.3%
- GPT-6.1 Terra47.6%
- Claude Opus 4.647%
- Claude Opus 4.546.6%
- DeepSeek V4 Pro45.2%
- MiniMax M3.1 Flash45.1%
- Grok 4.742.7%
- GPT-5.6 Terra42.3%
- Muse Spark Open42%
- Claude Sonnet 4.640.9%
- Claude Sonnet 540.1%
- GPT-6 Luna39.6%
- DeepSeek V4.1 Flash39.6%
- Muse Wonder38%
- Muse Spark 1.338%
- Step 5 Preview36.4%
- MiMo-V2.6-Pro34.8%
- Claude Haiku 5.534.7%
- Claude Sonnet 4.532.9%
- GLM-5.332.3%
- Qwen3.8 Max29.5%
- GLM-5.3-Flash28.2%
- MiMo-V2.6-Flash27.2%
- Ling 3.1 Flash26.5%
- Mistral Large 426%
- Mistral Medium 3.523%
- Claude Haiku 4.518%
- Qwen3.8 27B17.3%
- GPT-5.6 Sol69.7%Epoch AI's run, SimpleQA Verified
- Gemini 3.6 Flash66.2%Epoch AI's run, SimpleQA Verified
- GPT-5.563%Epoch AI's run, SimpleQA Verified
- Kimi K350.6%Epoch AI's run, SimpleQA Verified
- Grok 4.648.9%Epoch AI's run, SimpleQA Verified
- Grok 4.548.3%Epoch AI's run, SimpleQA Verified
- GPT-5.6 Luna41%Epoch AI's run, SimpleQA Verified
- Kimi K2.7 Code36.5%Epoch AI's run, SimpleQA Verified
- GLM-5.234.2%Epoch AI's run, SimpleQA Verified
- gpt-oss-120b16.8%OpenAI's own figure, SimpleQA
- Gemma 4 31B10.4%Epoch AI's run, SimpleQA Verified
The other models in the grid have no result on AA-Omniscience Accuracy yet.
Answers in fullSpeechMap
- Grok 4.593.1%
- Gemini 3.6 Flash87.2%
- Gemini 4 Argon87%
- Gemini 3.8 Flash86.1%
- Grok 4.683.6%
- Claude Opus 5.582.8%
- Gemma 4 31B82.3%
- Claude Sonnet 5.581.6%
- Claude Haiku 5.578.8%
- Claude Opus 573.7%
- Claude Fable 5.172.4%
- DeepSeek V4 Pro72%
- GLM-5.371.5%
- Mistral Medium 3.570.5%
- Claude Opus 4.770.2%
- Llama 4 Scout70.1%
- GPT-6.1 Sol69.5%
- GPT-6 Astra68.2%
- Claude Opus 4.867.4%
- MiMo-V2.6-Flash66.9%
- MiMo-V2.6-Pro66.5%
- Kimi K365.9%
- GLM-5.3-Flash65.4%
- Qwen3.8 Max65.3%
- Claude Fable 564.8%
- Grok 4.764.1%
- GPT-6 Sol64%
- GPT-6 Luna63.4%
- Claude Opus 4.659.9%
- Gemini 3.1 Pro Preview59.9%
- Step 5 Preview59.7%
- Llama 4 Maverick58.3%
- Claude Opus 4.557.5%
- Muse Wonder42.7%
- gpt-oss-120b36.3%
- Claude Sonnet 4.635.8%
- Claude Sonnet 4.531.4%
- Muse Spark 1.325.8%
- Claude Haiku 4.524.7%
The other models in the grid have no result on SpeechMap yet.
Instruction followingMultiChallenge
- GPT-6.1 Sol82%
- Gemini 4 Argon78.7%
- DeepSeek V4.1 Flash74.9%
- GPT-6.1 Terra74.2%
- Muse Spark Open74.2%
- Gemini 3.1 Pro Preview71.4%
- Claude Haiku 5.567.7%
- Claude Opus 4.559%
- Claude Sonnet 4.555.3%
- Claude Haiku 4.550.5%
- gpt-oss-120b45.3%
- Claude Opus 4.637.2%
- Qwen3.8 27B79.5%Alibaba's own figure, IFBench
- GPT-5.575.9%Artificial Analysis's run, IFBench
- Gemma 4 31B75.6%Artificial Analysis's run, IFBench
- GPT-5.3-Codex75.4%Artificial Analysis's run, IFBench
- GPT-5.473.9%Artificial Analysis's run, IFBench
- GLM-5.273.3%Artificial Analysis's run, IFBench
- GPT-5.6 Sol72.7%Artificial Analysis's run, IFBench
- GPT-5.6 Terra71.2%Artificial Analysis's run, IFBench
- Mistral Medium 3.568.8%Artificial Analysis's run, IFBench
- Claude Fable 563.5%Artificial Analysis's run, IFBench
- Kimi K2.7 Code63.1%Artificial Analysis's run, IFBench
- Claude Opus 4.862.2%Artificial Analysis's run, IFBench
- Claude Opus 4.758.6%Artificial Analysis's run, IFBench
- Claude Sonnet 4.656.6%Artificial Analysis's run, IFBench
- Llama 4 Maverick43%Artificial Analysis's run, IFBench
- Llama 4 Scout39.5%Artificial Analysis's run, IFBench
The other models in the grid have no result on MultiChallenge yet.
Every measure on record
Cost, speed, reliability, knowledge-work and domain results for Claude Haiku 5.5 beyond the twelve chart axes, each with the run it comes from and its publisher. These stay separate measures; none is folded into the chart.
| Measure | Claude Haiku 5.5 | Run | Source | Read |
|---|---|---|---|---|
| Specs and access | ||||
| Context windowThe most tokens one request can hold, as the maker states it. Size alone says nothing about how well the model uses a long input; the Long context axis measures that. | 1M | Artificial Analysis | Oct 26, 2026 | |
| Max outputThe most tokens the model writes in one response. | 128K | no Anthropic figure in the stored maker sources; OpenRouter lists 128,000 | OpenRouter | Oct 28, 2026 |
| ReadsThe kinds of input the model accepts. | Text, Images | Artificial Analysis | Oct 26, 2026 | |
| LicenceThe terms the weights or the service are offered under. | Commercial Terms of Service | Anthropic | Nov 1, 2026 | |
| Open weightsWhether the maker publishes the weights for anyone to run. | No | Artificial Analysis | Oct 26, 2026 | |
| Routes offering itOf the 19 clouds, gateways and coding products GILD tracks, how many offer the release today, preview included. | 12 of 19 | GILD route matrix | Oct 28, 2026 | |
| Cost | ||||
| Answer tokens per taskLower is better. Answer tokens per Intelligence Index task, reasoning excluded. | 33,118 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Cost per task, cache readsLower is better. The cache-read share of AA's cost per Intelligence Index task. | $0.095 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Cost per task, cache writesLower is better. The cache-write share of AA's cost per Intelligence Index task. | $0.034 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Cost per task, inputLower is better. The input-token share of AA's cost per Intelligence Index task. | $0.13 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Cost per task, outputLower is better. The answer-token share of AA's cost per Intelligence Index task. | $0.081 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Cost per task, reasoningLower is better. The reasoning-token share of AA's cost per Intelligence Index task. | $0.065 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Cost per taskLower is better. What one Intelligence Index task cost AA at list price, input, cache, output and reasoning tokens together. | $0.21 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Cost to run the indexLower is better. What the whole Intelligence Index run cost AA at list price. The runs cost from tens of dollars to about $13,000, so the axis is log from $10 to $100,000. | $330.25 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Output tokens per taskLower is better. Tokens the model wrote per Intelligence Index task, reasoning included: how verbose it is. | 162,164 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Reasoning tokens per taskLower is better. Reasoning tokens per Intelligence Index task. | 129,047 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Speed | ||||
| End-to-end time, 100K promptLower is better. Median seconds for a whole response with a 100,000-token prompt. | 423 s | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| End-to-end response timeLower is better. Seconds AA measures for a whole response on its standard prompt. | 337 s | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Time to first answer token, 100K promptLower is better. Median seconds to the first answer token with a 100,000-token prompt. | 421 s | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Time to first answer tokenLower is better. Seconds until the first answer token, after any reasoning, on AA's API runs. | 335 s | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Output speed, 100K promptMedian output tokens per second when the prompt is about 100,000 tokens long. | 244 tokens/s | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Output speedMedian output tokens per second on AA's API runs, with the 5th and 95th percentile when AA prints them. | 234 tokens/s | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Time per taskLower is better. Wall-clock seconds per Intelligence Index task. Runs take from about 15 s to about 33 min, so the axis is log from 1 s to 10,000 s. | 425 s | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Time to first token, 100K promptLower is better. Median seconds to the first chunk when the prompt is about 100,000 tokens long: long-input latency, separate from window size. | 421 s | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Time to first tokenLower is better. Median seconds to the first streamed chunk on AA's API runs, with the 5th and 95th percentile when AA prints them. Reasoning models spend this time thinking. | 335 s | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Reliability | ||||
| Hallucination rateLower is better. AA-Omniscience hallucination rate shows how often the model answers wrongly when it does not know and should decline. | 34.2% | Max effort | Artificial Analysis | Oct 28, 2026 |
| Omniscience accuracyShare of AA-Omniscience knowledge questions answered correctly. | 34.7% | Max effort | Artificial Analysis | Oct 28, 2026 |
| Omniscience indexAA-Omniscience index: correct answers minus wrong answers, declines neutral, -100 to 100. | 10.7 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| SimpleQA Verified (Epoch)Share of SimpleQA Verified factual questions answered correctly, best scorer, Epoch AI's run. A second factuality source beside AA-Omniscience. | 23.8% | Max effort | Epoch AI | Oct 9, 2026 |
| Knowledge work | ||||
