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GLM model by Z.ai

GLM-5.3-Flash

Z.ai's first native multimodal model in the GLM-5 series, for efficient coding and long-horizon agent tasks, with a 1M-token context and a faster FlashX variant. 320B parameters with 18B active; weights under the MIT license.

Official page

Where it sits in Z.ai's lineup

  1. GLMZ.ai's GLM family of open-weight models for coding, agents, and reasoning.This model
Stack role
Foundation Model / Reasoning Engine
Offered in
2 products
Made by
Z.ai
Family
GLM, version 5.3-Flash
Released
Aug 26, 2026 Current
In its family
The newest of 3 GLM releases.

Strengths and weaknesses

Compare with other models Up to 3 beside GLM-5.3-Flash

  • GLM-5.3-Flash

Suggested comparisons

The models a buyer weighs against GLM-5.3-Flash, picked on list price and on Artificial Analysis's Intelligence Index of overall quality. Add one to draw it beside GLM-5.3-Flash.

GLM-5.3-FlashZ.aiThis page, always on the chart

Among the 40 current releases on this chart, GLM-5.3-Flash's best rank is 15th of 38 on Tool use. On Multimodal, Vals.ai's runs on MMMU-Pro put GLM-5.3-Flash 4.6 points under Claude Fable 5.1. Computer use is Z.ai's own figure on OSWorld 2.0, marked on the chart. It ranks lowest on Math, 27th of 28 and Science, 21st of 24. Instruction following: not yet published by Z.ai 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

  • GLM-5.3-FlashThis pageResults on 11 of 12 axes$0.15 / $0.5071 tokens/sFirst token in 1.6 s

Prices and speeds as of Oct 29, 2026.

GLM-5.3-Flash on each axis11 of 12 with a result

Beside the benchmarks

Cost, speed, capacity, quality and accuracy for GLM-5.3-Flash, with where each figure sits among the 40 models GILD tracks. These measures stand beside the chart and never feed it.

Cost

$0.28per task

Cost per task. One Intelligence Index task at list price, every token counted.

Speed

73tokens a second

Output speed. How fast it writes once the answer starts.

First token
2.8 s
Whole answer
37 s

Capacity

1Mtokens

Context window. The most one request can hold, as the maker states it.

Max output
944K
Reads
Text, Images

Quality

41.8of 100

Intelligence Index. One composite score across independent evaluations.

GDPval knowledge work
1,650 Elo
Terminal-Bench 4.0
32.8%
Epoch Capabilities Index
153

Hallucination

38.3%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
28.2%

Where you can get it

7of 19 routes

Routes offering it. Of the clouds, gateways and coding products GILD tracks, those offering it today.

Open weights
Yes
Licence
MIT
Every route and price

Figures from Artificial Analysis, OpenRouter, Epoch AI and GILD's route matrix, read Oct 26 to 29, 2026. Each measure's definition and source open under . Not yet published for GLM-5.3-Flash: Cached input.