| AA-BriefcaseAA-Briefcase overall Elo for long business deliverables, with AA's 95% interval. | 1,575 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| AnalystAgent (AA run)AA's AnalystAgent score for analyst workflows. | 53.8% | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| APEX-Agents (maintainers)APEX-Agents pass@1 from the maintainers, as Epoch AI republishes it. | 56.1% | Max effort | Mercor (via Epoch AI) | Oct 26, 2026 |
| GDP-PDF (AA run)Share of GDP-PDF document tasks where every check passes. | 20.8% | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| GDPval-AAGDPval-AA Elo: AA's run of economically valuable knowledge-work tasks, graded pairwise, with AA's interval. | 1,619 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| MLCR (AA run)AA's MLCR overall score for long, multi-document reading. | 35% | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Vending-Bench 2Vending-Bench 2 final balance of a simulated vending business run by the model, as Epoch AI republishes it. | $3,602 | Andon Labs (via Epoch AI) | Oct 26, 2026 | |
| Capability beyond the chart | ||||
| AA-LCRAA long-context reasoning accuracy. | 82.7% | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| ARC-AGI-2ARC-AGI-2 score from the ARC Prize maintainers, as Epoch AI republishes it. | 61.3% | Extra-high effort | ARC Prize Foundation (via Epoch AI) | Oct 26, 2026 |
| Chess puzzles (Epoch)Share of chess puzzles solved, Epoch AI's run. | 55% | Max effort | Epoch AI | Oct 13, 2026 |
| CritPt (AA run)Share of CritPt research-level physics problems solved in AA's run. | 18.9% | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| CursorBenchCursorBench score from Cursor, as Epoch AI republishes it. | 48.4% | Max effort | Cursor (via Epoch AI) | Oct 26, 2026 |
| DTBenchDTBench accuracy, as Epoch AI republishes it. | 95.0% | Max effort | Redwood Research (via Epoch AI) | Oct 26, 2026 |
| Epoch Capabilities IndexEpoch Capabilities Index: Epoch AI's composite across its benchmark hub, with its confidence interval. | 159 | Claude Haiku 5.5 | Epoch AI | Oct 26, 2026 |
| FrontierCode (AA run)AA's FrontierCode score: hard, unsaturated coding tasks. | 41.8% | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| FrontierMath Tier 4 (Epoch)Share of FrontierMath Tier 4 research math problems solved, Epoch AI's run. | 46.3% | Max effort | Epoch AI | Oct 7, 2026 |
| FrontierMath Tiers 1 to 3 (Epoch)Share of FrontierMath Tiers 1 to 3 problems solved, Epoch AI's run. | 75.1% | Max effort | Epoch AI | Oct 7, 2026 |
| FrontierSWEFrontierSWE score, as Epoch AI republishes it. | 43.8% | Max effort | FrontierSWE (via Epoch AI) | Oct 26, 2026 |
| GPQA Diamond (Epoch)GPQA Diamond accuracy, best scorer, Epoch AI's run. | 89.6% | Max effort | Epoch AI | Oct 9, 2026 |
| Humanity's Last Exam (AA run)Share of Humanity's Last Exam questions answered correctly in AA's run. | 44.4% | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Intelligence IndexArtificial Analysis Intelligence Index: AA's composite of its own evaluation runs, 0 to 100 points. | 43.4 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| LMCALMCA score, 0 to 100, as Epoch AI republishes it. Its interval is printed only as a half-width, so it is not drawn. | 51.1 | Max effort | Redwood Research (via Epoch AI) | Oct 26, 2026 |
| Mystery game puzzles (Epoch)Share of mystery game puzzles solved, Epoch AI's run. | 30% | Max effort | Epoch AI | Oct 7, 2026 |
| OTIS Mock AIME (Epoch)Share of OTIS Mock AIME 2024 to 2025 problems solved, Epoch AI's run. | 98.9% | Extra-high effort | Epoch AI | Oct 7, 2026 |
| ProofBenchShare of ProofBench proofs graded correct, from Vals AI as Epoch AI republishes it. | 89% | Max effort | Vals AI (via Epoch AI) | Oct 26, 2026 |
| SciCode (AA run)SciCode subproblem accuracy in AA's run. | 55.0% | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Terminal-Bench 4.0 (AA run)Share of Terminal-Bench 4.0 tasks solved in AA's own harness. | 32.8% | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Terminal-Bench Science (AA run)Share of Terminal-Bench Science tasks solved in AA's harness. | 20% | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| WeirdML v3, best runWeirdML v3 maximum performance across runs, as Epoch AI republishes it. | 24.1% | Extra-high effort | Håvard Tveit Ihle (via Epoch AI) | Oct 26, 2026 |
| Domains | ||||
| EconomicsArtificial Analysis capability score for economics questions, 0 to 100 points. | 51.2 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| EngineeringArtificial Analysis capability score for engineering questions, 0 to 100 points. | 44.9 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Finance and accountingArtificial Analysis capability score for finance and accounting questions, 0 to 100 points. | 43.9 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Healthcare and medicalArtificial Analysis capability score for healthcare and medical questions, 0 to 100 points. | 39.9 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| LegalArtificial Analysis capability score for legal questions, 0 to 100 points. | 42.3 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Strategy and operationsArtificial Analysis capability score for strategy and operations questions, 0 to 100 points. | 40.4 | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
| Harvey Legal Lab (AA run)AA's run of Harvey's legal task lab. | 0.6% | Claude Haiku 5.5 (Max) | Artificial Analysis | Oct 26, 2026 |
Routes, prices and uptime
Where Claude Haiku 5.5 is offered, what each route charges, and how each endpoint is holding up.
Where it is offered
12 of 19 routes GILD tracks offer Claude Haiku 5.5 today, preview included. Read Oct 28, 2026 from each route's own pages.
| Route | Status | Model ID | Limits and lifecycle | Sources |
|---|---|---|---|---|
| Amazon Bedrock | Available | anthropic.claude-haiku-5-5 | us./eu./au./jp./global. profiles; Active, EOL not sooner than Oct 7, 2027; tiers: Standard only (Batch, Priority, Flex, Reserved not supported); context / output 1M / 128K | Amazon Web Services, Models at a glanceAmazon Web Services, Claude Haiku 5.5 |
| Claude Code | Available | claude-haiku-5-5 | By full model name on the Anthropic API, or provider ARN, version or deployment name; aliases resolve per provider | Anthropic |
| Claude on Google Cloud (Anthropic managed) | Available | claude-haiku-5-5 | Claude API model IDs (dated IDs use a hyphen) | Anthropic, Claude Haiku 5.5Anthropic, Claude Models on Google Cloud (Anthropic managed) |
| Claude Platform on AWS | Available | claude-haiku-5-5 | Same ID as the Claude API | Anthropic |
| Cursor | Available | not statedpicker label Claude Haiku 5.5 | available (over 100k input tokens bills 5x); Premium plan or higher, API list price | Anysphere (Cursor)Cursor, Cursor Docs — Agent, Rules, MCP, Skills & CLICursor, Models & Pricing |
| Databricks | Available | databricks-claude-haiku-5-5 | available | Databricks, Databricks-hosted inferenceDatabricks, Detailed list of models supported by Databricks Foundation Model APIs |
| The maker's own API | Available | claude-haiku-5-5 | context / max output 1M / 128K; prompts over 100K tokens pay 5x; status per deprecations table | Anthropic, Claude Haiku 5.5Anthropic, Model deprecations |
| GitHub Copilot | Available | not statedpicker label Claude Haiku 5.5 | available (GA) | GitHub |
| GitHub Models | Retired | not applicableservice retired | service retired July 30, 2026 | GitHub, GitHub Models |
| Google Cloud Vertex / Agent Platform (Google managed) | Available | claude-haiku-5-5 (GA) | retirement not sooner than Oct 7, 2027; input / output 1,000,000 / 128,000 | Google, Anthropic's Claude on Google Cloud modelsGoogle, Anthropic's Claude Haiku 5.5 on Google CloudAnthropic |
| Microsoft Foundry | Available | claude-haiku-5-5 | deployment name defaults to model ID; hosted on Azure and Anthropic; context 1M; no Message Batches API | AnthropicMicrosoft Learn |
| OpenCode | Available | claude-haiku-5-5 | OpenCode Zen gateway, Anthropic Messages endpoint | Anomaly (OpenCode), ModelsOpenCode (Anomaly) |
| OpenRouter | Available | anthropic/claude-haiku-5.5 | context / max completion 1,000,000 / 128,000 | OpenRouter |
Not offered on 1 route: Antigravity.
5 routes do not say: Codex, Gemini CLI, Grok Build, Snowflake Cortex and Windsurf / Devin.
What each of these 6 routes' pages say, with sources
| Route | Status | Model ID | Limits and lifecycle | Sources |
|---|---|---|---|---|
| Antigravity | Not offered | not listed; Gemini CLI serves Gemini models only | ||
| Codex | Not stated | Codex model docs list OpenAI models only | ||
| Gemini CLI | Not stated | Region, tier and limits not stated on the route's pages. | ||
| Grok Build | Not stated | Grok Build pages name only grok-4.7 | xAI (SpaceXSI) | |
| Snowflake Cortex | Not stated | Region, tier and limits not stated on the route's pages. | ||
| Windsurf / Devin | Not stated | not stated (absent from Devin model data) | Cognition (Devin, formerly Windsurf), AI Models |
Prices by route
The lowest standard rate is Claude API, at $0.1 / $0.5 per million input and output tokens (prompts up to 100K tokens). US dollars per million tokens, as each route lists them, one row per route and rate plan, cheapest standard rate first after the maker's own; a blended rate states its mix. OpenRouter's endpoints follow under one heading, each with its monitor's uptime over 1 day and 30 minutes and the context and output it serves.
| Route | Standard, in / out | Cache read | Other rates | Source, or uptime (1 day / 30 min) and limits |
|---|---|---|---|---|
| Claude APIMakerLowestprompts up to 100K tokens | $0.1 / $0.5 | Long context $0.5 / $2.50, prompts over 100K tokens | Anthropic, Oct 26, 2026 | |
| Artificial Analysis price record | $0.1 / $0.5 | $0.01 | Blended $0.2, 3 input : 1 output, no cacheBlended $0.077, 7 cache-hit : 2 input : 1 outputCache write $0.13 | Artificial Analysis, Oct 26, 2026 |
| OpenRouterprompts under 100K tokens | $0.1 / $0.5 | $0.01 | Cache read $0.05, prompts of 100K tokens or moreCache write $1, 1-hour cache, prompts of 100K tokens or moreCache write $0.2, 1-hour cache, prompts under 100K tokensCache write $0.63, 5-minute cache, prompts of 100K tokens or moreCache write $0.13, 5-minute cache, prompts under 100K tokensLong context $0.5 / $2.50, prompts of 100K tokens or more | OpenRouter, Oct 28, 2026 |
| Through OpenRouter, 10 provider endpoints. OpenRouter, Oct 28, 2026 | ||||
| Amazon Bedrockprompts under 100K tokens | $0.1 / $0.5 | $0.01 | Cache read $0.05, prompts of 100K tokens or moreCache write $1, 1-hour cache, prompts of 100K tokens or moreCache write $0.2, 1-hour cache, prompts under 100K tokensCache write $0.63, 5-minute cache, prompts of 100K tokens or moreCache write $0.13, 5-minute cache, prompts under 100K tokensLong context $0.5 / $2.50, prompts of 100K tokens or more | Up 99.7% / 100%1M context, 128K output |
| Anthropicprompts under 100K tokens | $0.1 / $0.5 | $0.01 | Cache read $0.05, prompts of 100K tokens or moreCache write $1, 1-hour cache, prompts of 100K tokens or moreCache write $0.2, 1-hour cache, prompts under 100K tokensCache write $0.63, 5-minute cache, prompts of 100K tokens or moreCache write $0.13, 5-minute cache, prompts under 100K tokensLong context $0.5 / $2.50, prompts of 100K tokens or more | Up 100% / 100%1M context, 128K output |
| Azure (global)prompts under 100K tokens | $0.1 / $0.5 | $0.01 | Cache read $0.05, prompts of 100K tokens or moreCache write $1, 1-hour cache, prompts of 100K tokens or moreCache write $0.2, 1-hour cache, prompts under 100K tokensCache write $0.63, 5-minute cache, prompts of 100K tokens or moreCache write $0.13, 5-minute cache, prompts under 100K tokensLong context $0.5 / $2.50, prompts of 100K tokens or more | Up 100% / 100%1M context, 128K output |
| Claude Platform on AWSprompts under 100K tokens | $0.1 / $0.5 | $0.01 | Cache read $0.05, prompts of 100K tokens or moreCache write $1, 1-hour cache, prompts of 100K tokens or moreCache write $0.2, 1-hour cache, prompts under 100K tokensCache write $0.63, 5-minute cache, prompts of 100K tokens or moreCache write $0.13, 5-minute cache, prompts under 100K tokensLong context $0.5 / $2.50, prompts of 100K tokens or more | Up 100% / 100%1M context, 128K output |
| Google (global)prompts under 100K tokens | $0.1 / $0.5 | $0.01 | Cache read $0.05, prompts of 100K tokens or moreCache write $1, 1-hour cache, prompts of 100K tokens or moreCache write $0.2, 1-hour cache, prompts under 100K tokensCache write $0.63, 5-minute cache, prompts of 100K tokens or moreCache write $0.13, 5-minute cache, prompts under 100K tokensLong context $0.5 / $2.50, prompts of 100K tokens or more | Up 100% / 100%1M context, 128K output |
| Amazon Bedrock (eu-west-1)prompts under 100K tokens | $0.11 / $0.55 | $0.011 | Cache read $0.055, prompts of 100K tokens or moreCache write $1.10, 1-hour cache, prompts of 100K tokens or moreCache write $0.22, 1-hour cache, prompts under 100K tokensCache write $0.69, 5-minute cache, prompts of 100K tokens or moreCache write $0.14, 5-minute cache, prompts under 100K tokensLong context $0.55 / $2.75, prompts of 100K tokens or more | Up 100% / 100%1M context, 128K output |
| Amazon Bedrock (us-east-1)prompts under 100K tokens | $0.11 / $0.55 | $0.011 | Cache read $0.055, prompts of 100K tokens or moreCache write $1.10, 1-hour cache, prompts of 100K tokens or moreCache write $0.22, 1-hour cache, prompts under 100K tokensCache write $0.69, 5-minute cache, prompts of 100K tokens or moreCache write $0.14, 5-minute cache, prompts under 100K tokensLong context $0.55 / $2.75, prompts of 100K tokens or more | Up 99.8% / 100%1M context, 128K output |
| Azure (us)prompts under 100K tokens | $0.11 / $0.55 | $0.011 | Cache read $0.055, prompts of 100K tokens or moreCache write $1.10, 1-hour cache, prompts of 100K tokens or moreCache write $0.22, 1-hour cache, prompts under 100K tokensCache write $0.69, 5-minute cache, prompts of 100K tokens or moreCache write $0.14, 5-minute cache, prompts under 100K tokensLong context $0.55 / $2.75, prompts of 100K tokens or more | Up 100%1M context, 128K output |
| Google (europe)prompts under 100K tokens | $0.11 / $0.55 | $0.011 | Cache read $0.055, prompts of 100K tokens or moreCache write $1.10, 1-hour cache, prompts of 100K tokens or moreCache write $0.22, 1-hour cache, prompts under 100K tokensCache write $0.69, 5-minute cache, prompts of 100K tokens or moreCache write $0.14, 5-minute cache, prompts under 100K tokensLong context $0.55 / $2.75, prompts of 100K tokens or more | Up 100% / 100%1M context, 128K output |
| Google (us)prompts under 100K tokens | $0.11 / $0.55 | $0.011 | Cache read $0.055, prompts of 100K tokens or moreCache write $1.10, 1-hour cache, prompts of 100K tokens or moreCache write $0.22, 1-hour cache, prompts under 100K tokensCache write $0.69, 5-minute cache, prompts of 100K tokens or moreCache write $0.14, 5-minute cache, prompts under 100K tokensLong context $0.55 / $2.75, prompts of 100K tokens or more | Up 100% / 100%1M context, 128K output |
Uptime and speed by endpoint
Each provider endpoint as its monitor reports it: uptime over the last day and the last 30 minutes, output speed and time to first token, and the limits it serves. A blank is a figure the monitor does not publish for that endpoint.
| Endpoint | Uptime, 1 day | Uptime, 30 min | Output speed | First token | Context / max output | Source |
|---|---|---|---|---|---|---|
| Artificial Analysis: Anthropic | 241 tokens/s | 311 s | Artificial Analysis, Oct 28, 2026 | |||
| Status page: Claude API (api.anthropic.com) | 99.7%, Calendar month: August 2026; component: Claude API (api.anthropic.com) | Anthropic, Oct 28, 2026 | ||||
| Status page: Claude API (api.anthropic.com) | 99.7%, Calendar month: September 2026; component: Claude API (api.anthropic.com) | Anthropic, Oct 28, 2026 | ||||
| Status page: Claude API (api.anthropic.com) | 99.8%, Calendar month: October 2026; component: Claude API (api.anthropic.com) | Anthropic, Oct 28, 2026 |
Lineup
Anthropic's 14 releases in 4 families, oldest to newest along each tier, with list price and speed. Claude Haiku 5.5 is lit.
CurrentLegacyThis model
Price is the list price per 1M tokens, input / output. Speed is median output tokens per second. Bars share one scale across the lineup. Read between Oct 26, 2026 and Oct 28, 2026.
Claude Haiku
Anthropic's small, fast Claude model family for high-volume, latency-sensitive work.
2 releases
- 4.5Legacy. In 4 products we coverPrice$1 / $5Speed97 tok/s
- This page5.5Current. In 6 products we coverPrice$0.10 / $0.50Speed240.7 tok/s
Claude Sonnet
Anthropic's balanced Claude model family.
4 releases
Claude Opus
Anthropic's high-capability Claude model family.
6 releases
- 4.5Legacy. In 4 products we coverPrice$5 / $25Speed58.8 tok/s
- 4.6Legacy. In 4 products we coverPrice$5 / $25Speed47.7 tok/s
- 4.7Legacy. In 4 products we coverPrice$5 / $25Speed54.7 tok/s
- 4.8Legacy. In 4 products we coverPrice$5 / $25Speed65.9 tok/s
- 5Legacy. In 4 products we coverPrice$5 / $25Speed53.3 tok/s
- 5.5Current. In 9 products we coverPrice$4 / $20Speed98.4 tok/s
Claude Fable
Anthropic's Claude family for demanding reasoning and long-horizon agentic work.
2 releases
Where it fits
The layers of Agentic Software Development that Claude Haiku 5.5 can power. A product supplies the rest.
Product & Engineering, Software Development
Agentic Software Development
- Primary role
- Foundation Model / Reasoning Engine
Claude Haiku 5.5 is positioned by its provider for focused, high-volume tasks. In an agentic software-development product it suits steps such as routing, classification, and extraction. The Model does not itself provide the surrounding interface, execution environment, or system of record.
- Supporting
Work planning & task decomposition
The Model can assist with work planning & task decomposition when a Product supplies the necessary context and tools.
- Supporting
Codebase context & intelligence
The Model can assist with codebase context & intelligence when a Product supplies the necessary context and tools.
- Supporting
Coding & implementation
The Model can assist with coding & implementation when a Product supplies the necessary context and tools.
- Supporting
Debugging & investigation
The Model can assist with debugging & investigation when a Product supplies the necessary context and tools.
- Supporting
Testing & verification
The Model can assist with testing & verification when a Product supplies the necessary context and tools.
- Supporting
Code review & quality
The Model can assist with code review & quality when a Product supplies the necessary context and tools.
Products
We cover 6 products that offer Claude Haiku 5.5 today: 5 subscriptions and 1 API.
Through a subscription
- Kiro+10 MixedAWS's spec-driven agent for the IDE, terminal, and cloud.
- OpenCode+27 PositiveOpen-source, MIT-licensed coding agent that edits code and runs commands from a terminal UI, desktop app, web client, or editor extension.
- Claude Code+75 Strong positiveAnthropic's agentic coding product for terminal and IDE workflows.
- Cursor+11 MixedAI coding agent and code editor for writing, reviewing, and shipping software, with agents in the editor, the terminal, and the cloud.
- GitHub Copilot+15 MixedGitHub's AI coding agents, IDE assistance, and pull request review.
Through an API
Limits each vendor states
- KiroPro, Pro+, Pro Max, Power
- Credit cost 0.06x* relative to Auto (1.0x baseline); 1M context; regions US, EU; requests over 100K tokens billed at 0.3x
- OpenCodeGo, Go Plus
- Estimated requests per 5 hours: Go 3,850, Go Plus 15,380; monthly usage: Go $15, Go Plus $60
- Claude CodePro, Max, Ultra, Team, Enterprise
- Through the haiku alias, which resolves to Haiku 5.5 on the Anthropic API; these plans default to Opus 5.5.
- CursorPremium, Super, Ultra, Teams, Enterprise
- Not part of the included pool: on Premium and above, billed at the model's API list price from the first request, paid from prepaid credit (input $0.1, cache write $0.125, cache read $0.01, output $0.5 per million tokens); US data residency: Yes. Plus and Free do not include third-party models.
- GitHub CopilotCopilot Pro, Copilot Pro+, Copilot Max, Copilot Business, Copilot Enterprise
- Included on the Copilot plans listed. Copilot Free and Copilot Student get auto model selection only, so they cannot pick it.
Plan access as each vendor's page states it: Amazon (Oct 28, 2026), Anomaly (Oct 28, 2026), Anthropic (Nov 2, 2026), Anysphere (Oct 28, 2026), GitHub (Oct 31, 2026).
Comparisons
GILD comparisons that name Claude Haiku 5.5, then those of the clouds and APIs that serve it.
- Gemini API vs Amazon Bedrock: One Model Family or a Model Marketplace?Names Claude Haiku 5.5
- Amazon Bedrock vs Claude API: Buying Claude Through AWS or From AnthropicCompares routes that serve Claude Haiku 5.5
- Amazon Bedrock vs Microsoft Foundry: Which Cloud AI Platform Fits Your Stack?Compares routes that serve Claude Haiku 5.5
- Claude API vs Gemini API: Which Model Platform Fits Your Workloads?Compares routes that serve Claude Haiku 5.5
- Claude API vs OpenAI API: Top Scores or Lower Cost per Task?Compares routes that serve Claude Haiku 5.5
- DeepSeek API vs Claude API: Price per Task or Controls and Residency?Compares routes that serve Claude Haiku 5.5
- DeepSeek API vs OpenRouter: Buy DeepSeek From DeepSeek, or From Another Host?Compares routes that serve Claude Haiku 5.5
- Gemini API vs Gemini Enterprise Agent Platform: Which Route to Gemini?Compares routes that serve Claude Haiku 5.5
- Gemini Enterprise Agent Platform vs Microsoft Foundry: Google Cloud or Azure for Models and AgentsCompares routes that serve Claude Haiku 5.5
- LiteLLM vs OpenRouter: Run Your Own Gateway or Rent One?Compares routes that serve Claude Haiku 5.5
- OpenRouter vs Portkey: Rent Model Access or Run a Governed Gateway?Compares routes that serve Claude Haiku 5.5
- Together AI vs OpenRouter: One Open-Model Host or a Router Across HostsCompares routes that serve Claude Haiku 5.5
What practitioners report
Strongest on Speed, weakest on Reliability.