Every value with its benchmark, run and source

GLM-5.3-Flash's result on each axis, with the benchmark, the setting and run, who reported it, the source and the date
AxisBenchmark and settingGLM-5.3-FlashRunSourceDate
Agentic codingTerminal-Bench 4.0Pass@1 averaged over 3 runs, mini-swe-agent harness37.4%GLM 5.3 FlashEffort: not published. Harness: mini-swe-agent.Not drawn: Artificial Analysis 32.8% in another lab's harness, Oct 29, 2026. SourceVals.aiOct 29, 2026
CodeSciCodeSubproblem accuracy, pass@1, highest effort run51.6%GLM-5.3-FlashEffort: max. Harness: none.Artificial AnalysisOct 29, 2026
MathFrontierMath Tiers 1-3Accuracy, pass@1, highest effort run55.8%Effort: max. Harness: none.Epoch AIAug 27, 2026
ScienceGPQA DiamondAccuracy, pass@1, highest effort run90.2%Effort: max. Harness: none.Not drawn: Artificial Analysis 91.2% in another lab's harness, Oct 29, 2026. SourceNot drawn: Vals.ai 86.4% in another lab's harness, Sep 1, 2026. SourceEpoch AIAug 26, 2026
Hard reasoningHumanity's Last ExamAccuracy, text-only questions, no tools39.9%GLM-5.3-FlashEffort: max. Harness: none.Not drawn: Z.ai 55.3% in its maker's own harness, Aug 26, 2026. SourceArtificial AnalysisOct 29, 2026
Tool useAutomationBench-AAScore, Artificial Analysis run60.4%GLM-5.3-FlashEffort: max. Harness: Artificial Analysis AutomationBench harness.Artificial AnalysisOct 29, 2026
Computer useOSWorld 2.0Score as Z.ai reports it, task release, task set and metric not stated. Z.ai's own figure from its launch post. It does not name the OSWorld 2.0 task release, task set, harness or metric. No independent lab has run it.Stands in for OSWorld 2.1, partial score, full 108-task set, v2.1 release, which has no run of GLM-5.3-Flash yet.59.1%GLM-5.3-FlashEffort: not published. Harness: not stated by Z.ai.Z.aiReported by Z.aiAug 26, 2026
Long contextAA-LCR v1.1Accuracy, Artificial Analysis run80%GLM-5.3-FlashEffort: max. Harness: none.Artificial AnalysisOct 29, 2026
MultimodalMMMU-ProAccuracy on Vals.ai's 4-option variant of about 1,700 questions. Vals.ai's run, rank 23 on its MMMU Pro board, last updated September 1, 2026. Vals's variant offers 4 answer options, so it reads higher than the 10-option set.Stands in for MMMU-Pro, overall accuracy, no tools, which has no run of GLM-5.3-Flash yet.86%GLM 5.3 FlashEffort: not published. Harness: none.Vals.aiRun by Vals.aiSep 1, 2026
FactualityAA-Omniscience AccuracyAccuracy, Artificial Analysis run28.2%Hallucination rate 38.3% in the same run (lower is better)GLM-5.3-FlashEffort: max. Harness: none.Artificial AnalysisOct 29, 2026
Answers in fullSpeechMap% Complete over 2,120 contested requests, reasoning-on entry. SpeechMap counted 28.9% of requests denied and 3.3% evaded. 2.4% failed at the provider.65.4%2.4% of requests failed at the provider and count against itEffort: reasoning on. Harness: none.SpeechMap.AIOct 29, 2026
Instruction followingMultiChallengeAccuracy, Scale AI runNot yet published by Z.ai or any independent evaluator, checked Nov 2, 2026

Price and speed

  • GLM-5.3-Flash lists at $0.15 per million input tokens and $0.50 per million output tokens. Z.ai pricing, Oct 29, 2026.
  • GLM-5.3-Flash writes 70.5 output tokens per second on Z AI's API, and its first token arrives 1.6 s after the request. Artificial Analysis, Oct 29, 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.