Each figure is the share of accounts naming Claude Haiku 5.5 that speak well of it on that axis. An axis is scored once praise and concerns are both on record from at least five accounts by three authors. An account from the maker, a company that sells or serves the model, or the maker's expert program is listed and counts toward no figure.
| Axis | Favorable | Accounts |
|---|---|---|
| Quality and reasoningWhether its answers and code are right on the author's own task: reasoning, accuracy, instruction following, and consistency. | 73% | 19 favorable, 7 not, 26 authors |
| Tool use and structured outputWhether it calls tools correctly, returns valid structured output, and keeps agent loops on track. | 67% | 6 favorable, 3 not, 9 authors |
| Long contextHow it holds up over long documents, large codebases, long sessions, and many images or PDFs. | 29% | 2 favorable, 5 not, 7 authors |
| Developer experienceHow it is to build with: prompting, steering, effort settings, and support across SDKs and providers. | 25% | 2 favorable, 6 not, 8 authors |
| CostWhat the work costs: price per task, token use, and what caching and batch save. | 69% | 20 favorable, 9 not, 29 authors |
| SpeedResponse time, time to first token, and throughput. | 74% | 14 favorable, 5 not, 19 authors |
| ReliabilityWhether requests keep succeeding: overloaded errors, outages, rate limits, and changes that broke what worked. | 20% | 1 favorable, 4 not, 5 authors |
Every model on these seven axes
| Axis | Claude Haiku 5.5 | Claude Sonnet 5.5 | Claude Opus 5.5 | Claude Fable 5.1 | Claude Opus 5 | Claude Sonnet 5 | Claude Fable 5 | Claude Opus 4.8 | Claude Opus 4.7 | Claude Sonnet 4.6 | Claude Opus 4.6 | Claude Opus 4.5 | Claude Haiku 4.5 | Claude Sonnet 4.5 | GPT-6.1 Terra | GPT-6.1 Sol | GPT-6 Luna | GPT-6 Astra | GPT-5.6 Sol | GPT-5.6 Terra | GPT-5.6 Luna | GPT-5.5 | GPT-5.4 | gpt-oss-120b | GPT-6 Sol | GPT-5.3-Codex | Gemini 4 Argon | Gemini 3.8 Flash | Gemma 4 31B | Gemini 3.1 Pro Preview | Gemini 3.6 Flash | DeepSeek V4.1 Flash | DeepSeek V4 Pro | Kimi K3.1 Pro | Kimi K3 | Kimi K2.7 Code | Mistral Large 4 | Mistral Medium 3.5 | Grok 4.7 | Grok 4.6 | Grok 4.5 | MiniMax M3.1 Flash | Mercury 3 | GLM-5.3-Flash | GLM-5.3 | GLM-5.2 | Muse Spark Open | Muse Wonder | Muse Spark 1.3 | Llama 4 Scout | Llama 4 Maverick | Qwen3.8 27B | Qwen3.8 Max | MiMo-V2.6-Pro | MiMo-V2.6-Flash | Ling 3.1 Flash | Step 5 Preview |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Quality and reasoning | 73%19 favorable, 7 not, 26 authors | 64%14 favorable, 8 not, 22 authors | 68%17 favorable, 8 not, 25 authors | 54%15 favorable, 13 not, 28 authors | 50%6 favorable, 6 not, 12 authors | 56%9 favorable, 7 not, 16 authors | 68%13 favorable, 6 not, 18 authors | 68%17 favorable, 8 not, 25 authors | 57%12 favorable, 9 not, 21 authors | 67%12 favorable, 6 not, 18 authors | 70%19 favorable, 8 not, 27 authors | 62%13 favorable, 8 not, 20 authors | 58%14 favorable, 10 not, 24 authors | 69%11 favorable, 5 not, 16 authors | 33%3 favorable, 6 not, 8 authors | 55%12 favorable, 10 not, 22 authors | 55%16 favorable, 13 not, 29 authors | 44%12 favorable, 15 not, 26 authors | 33%3 favorable, 6 not, 9 authors | 38%3 favorable, 5 not, 8 authors | Needs 5 accounts from 3 authors3 favorable, 1 not, 4 authors | 33%4 favorable, 8 not, 12 authors | 33%2 favorable, 4 not, 6 authors | 58%22 favorable, 16 not, 38 authors | 31%10 favorable, 22 not, 32 authors | Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors | 63%22 favorable, 13 not, 35 authors | 56%27 favorable, 21 not, 45 authors | 80%24 favorable, 6 not, 28 authors | 56%20 favorable, 16 not, 36 authors | Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors | 61%25 favorable, 16 not, 40 authors | Only concerns on record0 favorable, 4 not, 4 authors | 59%20 favorable, 14 not, 32 authors | Only praise on record3 favorable, 0 not, 3 authors | 50%3 favorable, 3 not, 6 authors | 58%11 favorable, 8 not, 18 authors | 36%5 favorable, 9 not, 14 authors | 43%16 favorable, 21 not, 36 authors | 86%6 favorable, 1 not, 7 authors | 60%3 favorable, 2 not, 5 authors | 62%13 favorable, 8 not, 21 authors | 86%18 favorable, 3 not, 21 authors | 67%14 favorable, 7 not, 21 authors | 76%47 favorable, 15 not, 61 authors | 71%5 favorable, 2 not, 7 authors | Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors | 20%1 favorable, 4 not, 5 authors | Only praise on record2 favorable, 0 not, 2 authors | 53%8 favorable, 7 not, 15 authors | 44%8 favorable, 10 not, 17 authors | 71%24 favorable, 10 not, 34 authors | 57%4 favorable, 3 not, 7 authors | 22%2 favorable, 7 not, 9 authors | 40%4 favorable, 6 not, 10 authors | Only concerns on record0 favorable, 1 not, 1 author | Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors |
| Tool use and structured output | 67%6 favorable, 3 not, 9 authors | 40%4 favorable, 6 not, 10 authors | 46%6 favorable, 7 not, 13 authors | 30%3 favorable, 7 not, 10 authors | Needs 5 accounts from 3 authors3 favorable, 1 not, 4 authors | 70%7 favorable, 3 not, 10 authors | 57%4 favorable, 3 not, 7 authors | 50%4 favorable, 4 not, 8 authors | 50%4 favorable, 4 not, 8 authors | 29%2 favorable, 5 not, 7 authors | 33%4 favorable, 8 not, 12 authors | 70%7 favorable, 3 not, 10 authors | 43%3 favorable, 4 not, 6 authors | 50%3 favorable, 3 not, 6 authors | 40%2 favorable, 3 not, 5 authors | 40%4 favorable, 6 not, 10 authors | 33%3 favorable, 6 not, 9 authors | 62%8 favorable, 5 not, 13 authors | No accounts yet | Only concerns on record0 favorable, 2 not, 2 authors | Only praise on record1 favorable, 0 not, 1 author | Only praise on record1 favorable, 0 not, 1 author | No accounts yet | 27%4 favorable, 11 not, 15 authors | 56%5 favorable, 4 not, 9 authors | No accounts yet | 25%2 favorable, 6 not, 8 authors | 47%7 favorable, 8 not, 15 authors | 67%4 favorable, 2 not, 6 authors | 54%7 favorable, 6 not, 12 authors | Only concerns on record0 favorable, 1 not, 1 author | 59%10 favorable, 7 not, 17 authors | Only concerns on record0 favorable, 1 not, 1 author | Needs 5 accounts from 3 authors3 favorable, 1 not, 4 authors | Only concerns on record0 favorable, 1 not, 1 author | No accounts yet | 38%5 favorable, 8 not, 13 authors | Only concerns on record0 favorable, 5 not, 5 authors | 38%6 favorable, 10 not, 16 authors | Only praise on record2 favorable, 0 not, 2 authors | Only concerns on record0 favorable, 1 not, 1 author | 67%4 favorable, 2 not, 6 authors | 80%8 favorable, 2 not, 10 authors | 33%2 favorable, 4 not, 6 authors | 57%4 favorable, 3 not, 7 authors | No accounts yet | No accounts yet | Only concerns on record0 favorable, 1 not, 1 author | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | Needs 5 accounts from 3 authors1 favorable, 2 not, 3 authors | Only concerns on record0 favorable, 4 not, 4 authors | 46%6 favorable, 7 not, 13 authors | Only praise on record1 favorable, 0 not, 1 author | Only concerns on record0 favorable, 5 not, 5 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author |
| Long context | 29%2 favorable, 5 not, 7 authors | 60%3 favorable, 2 not, 5 authors | 80%8 favorable, 2 not, 10 authors | 63%5 favorable, 3 not, 8 authors | 67%4 favorable, 2 not, 6 authors | 50%3 favorable, 3 not, 6 authors | 33%2 favorable, 4 not, 6 authors | 60%6 favorable, 4 not, 10 authors | 50%4 favorable, 4 not, 7 authors | 71%5 favorable, 2 not, 7 authors | 64%7 favorable, 4 not, 11 authors | 40%4 favorable, 6 not, 10 authors | 40%2 favorable, 3 not, 5 authors | 50%4 favorable, 4 not, 8 authors | Only concerns on record0 favorable, 1 not, 1 author | 38%3 favorable, 5 not, 8 authors | 20%1 favorable, 4 not, 5 authors | 27%3 favorable, 8 not, 11 authors | No accounts yet | No accounts yet | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors | 25%2 favorable, 6 not, 8 authors | Only concerns on record0 favorable, 3 not, 3 authors | No accounts yet | 25%2 favorable, 6 not, 8 authors | 38%5 favorable, 8 not, 13 authors | 20%1 favorable, 4 not, 5 authors | 44%7 favorable, 9 not, 16 authors | Only praise on record1 favorable, 0 not, 1 author | 58%7 favorable, 5 not, 12 authors | No accounts yet | Needs 5 accounts from 3 authors3 favorable, 1 not, 4 authors | Only praise on record1 favorable, 0 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors | No accounts yet | 42%5 favorable, 7 not, 12 authors | No accounts yet | No accounts yet | 40%2 favorable, 3 not, 5 authors | 60%3 favorable, 2 not, 5 authors | No accounts yet | 40%2 favorable, 3 not, 5 authors | No accounts yet | No accounts yet | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 3 not, 3 authors | Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors | 75%6 favorable, 2 not, 8 authors | Only praise on record1 favorable, 0 not, 1 author | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | No accounts yet | No accounts yet |
| Developer experience | 25%2 favorable, 6 not, 8 authors | 38%5 favorable, 8 not, 13 authors | 38%6 favorable, 10 not, 16 authors | 50%7 favorable, 7 not, 14 authors | 25%3 favorable, 9 not, 12 authors | 29%2 favorable, 5 not, 7 authors | 25%3 favorable, 9 not, 12 authors | 53%10 favorable, 9 not, 19 authors | 29%4 favorable, 10 not, 14 authors | 57%4 favorable, 3 not, 7 authors | 45%5 favorable, 6 not, 11 authors | 67%8 favorable, 4 not, 12 authors | Needs 5 accounts from 3 authors2 favorable, 2 not, 4 authors | 50%3 favorable, 3 not, 6 authors | Only concerns on record0 favorable, 2 not, 2 authors | 27%3 favorable, 8 not, 11 authors | 25%2 favorable, 6 not, 8 authors | 41%7 favorable, 10 not, 17 authors | No accounts yet | Only concerns on record0 favorable, 1 not, 1 author | No accounts yet | No accounts yet | No accounts yet | 14%1 favorable, 6 not, 7 authors | 25%3 favorable, 9 not, 12 authors | No accounts yet | 33%3 favorable, 6 not, 9 authors | 39%7 favorable, 11 not, 17 authors | Only concerns on record0 favorable, 7 not, 7 authors | 40%8 favorable, 12 not, 20 authors | No accounts yet | 35%8 favorable, 15 not, 23 authors | No accounts yet | 9%1 favorable, 10 not, 11 authors | No accounts yet | Only concerns on record0 favorable, 1 not, 1 author | 13%2 favorable, 14 not, 16 authors | Only concerns on record0 favorable, 9 not, 9 authors | 25%5 favorable, 15 not, 20 authors | No accounts yet | No accounts yet | Only concerns on record0 favorable, 7 not, 7 authors | 11%1 favorable, 8 not, 9 authors | Only concerns on record0 favorable, 3 not, 3 authors | 33%5 favorable, 10 not, 15 authors | No accounts yet | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | No accounts yet | Only concerns on record0 favorable, 6 not, 6 authors | Only concerns on record0 favorable, 1 not, 1 author | 17%1 favorable, 5 not, 6 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | No accounts yet | No accounts yet |
| Cost | 69%20 favorable, 9 not, 29 authors | 45%10 favorable, 12 not, 22 authors | 56%15 favorable, 12 not, 27 authors | 20%4 favorable, 16 not, 20 authors | 43%3 favorable, 4 not, 7 authors | 39%7 favorable, 11 not, 17 authors | 8%1 favorable, 11 not, 12 authors | 40%6 favorable, 9 not, 14 authors | 50%7 favorable, 7 not, 13 authors | 50%6 favorable, 6 not, 12 authors | 33%7 favorable, 14 not, 20 authors | 53%8 favorable, 7 not, 15 authors | 29%4 favorable, 10 not, 12 authors | 38%3 favorable, 5 not, 8 authors | 80%8 favorable, 2 not, 9 authors | 74%14 favorable, 5 not, 19 authors | 79%22 favorable, 6 not, 26 authors | 29%7 favorable, 17 not, 23 authors | 57%4 favorable, 3 not, 7 authors | Only concerns on record0 favorable, 1 not, 1 author | 50%3 favorable, 3 not, 6 authors | Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors | 63%5 favorable, 3 not, 8 authors | 60%3 favorable, 2 not, 5 authors | 55%12 favorable, 10 not, 21 authors | Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors | 35%7 favorable, 13 not, 20 authors | 58%14 favorable, 10 not, 23 authors | Only praise on record7 favorable, 0 not, 7 authors | 50%8 favorable, 8 not, 16 authors | Only concerns on record0 favorable, 1 not, 1 author | 66%25 favorable, 13 not, 37 authors | Only praise on record2 favorable, 0 not, 2 authors | 40%10 favorable, 15 not, 25 authors | 71%5 favorable, 2 not, 7 authors | Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors | 27%3 favorable, 8 not, 11 authors | Only concerns on record0 favorable, 8 not, 8 authors | 19%5 favorable, 21 not, 26 authors | 20%1 favorable, 4 not, 5 authors | Only praise on record4 favorable, 0 not, 4 authors | 80%12 favorable, 3 not, 15 authors | 50%6 favorable, 6 not, 12 authors | 76%16 favorable, 5 not, 21 authors | 35%8 favorable, 15 not, 23 authors | 71%5 favorable, 2 not, 7 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 9 not, 9 authors | Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors | Only praise on record1 favorable, 0 not, 1 author | Only praise on record1 favorable, 0 not, 1 author | Needs 5 accounts from 3 authors2 favorable, 2 not, 4 authors | Only concerns on record0 favorable, 2 not, 2 authors | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | Only praise on record3 favorable, 0 not, 3 authors | Only praise on record1 favorable, 0 not, 1 author | Only praise on record1 favorable, 0 not, 1 author |
| Speed | 74%14 favorable, 5 not, 19 authors | 88%14 favorable, 2 not, 16 authors | 46%6 favorable, 7 not, 13 authors | 44%4 favorable, 5 not, 9 authors | 60%3 favorable, 2 not, 5 authors | 44%4 favorable, 5 not, 9 authors | 57%4 favorable, 3 not, 7 authors | 31%4 favorable, 9 not, 13 authors | 63%5 favorable, 3 not, 8 authors | 17%1 favorable, 5 not, 6 authors | 36%4 favorable, 7 not, 11 authors | 73%8 favorable, 3 not, 11 authors | 78%7 favorable, 2 not, 9 authors | 75%6 favorable, 2 not, 8 authors | 83%5 favorable, 1 not, 5 authors | 37%7 favorable, 12 not, 19 authors | 45%5 favorable, 6 not, 11 authors | 41%7 favorable, 10 not, 17 authors | Only praise on record1 favorable, 0 not, 1 author | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | 80%4 favorable, 1 not, 5 authors | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | No accounts yet | 85%23 favorable, 4 not, 27 authors | 57%4 favorable, 3 not, 7 authors | Only praise on record1 favorable, 0 not, 1 author | 45%5 favorable, 6 not, 11 authors | 61%19 favorable, 12 not, 31 authors | 53%10 favorable, 9 not, 17 authors | 35%6 favorable, 11 not, 16 authors | Needs 5 accounts from 3 authors2 favorable, 1 not, 3 authors | 68%21 favorable, 10 not, 30 authors | Only concerns on record0 favorable, 1 not, 1 author | 21%4 favorable, 15 not, 19 authors | Only concerns on record0 favorable, 2 not, 2 authors | No accounts yet | 21%3 favorable, 11 not, 14 authors | Only concerns on record0 favorable, 2 not, 2 authors | 29%5 favorable, 12 not, 17 authors | Needs 5 accounts from 3 authors1 favorable, 2 not, 3 authors | Only praise on record2 favorable, 0 not, 2 authors | 44%4 favorable, 5 not, 9 authors | 65%13 favorable, 7 not, 20 authors | 33%3 favorable, 6 not, 9 authors | 20%3 favorable, 12 not, 15 authors | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 1 not, 1 author | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | Needs 5 accounts from 3 authors3 favorable, 1 not, 4 authors | No accounts yet | 33%7 favorable, 14 not, 21 authors | Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors | Only concerns on record0 favorable, 4 not, 4 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 2 not, 2 authors | Only concerns on record0 favorable, 1 not, 1 author |
| Reliability | 20%1 favorable, 4 not, 5 authors | 18%2 favorable, 9 not, 11 authors | 13%1 favorable, 7 not, 8 authors | 9%1 favorable, 10 not, 11 authors | 13%1 favorable, 7 not, 8 authors | 63%5 favorable, 3 not, 7 authors | 22%2 favorable, 7 not, 9 authors | 17%2 favorable, 10 not, 12 authors | 18%2 favorable, 9 not, 11 authors | 17%1 favorable, 5 not, 6 authors | 20%3 favorable, 12 not, 15 authors | 38%6 favorable, 10 not, 16 authors | 29%2 favorable, 5 not, 7 authors | 40%4 favorable, 6 not, 10 authors | Only concerns on record0 favorable, 1 not, 1 author | 13%1 favorable, 7 not, 8 authors | Only concerns on record0 favorable, 5 not, 5 authors | 14%2 favorable, 12 not, 14 authors | Only concerns on record0 favorable, 4 not, 4 authors | Only concerns on record0 favorable, 1 not, 1 author | No accounts yet | Only concerns on record0 favorable, 2 not, 2 authors | Only concerns on record0 favorable, 4 not, 4 authors | Only concerns on record0 favorable, 4 not, 4 authors | 17%1 favorable, 5 not, 6 authors | Only concerns on record0 favorable, 3 not, 3 authors | 5%1 favorable, 18 not, 19 authors | 15%3 favorable, 17 not, 20 authors | Only concerns on record0 favorable, 5 not, 5 authors | 14%3 favorable, 19 not, 22 authors | Only concerns on record0 favorable, 1 not, 1 author | 42%5 favorable, 7 not, 12 authors | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | 8%1 favorable, 12 not, 13 authors | Only concerns on record0 favorable, 1 not, 1 author | Needs 5 accounts from 3 authors1 favorable, 1 not, 2 authors | 38%3 favorable, 5 not, 8 authors | Only concerns on record0 favorable, 4 not, 4 authors | 22%2 favorable, 7 not, 9 authors | Only concerns on record0 favorable, 1 not, 1 author | No accounts yet | 60%3 favorable, 2 not, 5 authors | 33%2 favorable, 4 not, 5 authors | Only concerns on record0 favorable, 7 not, 7 authors | Only concerns on record0 favorable, 4 not, 4 authors | Only concerns on record0 favorable, 1 not, 1 author | No accounts yet | Only concerns on record0 favorable, 2 not, 2 authors | Only concerns on record0 favorable, 1 not, 1 author | Needs 5 accounts from 3 authors1 favorable, 3 not, 4 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 9 not, 9 authors | Only concerns on record0 favorable, 1 not, 1 author | Only concerns on record0 favorable, 2 not, 2 authors | No accounts yet | No accounts yet | Only concerns on record0 favorable, 1 not, 1 author |
The 62 accounts behind Claude Haiku 5.5's figures
GILD searched Hacker News, GitHub, Reddit, X, Artificial Analysis, OpenRouter, Vals AI and the web for accounts that name Claude Haiku 5.5.
Quality and reasoning 73%
- Favorable
was shocked to find haiku 5.5 can do it
Comment on Hacker News. tlarkworthy, Hacker News commenter, self-described, Oct 24, 2026. Haiku 5.5 on a Rust zstd rewrite benchmark.
- Unfavorable
Both evals and Human pairwise tests for our use case are giving Haiku 4.5 first place in pretty much all tests
Claude Haiku 5.5. HyperL0gi, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 against Haiku 4.5 in the author's team evals.
- Favorable
Overall very good at data analysis - handling all of the straightforward data analytics questions correctly
Claude Haiku 5.5. chriddyp, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 on Plotly's DataAnalyticsBench.