The result of every model GILD has benchmark results for, on each of the 12 axes. A vendor-reported value is marked. A model's run notes open under its row from its name, each with the axes it covers.
ModelAgentic codingCodeMathScienceHard reasoningTool useComputer useLong contextMultimodalFactualityAnswers in fullInstructions
GLM-5.3-Flash37.4%51.6%55.8%90.2%39.9%60.4%59.1%Reported by Z.aiOSWorld 2.080%86%Run by Vals.aiMMMU-Pro28.2%65.4%Not yet published
GLM-5.338.9%59%68.8%90.9%42.3%62.2%Not yet published79.7%Not yet published32.3%71.5%Not yet published
GLM-5.21%Run by Artificial AnalysisTerminal-Bench 4.051.2%59.2%Not yet published41.1%28.4%Not yet published78.3%Not yet published34.2%Run by Epoch AISimpleQA VerifiedNot yet published73.3%Run by Artificial AnalysisIFBench
Claude Haiku 5.536.4%55%75.1%89.6%44.4%35.4%72.4%Reported by AnthropicOSWorld 2.182.7%Not yet published34.7%78.8%67.7%
Claude Sonnet 5.564.1%61%88.8%95.6%55%71.8%80.1%Reported by Anthropic82.7%Not yet published51.5%81.6%Not yet published
Claude Opus 5.565.2%66.9%91.2%90.6%61.4%69.5%81.8%Reported by Anthropic84.7%87.7%65.5%82.8%Not yet published
Claude Fable 5.158.1%63.1%90.2%93.4%Run by Vals.ai59.1%59.4%80.7%Reported by Anthropic85.3%86.3%Run by Moonshot AI68.4%72.4%Not yet published
Claude Opus 553.5%56.4%85.6%93.9%54.9%56.6%77.7%79.3%84.7%60.9%73.7%Not yet published
Claude Sonnet 510.6%54.3%65.6%80.3%41.3%36.5%57%Reported by Anthropic82%77.3%40.1%Not yet publishedNot yet published
Claude Fable 541.4%61%87%85.9%55.5%54.1%Not yet published82.3%Not yet published65.4%64.8%63.5%Run by Artificial AnalysisIFBench
Claude Opus 4.823.2%54.4%80%91%48.7%45.6%54.8%Run by OSWorld teamOSWorld 2.077.7%Not yet published48.8%67.4%62.2%Run by Artificial AnalysisIFBench
Claude Opus 4.7Not yet published54.5%70.2%86.4%42.3%Not yet published48.9%Run by OSWorld teamOSWorld 2.078.7%78.8%48.9%70.2%58.6%Run by Artificial AnalysisIFBench
Claude Sonnet 4.63%Run by Artificial AnalysisTerminal-Bench 4.050.1%71.1%Run by Epoch AIOTIS Mock AIME 2024-202578.8%33.6%20.1%41.5%Run by OSWorld teamOSWorld 2.080%73.3%40.9%35.8%56.6%Run by Artificial AnalysisIFBench
Claude Opus 4.6Not yet publishedNot yet published66%88.4%39.9%Not yet publishedNot yet published78%75.4%47%59.9%37.2%
Claude Opus 4.5Not yet publishedNot yet published34.4%86%30.1%Not yet publishedNot yet published77.3%74%46.6%57.5%59%
Claude Haiku 4.5Not yet published42.2%66.7%Run by Epoch AIOTIS Mock AIME 2024-202571.2%10.4%3.2%Not yet published74.3%58.6%18%24.7%50.5%
Claude Sonnet 4.5Not yet published45.7%23.9%82.3%17.8%14.1%Not yet published72.3%68.7%32.9%31.4%55.3%
GPT-6.1 Terra49.5%55.1%90.9%94.3%46.2%60.2%74.2%Reported by OpenAIOSWorld 2.182.7%84.7%47.6%Not yet published74.2%
GPT-6.1 Sol55.1%54.2%93.7%95.4%52.9%64.9%78%Reported by OpenAIOSWorld 2.183%86%61.8%69.5%82%
GPT-6 Luna13.6%54.6%78.9%90.5%38.5%53.2%48.9%Run by AnthropicOSWorld 2.183.3%79.7%39.6%63.4%Not yet published