- Favorable
I got a good bicycle frame for everything beyond low
Claude Haiku 5.5. Simon Willison, blogger and creator of the LLM command-line tool, self-described, Oct 7, 2026. Claude Haiku 5.5 via llm-anthropic at low through max thinking effort.
- Unfavorable
Results so far are trash.
Claude Haiku 5.5. d1l, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 tested at work against Haiku 4.5 on latency-sensitive tasks.
- Unfavorable
I was curious if this smaller model was good enough for complex UI. It was not.
Claude Haiku 5.5. jjcm, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 on image-to-HTML tests for complex UI.
- Favorable
Haiku 5.5 is noticeably smarter than GPT-6 Luna
Claude Haiku 5.5. dannyw, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 against GPT-6 Luna.
- Favorable
Twice as expensive as Luna, but also considerably smarter too
Claude Haiku 5.5. XCSme, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 at xhigh on the author's aibenchy tests.
- Favorable
Luna 6 and now Haiku 5.5 are both very competitive if not better on all axes that we care about
Why isn't the industry freaking out about DeepSeek 4.1 Flash?. _jayhack_, Hacker News commenter, self-described, Oct 8, 2026. Haiku 5.5 for non-coding knowledge work at the author's employer.
- Unfavorable
haiku 5.5, sonnet 5.5, and this latest deepseek 1017 as worker agents running claude code... and deepseek won
DeepSeek-v4.1-Flash-1017. johnnyApplePRNG, Hacker News commenter, self-described, Oct 17, 2026. Haiku 5.5 as a Claude Code worker agent against Sonnet 5.5 and DeepSeek 1017.
- Favorable
Haiku 5.5 is equally fast and more intelligent than 2.5 composer
Post on X by gokul_i. Gokul, software engineer and ex-Google GDE for web, self-described, Oct 25, 2026. Haiku 5.5 against Composer 2.5.
- Favorable
Now that Haiku 5.5 is actually a good model, I have a really good agent to help me send emails
Post on X by leodev. Leo, founder of Slashy, self-described, Oct 21, 2026. Haiku 5.5 running the email agent in Slashy.
- Favorable
got P0 outages right in 80/80
feat(triage): run TriageAgent on Claude Haiku 5.5. sidreddy88, GitHub user, self-described, Oct 26, 2026. Claude Haiku 5.5 running TriageAgent on 114 held-out incident cases.
- Favorable
Claude Haiku 5.5 got all 20 analytics questions right.
Claude Haiku 5.5 Benchmark Results for Data Analysis. Plotly, data analytics software company (DataAnalyticsBench), self-described, Oct 7, 2026. Claude Haiku 5.5 in Plotly Studio Embedded on Plotly's 43-question data analytics exam.
- Favorable
Haiku 4.5 just was not good enough to push that idea far. Haiku 5.5 is.
Opus 5.5 Took Fable's Job. Haiku 5.5 Proved Me Wrong.. Pawel Jozefiak, author of the Digital Thoughts Substack who runs an agent on Claude models daily, self-described, Oct 9, 2026. Haiku 5.5 as the subagent model under Opus 5.5 at medium effort.
- Favorable
Claude Haiku 5.5 (Max) is amongst the leading models in intelligence
Claude Haiku 5.5 (max) - Intelligence, Performance & Price Analysis. Artificial Analysis, independent AI model benchmarking, self-described, Oct 26, 2026. Claude Haiku 5.5 (Max) on Anthropic's API.
- Favorable
Leading small-class model performance
Anthropic has released Claude Haiku 5.5. Artificial Analysis, independent AI model benchmarking, self-described, Oct 7, 2026. Claude Haiku 5.5 across effort settings on the Artificial Analysis Intelligence Index.
- Favorable
Haiku 5.5 is a good agentic coding model now!
Property-based testing with Claude. Tom Larkworthy, Staff Engineer, Taktile Engineering, Oct 22, 2026. Haiku 5.5 writing a Rust Zstd compressor and decompressor from the format specification.
- Favorable
Haiku at its default matches Sonnet at its default, for about half the money.
Haiku 5.5, Sonnet 5.5 or Opus 5.5 for coding: what the cheap model is actually worth. Anand Das, Co-founder and CTO of Bito, Oct 9, 2026. Haiku 5.5 at medium and high effort in Claude Code on Bito's agentic coding benchmark.
- Favorable
It actually worked quite well to begin with. Very impressed
Haiku 5.5 is very good; here's why I don't use it. dcidino, Reddit user on r/ClaudeAI, self-described, Oct 22, 2026. Haiku 5.5 on a Pro account for a database project.
- Favorable
Haiku alone passed everything.
Does delegating save Claude quota? I measured 6 setups on 28 real tasks: Haiku alone passed every check for 93% less, handing work to cheaper models cost more. scomage, Reddit user on r/ClaudeAI, self-described, Oct 22, 2026. Haiku 5.5 alone and as a subagent in Claude Code on 28 coding tasks.
- Favorable
Despite running on the cheaper plan, Haiku won on everything: price, speed and output quality.
The cheapest Claude plan running Haiku beat Z.ai's Pro plan running GLM 5.3 Flash. The "generous Z.ai quota" is a myth.. Necessary_Spring_425, Reddit user on r/ZaiGLM, self-described, Oct 22, 2026. Haiku 5.5 Max on a Claude Pro subscription porting the DOS game Digger to HTML5.
- Favorable
Fast and surprisingly intelligent
Samsung TV privacy issues. nekitamo, Hacker News commenter, self-described, Oct 24, 2026. Claude Haiku 5.5 in Claude Code via OpenRouter on a crackme reverse-engineering benchmark.
- Favorable
Blind bake-off: Haiku 9.2 vs Gemini 7.3 (6 scenarios).
llm: switch the DM model to Haiku 5.5 , audit + plan. Garblesnarff, owner of infinite-realms-app, self-described, Oct 14, 2026. anthropic/claude-haiku-5.5 via OpenRouter as the game's DM model in a 6-scenario blind bake-off.
- Unfavorable
GPT 6 Luna crushes it on data labeling and summarization tasks.
Haiku 5.5 is very good; here's why I don't use it. Blackhat165, Reddit user on r/ClaudeAI, self-described, Oct 22, 2026. Haiku 5.5 for coding and API tasks, in a thread on Haiku 5.5.
- Favorable
It takes #3 of 110 on Vibe Code Bench (90.44%), behind only Gemini 4 Argon (91.91%) and Claude Sonnet 5.5 (92.39%)
Claude Haiku 5.5 Benchmarks, Cost and Capabilities | Vals AI. Vals AI, independent model benchmarking, self-described, Oct 8, 2026. Claude Haiku 5.5 at max compute effort across the Vals benchmark suite.
- Unfavorable
Missed suspect files, or reported a path that doesn't exist.
eval(multi-agent): Haiku 5.5 fails as the retrieval-role default against sonnet (low). kyle-sexton, maintainer of melodic-software/claude-code-plugins, self-described, Oct 11, 2026. Haiku 5.5 at low effort via claude -p on 44 extraction and test-parsing items, 264 calls.
- Favorable
Haiku 5.5 had the best median error (1.8%)
Newer and Cheaper isn't always better: 4 cheap LLMs compete on calorie estimation and JSON workout building for my fitness app. RefactorFitness, Reddit user on r/LLMDevs building a fitness app, self-described, Oct 8, 2026. Claude Haiku 5.5 on calorie estimation and JSON workout generation for a fitness app.
- Unfavorable
If I had followed Haiku, I would have deleted half of a correct post
I tested Haiku 5.5, Sonnet 5.5 and Opus 5.5 as subagents on 6 real tasks. emarkosov, Reddit user on r/ClaudeAI, self-described, Oct 9, 2026. Haiku 5.5 at high and xhigh effort as a Claude Code subagent on 6 real tasks.
Tool use and structured output 67%
- Favorable
Haiku 5.5 calls the tool every time
feat(triage): run TriageAgent on Claude Haiku 5.5. sidreddy88, GitHub user, self-described, Oct 26, 2026. Claude Haiku 5.5 running TriageAgent on 114 held-out incident cases.
- Favorable
The model works each one out on its own, exploring the schema, writing queries and checking the results.
Claude Haiku 5.5 Benchmark Results for Data Analysis. Plotly, data analytics software company (DataAnalyticsBench), self-described, Oct 7, 2026. Claude Haiku 5.5 in Plotly Studio Embedded on Plotly's 43-question data analytics exam.
- Favorable
Highly capable at agentic knowledge work
Anthropic has released Claude Haiku 5.5. Artificial Analysis, independent AI model benchmarking, self-described, Oct 7, 2026. Claude Haiku 5.5 across effort settings on the Artificial Analysis Intelligence Index.
- Favorable
returns the typed response on us. Opus 5, Opus 5.5, Sonnet 5, Sonnet 5.5 and Haiku 5.5
fix(aws): restore structured output for Claude 5 on Bedrock. JonZeolla, langchain-aws contributor, self-described, Oct 20, 2026. Haiku 5.5 on Amazon Bedrock with native structured output through Converse.
- Unfavorable
Haiku 5.5 fails more tool calls than GPT-4o
Post on X by AdamHoltererer. Adam Holter, X user, self-described, Oct 21, 2026. Haiku 5.5 tool calls against GPT-4o.
- Favorable
CI is green against the new models.
test(conformance): move fixtures to the latest models. weeco, contributor to redpanda-data/ai-sdk-go, self-described, Oct 21, 2026. Haiku 5.5 on the Anthropic API and Bedrock in the ai-sdk-go conformance suite.
- Favorable
Schema-valid rate: Haiku 6/6 clean; Gemini 3/6 with violations
llm: switch the DM model to Haiku 5.5 , audit + plan. Garblesnarff, owner of infinite-realms-app, self-described, Oct 14, 2026. anthropic/claude-haiku-5.5 via OpenRouter as the game's DM model in a 6-scenario blind bake-off.
- Unfavorable
It can't follow output specifications like JSON or selecting from a list.
Haiku 5.5 is very good; here's why I don't use it. Blackhat165, Reddit user on r/ClaudeAI, self-described, Oct 22, 2026. Haiku 5.5 for coding and API tasks, in a thread on Haiku 5.5.
- Unfavorable
Haiku couldn't open a league's official rulebook PDF, so it used last season's rules instead
I tested Haiku 5.5, Sonnet 5.5 and Opus 5.5 as subagents on 6 real tasks. emarkosov, Reddit user on r/ClaudeAI, self-described, Oct 9, 2026. Haiku 5.5 at high and xhigh effort as a Claude Code subagent on 6 real tasks.
- Favorable
Haiku got all 120 calculation results right at low effort
I benchmarked Haiku 5.5 against models costing 20x more on math problems. Hoddmachine, Reddit user on r/ClaudeAI who builds Tafla, self-described, Oct 9, 2026. Haiku 5.5 at default and low effort in Tafla, writing formulas for a calculation engine, 1,200 turns.
Long context 29%
- Unfavorable
No need for the full 1M context on cheap jobs!!