GPT-6 Astra59.6%56.5%93.7%95.8%54.7%68.5%75.8%Run by Meta80.7%86.9%61.6%68.2%Not yet published
GPT-5.6 Sol37.9%57.1%89.1%93.5%49.5%60.1%62.7%Run by OSWorld teamOSWorld 2.084%83.4%69.7%Run by Epoch AISimpleQA VerifiedNot yet published72.7%Run by Artificial AnalysisIFBench
GPT-5.6 Terra22.7%55%86%93.3%42.9%59.6%Not yet published83%80.7%42.3%Not yet published71.2%Run by Artificial AnalysisIFBench
GPT-5.6 Luna11.6%53.6%82.1%91.6%39.5%50.2%Not yet published83.7%78.6%41%Run by Epoch AISimpleQA VerifiedNot yet publishedNot yet published
GPT-5.514.6%Run by Artificial AnalysisTerminal-Bench 4.055.8%85.3%93.5%Run by Artificial Analysis45.8%47.3%49.5%Run by OSWorld teamOSWorld 2.084.3%79.9%63%Run by Epoch AISimpleQA VerifiedNot yet published75.9%Run by Artificial AnalysisIFBench
GPT-5.4Not yet publishedNot yet published78.6%93.3%43.7%Not yet publishedNot yet published82%78.4%Not yet publishedNot yet published73.9%Run by Artificial AnalysisIFBench
gpt-oss-120b14.2%Run by Terminal-Bench (via Epoch AI)Terminal-Bench 2.034%88.9%Run by Epoch AIOTIS Mock AIME 2024-202575.8%19.6%0.2%Not yet published52%Not yet published16.8%Reported by OpenAISimpleQA36.3%45.3%
GPT-6 Sol44.4%57.6%89.8%94.3%47.9%61.6%64.4%Reported by OpenAIOSWorld 2.083.7%82.9%54.5%64%Not yet published
GPT-5.3-CodexNot yet publishedNot yet publishedNot yet published91.5%Run by Artificial Analysis42.5%Not yet publishedNot yet published83.3%78.5%Not yet publishedNot yet published75.4%Run by Artificial AnalysisIFBench
Gemini 4 Argon57.6%61.8%86%96.4%57.1%77.5%69.2%Reported by GoogleOSWorld 2.079.7%Not yet published48.3%87%78.7%
Gemini 3.8 Flash19.7%56.6%68.4%95.4%47.8%59.9%59%Reported by GoogleOSWorld 2.081.3%85.6%50.5%86.1%Not yet published
Gemma 4 31BNot yet published45.5%Not yet published85.7%Run by Artificial AnalysisGPQA Diamond23.6%4.9%Not yet published69.7%73.4%10.4%Run by Epoch AISimpleQA Verified82.3%75.6%Run by Artificial AnalysisIFBench
Gemini 3.1 Pro Preview2.5%58.7%59.6%94.4%47%35.4%Not yet published82%82.4%54.9%59.9%71.4%
Gemini 3.6 Flash7.1%Run by Artificial AnalysisTerminal-Bench 4.053.4%58.9%94.1%40.8%53%83%Reported by GoogleOSWorld-Verified80%83.2%66.2%Run by Epoch AISimpleQA Verified87.2%Not yet published
DeepSeek V4.1 Flash31.8%55.9%71.2%93.2%43.3%66.6%69.3%Run by Moonshot AIOSWorld 2.182.7%80.6%39.6%Not yet published74.9%
DeepSeek V4 Pro14.1%51%64.6%91.7%41%56.7%Not yet published80.3%Not yet published45.2%72%Not yet published
Kimi K3.1 Pro63.6%60.8%Not yet published94.2%Reported by Moonshot AI52.5%73.3%79.6%Reported by Moonshot AIOSWorld 2.186.3%83.9%48.9%Not yet publishedNot yet published
Kimi K317.2%59.5%72.2%93.1%46.9%58.3%Not yet published88.7%80.5%50.6%Run by Epoch AISimpleQA Verified65.9%Not yet published
Kimi K2.7 Code1%Run by Artificial AnalysisTerminal-Bench 4.047.8%54%87.9%35%24.5%Not yet published79.3%Not yet published36.5%Run by Epoch AISimpleQA VerifiedNot yet published63.1%Run by Artificial AnalysisIFBench