Post on X by Gabriele Bolognese. Gabriele Bolognese, backend developer and founder of FlashFX, self-described, Oct 12, 2026. Haiku 5.5 on the Claude API with big prompts.
- Unfavorable
So a child whose context will grow past 100K either pays the surcharge or loses context to compaction.
feat(skills): route long-context Claude work to Sonnet 5.5. PedroNavHur, GitHub user, self-described, Oct 26, 2026. Haiku 5.5 as the default subagent model in the author's skills.
- Favorable
Haiku did best on long bug stories with many steps.
Claude Haiku 5.5 vs GPT-6 Luna: 256 jobs in Ask Buoy. the Buoy team, makers of the Buoy debugging tool, self-described, Oct 9, 2026. Claude Haiku 5.5 at default settings in Ask Buoy against GPT-6 Luna, 256 jobs.
- Unfavorable
It just does not have the ability to recall what you've been working on, and you will get asked for the same information over and over again.
Haiku 5.5 is very good; here's why I don't use it. dcidino, Reddit user on r/ClaudeAI, self-described, Oct 22, 2026. Haiku 5.5 on a Pro account for a database project.
- Favorable
Haiku was sitting at 870k context and had used 20%.
The cheapest Claude plan running Haiku beat Z.ai's Pro plan running GLM 5.3 Flash. The "generous Z.ai quota" is a myth.. Necessary_Spring_425, Reddit user on r/ZaiGLM, self-described, Oct 22, 2026. Haiku 5.5 Max on a Claude Pro subscription porting the DOS game Digger to HTML5.
- Unfavorable
the moment i need it to remember anything from two messages ago it's like talking to a wall
Haiku 5.5 is very good; here's why I don't use it. Easy_Air_1584, Reddit user on r/ClaudeAI, self-described, Oct 22, 2026. Haiku 5.5 for quick one-off tasks, in a thread on Haiku 5.5.
- Unfavorable
It's made to be a low-cost LLM tool with pretty good brainpower but not a lot of context
Haiku 5.5 is very good; here's why I don't use it. SocketByte, Reddit user on r/ClaudeAI, self-described, Oct 22, 2026. Haiku 5.5 through the API, in a thread on Haiku 5.5.
Developer experience 25%
- Unfavorable
The new Haiku doesn't let you disable reasoning, and defaults to medium
Claude Haiku 5.5. Simon Willison, blogger and creator of the LLM command-line tool, self-described, Oct 7, 2026. Claude Haiku 5.5 via llm-anthropic at low through max thinking effort.
- Unfavorable
Claude Haiku 5.5 runs adaptive thinking when the field is omitted, so the flag has the opposite effect
[BUG]
--thinking disabledis not sent for claude-haiku-5-5: the request has nothinkingfield, so Haiku 5.5 runs adaptive thinking. JusticeWay, GitHub user, self-described, Oct 9, 2026. claude-haiku-5-5 in Claude Code 2.1.295 and the Agent SDK. - Unfavorable
As soon as a tool such as `Read` is enabled, the model still returns `thinking` blocks on every turn, even with `effort="low"`
thinking {type: "disabled"} is not applied to Claude Haiku 5.5 when tools are enabled (Agent SDK, Bedrock). maxkim-zigbang, GitHub user, self-described, Oct 25, 2026. jp.anthropic.claude-haiku-5-5 on Bedrock through the Claude Agent SDK.
- Unfavorable
every request fails with HTTP 400
[Bug]: Agent fails with 400 on
anthropic/claude-haiku-5.5via Cloudflare. nopogle-pixel, GitHub user, self-described, Oct 14, 2026. anthropic/claude-haiku-5.5 in Cherry Studio 2.16 through Cloudflare. - Unfavorable
The saved model was claude-haiku-5-5 , which thinks by default; thinking counts against max_tokens , so the 800-token brief stopped at max_tokens with zero text.
fix(smart-read): turn off thinking per model so briefs are not empty. floo-one, GitHub user, self-described, Oct 26, 2026. claude-haiku-5-5 as the smart read model in vibeflow.
- Unfavorable
it generated 440M tokens, which is very verbose in comparison to the median of 100M
Claude Haiku 5.5 (max) - Intelligence, Performance & Price Analysis. Artificial Analysis, independent AI model benchmarking, self-described, Oct 26, 2026. Claude Haiku 5.5 (Max) on Anthropic's API.
- Favorable
Sonnet 5.5 and Haiku 5.5 are really good now, and they can be made better with instruction
Property-based testing with Claude. Tom Larkworthy, Staff Engineer, Taktile Engineering, Oct 22, 2026. Haiku 5.5 writing a Rust Zstd compressor and decompressor from the format specification.
- Favorable
Effort matters a lot here and costs almost nothing.
Haiku 5.5, Sonnet 5.5 or Opus 5.5 for coding: what the cheap model is actually worth. Anand Das, Co-founder and CTO of Bito, Oct 9, 2026. Haiku 5.5 at medium and high effort in Claude Code on Bito's agentic coding benchmark.
- Unfavorable
but overly thorough and token-heavy
Samsung TV privacy issues. nekitamo, Hacker News commenter, self-described, Oct 24, 2026. Claude Haiku 5.5 in Claude Code via OpenRouter on a crackme reverse-engineering benchmark.
Cost 69%
- Unfavorable
the 5x price increase beyond 100k is painful
Claude Haiku 5.5. Topfi, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 throughput on OpenRouter at launch.
- Favorable
9x cheaper than Haiku 4.5 and 2 letter grades better
Claude Haiku 5.5. chriddyp, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 on Plotly's DataAnalyticsBench.
- Unfavorable
so there's a hidden price increase there
Claude Haiku 5.5. Simon Willison, blogger and creator of the LLM command-line tool, self-described, Oct 7, 2026. Claude Haiku 5.5 via llm-anthropic at low through max thinking effort.
- Unfavorable
Over that it jumps to $0.50/$2.50!! THAT IS 400% MORE!!!
Post on X by Gabriele Bolognese. Gabriele Bolognese, backend developer and founder of FlashFX, self-described, Oct 12, 2026. Haiku 5.5 on the Claude API with big prompts.
- Favorable
Considering the price, no model comes close to being as good as this.
Claude Haiku 5.5. thefourthchime, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 on the Pac-Man Bench.
- Unfavorable
Luna comes out a fair amount cheaper due to more efficient tokenization and less outputs
Claude Haiku 5.5. Farmadupe, Hacker News commenter, self-described, Oct 8, 2026. Haiku 5.5 against GPT-6 Luna on classification work, batch and synchronous.
- Favorable
You can get the same performance for half the cost from Luna or Haiku 5.5 now
Why isn't the industry freaking out about DeepSeek 4.1 Flash?. criley2, Hacker News commenter, self-described, Oct 9, 2026. Haiku 5.5 against DeepSeek v4.1 Flash on cost.
- Unfavorable
Not only did it come in significantly cheaper even than Haiku
DeepSeek-v4.1-Flash-1017. johnnyApplePRNG, Hacker News commenter, self-described, Oct 17, 2026. Haiku 5.5 as a Claude Code worker agent against Sonnet 5.5 and DeepSeek 1017.
- Favorable
Haiku 5.5 is decent, cheap, and fast.
Ask HN: Best value/money AI coding plan bundle?. A_D_E_P_T, Hacker News commenter, self-described, Oct 20, 2026. Haiku 5.5 for grunt work under Opus 5.5 on a Claude Max 20x plan.
- Favorable
the value is very good for e.g. DeepSeek 4.1 Flash (not sure if they're serving 1017 yet), GLM 5.3 Flash, Qwen3.8 Flash and Claude Haiku 5.5
Kimi K3.1 Pro and K3.1 Pro Highspeed. ZiLxxi, Hacker News commenter, self-described, Oct 24, 2026. Claude Haiku 5.5 through OpenCode Go.
- Unfavorable
Haiku 5.5 becomes 5x more expensive past 100k context
Mercury 3: Diffusion at scale. 2001zhaozhao, Hacker News commenter, self-described, Oct 19, 2026. Haiku 5.5 pricing beyond 100k context against Mercury 3.
- Unfavorable
The cost per task from Artificial Analysis is roughly 3x higher at every reasoning effort level for Haiku than Luna
Claude Haiku 5.5. RussianCow, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 on Artificial Analysis and OpenRouter figures.
- Favorable
Haiku 5.5 is a monster for how cheap it is
Post on X by jonclegg77. Jon Clegg, X user, self-described, Oct 25, 2026. Haiku 5.5 against Opus 5.5 at OpenRouter retail prices.
- Favorable
Shakes out to 25% the cost of raw dogging Opus 5.5
Post on X by socialarb. socialarb, X user, self-described, Oct 25, 2026. Haiku 5.5 with the Opus advisor against Opus 5.5 alone.
- Favorable
The skills stopped routing to Sonnet 5.5 because Opus 5.5 and Haiku 5.5 give more intelligence per dollar.
feat(skills): route long-context Claude work to Sonnet 5.5. PedroNavHur, GitHub user, self-described, Oct 26, 2026. Haiku 5.5 as the default subagent model in the author's skills.
- Favorable
cost per accepted result $0.0006 vs $0.0071 (0.09x)
feat(triage): run TriageAgent on Claude Haiku 5.5. sidreddy88, GitHub user, self-described, Oct 26, 2026. Claude Haiku 5.5 running TriageAgent on 114 held-out incident cases.
- Favorable
most of the volume, written to explicit rules, at ~1/20 of the price
Coach on two models: Sonnet 5.5 for judgement, Haiku 5.5 for short messages. omerdweck, GitHub user, self-described, Oct 26, 2026. claude-haiku-5-5 for daily summaries and short coaching messages.
- Favorable
it's almost 9x cheaper ($3.27 to $0.38 per run)
Claude Haiku 5.5 Benchmark Results for Data Analysis. Plotly, data analytics software company (DataAnalyticsBench), self-described, Oct 7, 2026. Claude Haiku 5.5 in Plotly Studio Embedded on Plotly's 43-question data analytics exam.
- Unfavorable
Luna cost about 29% less.
Claude Haiku 5.5 vs GPT-6 Luna: 256 jobs in Ask Buoy. the Buoy team, makers of the Buoy debugging tool, self-described, Oct 9, 2026. Claude Haiku 5.5 at default settings in Ask Buoy against GPT-6 Luna, 256 jobs.
- Favorable
Keep its context small and it stays absurdly cheap.
Opus 5.5 Took Fable's Job. Haiku 5.5 Proved Me Wrong.. Pawel Jozefiak, author of the Digital Thoughts Substack who runs an agent on Claude models daily, self-described, Oct 9, 2026. Haiku 5.5 as the subagent model under Opus 5.5 at medium effort.
- Favorable
well priced when comparing to other models of similar price
Claude Haiku 5.5 (max) - Intelligence, Performance & Price Analysis. Artificial Analysis, independent AI model benchmarking, self-described, Oct 26, 2026. Claude Haiku 5.5 (Max) on Anthropic's API.