Mistral Large 424.7%Run by Artificial AnalysisTerminal-Bench 4.0Not yet publishedNot yet publishedNot yet publishedNot yet published57.8%Not yet publishedNot yet publishedNot yet published26%Not yet publishedNot yet published
Mistral Medium 3.50%Run by Artificial AnalysisTerminal-Bench 4.040.2%Not yet published74.8%Run by Artificial AnalysisGPQA Diamond13.8%6.3%Not yet published69.3%64.9%23%70.5%68.8%Run by Artificial AnalysisIFBench
Grok 4.728.8%57.4%53%92.7%43.1%65.6%Not yet published76.7%Not yet published42.7%64.1%Not yet published
Grok 4.617.2%56.5%66%93.2%42.9%66.7%Not yet published80.3%Not yet published48.9%Run by Epoch AISimpleQA Verified83.6%Not yet published
Grok 4.58.6%55%57.2%93.4%42.7%57.9%Not yet published79.3%80.4%48.3%Run by Epoch AISimpleQA Verified93.1%Not yet published
MiniMax M3.1 Flash25.3%53.4%Not yet publishedNot yet published48%72.5%61.7%Reported by MiniMaxOSWorld 2.079.7%79.1%45.1%Not yet publishedNot yet published
Mercury 33%Not yet publishedNot yet publishedNot yet publishedNot yet publishedNot yet publishedNot yet publishedNot yet publishedNot yet publishedNot yet publishedNot yet publishedNot yet published
Muse Spark Open6.6%58.4%69.8%88.6%43.9%49.7%80.4%Reported by MetaOSWorld-Verified78.3%80.9%42%Not yet published74.2%
Muse Wonder49.5%60.3%82.1%Not yet published50.1%68.1%72.6%Reported by Meta80.7%Not yet published38%42.7%Not yet published
Muse Spark 1.310.6%58.8%74%93.5%Run by Artificial AnalysisGPQA Diamond48.7%57.9%69.6%83%82%38%25.8%Not yet published
Llama 4 ScoutNot yet published21.3%7.8%Run by Epoch AIOTIS Mock AIME 2024-202551.8%3.8%0.2%Not yet published27.7%52.9%Not yet published70.1%39.5%Run by Artificial AnalysisIFBench
Llama 4 MaverickNot yet published31.7%20.6%Run by Epoch AIOTIS Mock AIME 2024-202567%4.9%0.2%Not yet published50%62.1%Not yet published58.3%43%Run by Artificial AnalysisIFBench
Qwen3.8 27B5.6%Run by Artificial AnalysisTerminal-Bench 4.046.6%Not yet published90.5%Run by Artificial AnalysisGPQA Diamond33.9%48.2%84.3%Reported by AlibabaOSWorld-Verified82%76.3%17.3%Not yet published79.5%Reported by AlibabaIFBench
Qwen3.8 Max38.9%52.1%65.6%92.3%43.1%56.2%Not yet published80.3%82.8%29.5%65.3%Not yet published
MiMo-V2.6-Pro31.3%60.9%Not yet publishedNot yet published49.4%58.6%82%Reported by XiaomiOSWorld-Verified86.3%Not yet published34.8%66.5%Not yet published
MiMo-V2.6-Flash24.2%51.3%Not yet publishedNot yet published35.1%64.1%80.8%Reported by XiaomiOSWorld-Verified74.3%73.1%27.2%66.9%Not yet published
Ling 3.1 Flash32.8%Run by Artificial AnalysisTerminal-Bench 4.056.8%Not yet publishedNot yet published43.5%67.5%Not yet published82.3%Not yet published26.5%Not yet publishedNot yet published
Step 5 Preview31.8%58.9%Not yet published93.5%Reported by StepFunGPQA Diamond46.5%51%Not yet published88.3%76.4%36.4%59.7%Not yet published