- Unfavorable
Heavy token use compared to GPT-6 Luna: Haiku 5.5 (max) uses ~162k output tokens per Intelligence Index task, ~3x GPT-6 Luna
Anthropic has released Claude Haiku 5.5. Artificial Analysis, independent AI model benchmarking, self-described, Oct 7, 2026. Claude Haiku 5.5 across effort settings on the Artificial Analysis Intelligence Index.
- Favorable
I was blown away that Haiku can now solve it, and at that price: about $1 per run, against $5 to $8 for Opus at medium and high effort.
Property-based testing with Claude. Tom Larkworthy, Staff Engineer, Taktile Engineering, Oct 22, 2026. Haiku 5.5 writing a Rust Zstd compressor and decompressor from the format specification.
- Favorable
For quick one-off asks, Haiku at high effort is the best value we have ever measured on this benchmark
Haiku 5.5, Sonnet 5.5 or Opus 5.5 for coding: what the cheap model is actually worth. Anand Das, Co-founder and CTO of Bito, Oct 9, 2026. Haiku 5.5 at medium and high effort in Claude Code on Bito's agentic coding benchmark.
- Favorable
It was 86-91% cheaper than Sonnet on every batch.
Does delegating save Claude quota? I measured 6 setups on 28 real tasks: Haiku alone passed every check for 93% less, handing work to cheaper models cost more. scomage, Reddit user on r/ClaudeAI, self-described, Oct 22, 2026. Haiku 5.5 alone and as a subagent in Claude Code on 28 coding tasks.
- Unfavorable
Haiku is the "cheap" model and still fit fewer tasks than Sonnet
How many real coding tasks fit in one 5-hour window on the $20 plans? I measured it: Sonnet ~33, Haiku ~20, Codex ~18, Opus ~6. Sufficient-Storage87, Reddit user on r/ClaudeAI, self-described, Oct 24, 2026. Haiku 5.5 at default settings on Claude Pro, an 8-file WeasyPrint feature.
- Favorable
After 3 hours, Haiku had a playable game on 33% of its 5h quota.
The cheapest Claude plan running Haiku beat Z.ai's Pro plan running GLM 5.3 Flash. The "generous Z.ai quota" is a myth.. Necessary_Spring_425, Reddit user on r/ZaiGLM, self-described, Oct 22, 2026. Haiku 5.5 Max on a Claude Pro subscription porting the DOS game Digger to HTML5.
- Favorable
Haiku costs 62% less per turn.
llm: switch the DM model to Haiku 5.5 , audit + plan. Garblesnarff, owner of infinite-realms-app, self-described, Oct 14, 2026. anthropic/claude-haiku-5.5 via OpenRouter as the game's DM model in a 6-scenario blind bake-off.
- Favorable
Prompt caching fixed that: warm calls dropped from ~$0.0046 to ~$0.001.
Newer and Cheaper isn't always better: 4 cheap LLMs compete on calorie estimation and JSON workout building for my fitness app. RefactorFitness, Reddit user on r/LLMDevs building a fitness app, self-described, Oct 8, 2026. Claude Haiku 5.5 on calorie estimation and JSON workout generation for a fitness app.
- Favorable
Same quality, so Haiku wins on price
I tested Haiku 5.5, Sonnet 5.5 and Opus 5.5 as subagents on 6 real tasks. emarkosov, Reddit user on r/ClaudeAI, self-described, Oct 9, 2026. Haiku 5.5 at high and xhigh effort as a Claude Code subagent on 6 real tasks.
- Favorable
The pricier models cost roughly 6–12x Haiku per question.
I benchmarked Haiku 5.5 against models costing 20x more on math problems. Hoddmachine, Reddit user on r/ClaudeAI who builds Tafla, self-described, Oct 9, 2026. Haiku 5.5 at default and low effort in Tafla, writing formulas for a calculation engine, 1,200 turns.
Speed 74%
- Favorable
roughly twice the throughput of Luna and far lower latency (up to 2sec depending on provider) is impressive
Claude Haiku 5.5. Topfi, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 throughput on OpenRouter at launch.
- Favorable
It's also now the fastest model (using the default speeds, not trying any of the other models "Fast" mode) to complete the exam
Claude Haiku 5.5. chriddyp, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 on Plotly's DataAnalyticsBench.
- Unfavorable
Prompt leakage even. And it's slower.
Claude Haiku 5.5. d1l, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 tested at work against Haiku 4.5 on latency-sensitive tasks.
- Favorable
Very fast though, and likely best used for small subagent tasks
Claude Haiku 5.5. jjcm, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 on image-to-HTML tests for complex UI.
- Unfavorable
However, it did take an extremely long time.
Claude Haiku 5.5. thefourthchime, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 on the Pac-Man Bench.
- Favorable
It is also really fast.
Claude Haiku 5.5. XCSme, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 at xhigh on the author's aibenchy tests.
- Favorable
Haiku 5.5 is decent, cheap, and fast.
Ask HN: Best value/money AI coding plan bundle?. A_D_E_P_T, Hacker News commenter, self-described, Oct 20, 2026. Haiku 5.5 for grunt work under Opus 5.5 on a Claude Max 20x plan.
- Favorable
the real reason to use Haiku is that it's faster than all of these models
Claude Haiku 5.5. RussianCow, Hacker News commenter, self-described, Oct 7, 2026. Haiku 5.5 on Artificial Analysis and OpenRouter figures.
- Favorable
I swapped in Haiku 5.5 and latency dropped to 3 seconds.
Post on X by jason4l5k. Gotspidercat (jason4l5k), X user, self-described, Oct 24, 2026. Haiku 5.5 in a Home Assistant voice control setup.
- Favorable
that model is about as capable as Claude Haiku 5.5 but 20 times as slow
Post on X by dlexc. Dominic Chambers, X user, self-described, Oct 24, 2026. Claude Haiku 5.5 against a local Qwen 3.8 Flash Next.
- Favorable
It's also the fastest model on the board, at a median of 14 seconds per question
Claude Haiku 5.5 Benchmark Results for Data Analysis. Plotly, data analytics software company (DataAnalyticsBench), self-described, Oct 7, 2026. Claude Haiku 5.5 in Plotly Studio Embedded on Plotly's 43-question data analytics exam.
- Favorable
1.3 s sooner first answer with Haiku
Claude Haiku 5.5 vs GPT-6 Luna: 256 jobs in Ask Buoy. the Buoy team, makers of the Buoy debugging tool, self-described, Oct 9, 2026. Claude Haiku 5.5 at default settings in Ask Buoy against GPT-6 Luna, 256 jobs.
- Favorable
Haiku 5.5 is cheap, fast, and good
Opus 5.5 Took Fable's Job. Haiku 5.5 Proved Me Wrong.. Pawel Jozefiak, author of the Digital Thoughts Substack who runs an agent on Claude models daily, self-described, Oct 9, 2026. Haiku 5.5 as the subagent model under Opus 5.5 at medium effort.
- Favorable
At 234 tokens per second, Claude Haiku 5.5 (Max) is notably fast
Claude Haiku 5.5 (max) - Intelligence, Performance & Price Analysis. Artificial Analysis, independent AI model benchmarking, self-described, Oct 26, 2026. Claude Haiku 5.5 (Max) on Anthropic's API.
- Unfavorable
Haiku needs more requests than the bigger models to do the same job, so at matched quality it's slower
Haiku 5.5, Sonnet 5.5 or Opus 5.5 for coding: what the cheap model is actually worth. Anand Das, Co-founder and CTO of Bito, Oct 9, 2026. Haiku 5.5 at medium and high effort in Claude Code on Bito's agentic coding benchmark.
- Favorable
~2.8× faster for 5.5 in this small paired run
Add judge-only replay for Haiku 5.5 comparison. AbhitejJohn, collaborator on dotnet/skills, self-described, Oct 12, 2026. Haiku 5.5 as an evaluation judge replayed on 333 saved trials.
- Unfavorable
Haiku 5.5 needs thinking disabled to get close to Luna, at 4.26 s vs 3.13 s p50 for a current-toll turn
Add model routing with outage fallback between Luna 6 and Haiku 5.5. rhprasad0, owner of nova-toll-budget-agent, self-described, Oct 7, 2026. Haiku 5.5 on Bedrock against GPT-6 Luna for TollChat turns.
- Favorable
the speed is nice though, i'll give it that
Haiku 5.5 is very good; here's why I don't use it. Easy_Air_1584, Reddit user on r/ClaudeAI, self-described, Oct 22, 2026. Haiku 5.5 for quick one-off tasks, in a thread on Haiku 5.5.
- Unfavorable
So Haiku at xhigh is half the price of Opus but twice as slow
I tested Haiku 5.5, Sonnet 5.5 and Opus 5.5 as subagents on 6 real tasks. emarkosov, Reddit user on r/ClaudeAI, self-described, Oct 9, 2026. Haiku 5.5 at high and xhigh effort as a Claude Code subagent on 6 real tasks.
- Favorable
Haiku had the lowest median and p90
I benchmarked Haiku 5.5 against models costing 20x more on math problems. Hoddmachine, Reddit user on r/ClaudeAI who builds Tafla, self-described, Oct 9, 2026. Haiku 5.5 at default and low effort in Tafla, writing formulas for a calculation engine, 1,200 turns.
Reliability 20%
- Unfavorable
Affected models: Claude Mythos 5.1, Fable 5.1, Opus 5.5, Sonnet 5.5 and Haiku 5.5.
Post on X by atnomos. Will, design and AI/ML program manager turned indie builder, self-described, Oct 25, 2026. Haiku 5.5 during the Oct 25, 2026 Anthropic outage.
- Unfavorable
Stage 2 selection calls on the adaptive-thinking models (`claude-opus-5-5`, `claude-haiku-5-5`) sometimes **never return**
Stage 2 calls on Opus 5.5 / Haiku 5.5 stall with no response on large selection prompts. christophechang, GitHub user, self-described, Oct 20, 2026. claude-haiku-5-5, non-streaming, in MixLab v1.21.0 and v1.21.1.
- Favorable
Neither judge reported an error.
Add judge-only replay for Haiku 5.5 comparison. AbhitejJohn, collaborator on dotnet/skills, self-described, Oct 12, 2026. Haiku 5.5 as an evaluation judge replayed on 333 saved trials.
- Unfavorable
Count provider errors too. Haiku had 69 in one run, Luna none.
I ran 4 LLMs (plus reasoning levels) on the same 177 task-parsing cases. The cheapest one was the best price and quality. danielbgolan, Reddit user on r/LLMDevs building an iOS to-do app, self-described, Oct 24, 2026. Claude Haiku 5.5 at default, high and xhigh reasoning via OpenRouter on 177 task-parsing cases.
- Unfavorable
Last success 10:59:51, first 403 11:00:30.
/provider/v1/messages: 403 "Authentication failed" for claude-sonnet-5-5 and claude-haiku-5-5 only (auth passes for other models). esp32gallery, CommandCode API user, self-described, Oct 16, 2026. claude-haiku-5-5 through the CommandCode provider Messages endpoint.