Agentic codingTerminal-Bench 4.0

  1. Claude Opus 5.565.2%
  2. Claude Sonnet 5.564.1%
  3. Kimi K3.1 Pro63.6%
  4. GPT-6 Astra59.6%
  5. Claude Fable 5.158.1%
  6. Gemini 4 Argon57.6%
  7. GPT-6.1 Sol55.1%
  8. Claude Opus 553.5%
  9. GPT-6.1 Terra49.5%
  10. Muse Wonder49.5%
  11. GPT-6 Sol44.4%
  12. Claude Fable 541.4%
  13. GLM-5.338.9%
  14. Qwen3.8 Max38.9%
  15. GPT-5.6 Sol37.9%
  16. GLM-5.3-Flash37.4%
  17. Claude Haiku 5.536.4%
  18. DeepSeek V4.1 Flash31.8%
  19. Step 5 Preview31.8%
  20. MiMo-V2.6-Pro31.3%
  21. Grok 4.728.8%
  22. MiniMax M3.1 Flash25.3%
  23. MiMo-V2.6-Flash24.2%
  24. Claude Opus 4.823.2%
  25. GPT-5.6 Terra22.7%
  26. Gemini 3.8 Flash19.7%
  27. Kimi K317.2%
  28. Grok 4.617.2%
  29. DeepSeek V4 Pro14.1%
  30. GPT-6 Luna13.6%
  31. GPT-5.6 Luna11.6%
  32. Claude Sonnet 510.6%
  33. Muse Spark 1.310.6%
  34. Grok 4.58.6%
  35. Muse Spark Open6.6%
  36. Mercury 33%
  37. Gemini 3.1 Pro Preview2.5%
  38. Ling 3.1 Flash32.8%Artificial Analysis's run, Terminal-Bench 4.0
  39. Mistral Large 424.7%Artificial Analysis's run, Terminal-Bench 4.0
  40. GPT-5.514.6%Artificial Analysis's run, Terminal-Bench 4.0
  41. gpt-oss-120b14.2%Terminal-Bench's run, Terminal-Bench 2.0
  42. Gemini 3.6 Flash7.1%Artificial Analysis's run, Terminal-Bench 4.0
  43. Qwen3.8 27B5.6%Artificial Analysis's run, Terminal-Bench 4.0
  44. Claude Sonnet 4.63%Artificial Analysis's run, Terminal-Bench 4.0
  45. GLM-5.21%Artificial Analysis's run, Terminal-Bench 4.0
  46. Kimi K2.7 Code1%Artificial Analysis's run, Terminal-Bench 4.0
  47. 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

  1. Claude Opus 5.566.9%
  2. Claude Fable 5.163.1%
  3. Gemini 4 Argon61.8%
  4. Claude Sonnet 5.561%
  5. Claude Fable 561%
  6. MiMo-V2.6-Pro60.9%
  7. Kimi K3.1 Pro60.8%
  8. Muse Wonder60.3%
  9. Kimi K359.5%
  10. GLM-5.359%
  11. Step 5 Preview58.9%
  12. Muse Spark 1.358.8%
  13. Gemini 3.1 Pro Preview58.7%
  14. Muse Spark Open58.4%
  15. GPT-6 Sol57.6%
  16. Grok 4.757.4%
  17. GPT-5.6 Sol57.1%
  18. Ling 3.1 Flash56.8%
  19. Gemini 3.8 Flash56.6%
  20. GPT-6 Astra56.5%
  21. Grok 4.656.5%
  22. Claude Opus 556.4%
  23. DeepSeek V4.1 Flash55.9%
  24. GPT-5.555.8%
  25. GPT-6.1 Terra55.1%
  26. Claude Haiku 5.555%
  27. GPT-5.6 Terra55%
  28. Grok 4.555%
  29. GPT-6 Luna54.6%
  30. Claude Opus 4.754.5%
  31. Claude Opus 4.854.4%
  32. Claude Sonnet 554.3%
  33. GPT-6.1 Sol54.2%
  34. GPT-5.6 Luna53.6%
  35. Gemini 3.6 Flash53.4%
  36. MiniMax M3.1 Flash53.4%
  37. Qwen3.8 Max52.1%
  38. GLM-5.3-Flash51.6%
  39. MiMo-V2.6-Flash51.3%
  40. GLM-5.251.2%
  41. DeepSeek V4 Pro51%
  42. Claude Sonnet 4.650.1%
  43. Kimi K2.7 Code47.8%
  44. Qwen3.8 27B46.6%
  45. Claude Sonnet 4.545.7%
  46. Gemma 4 31B45.5%
  47. Claude Haiku 4.542.2%
  48. Mistral Medium 3.540.2%
  49. gpt-oss-120b34%
  50. Llama 4 Maverick31.7%
  51. Llama 4 Scout21.3%

The other models in the grid have no result on SciCode yet.

MathFrontierMath Tiers 1-3

  1. GPT-6.1 Sol93.7%
  2. GPT-6 Astra93.7%
  3. Claude Opus 5.591.2%
  4. GPT-6.1 Terra90.9%
  5. Claude Fable 5.190.2%
  6. GPT-6 Sol89.8%
  7. GPT-5.6 Sol89.1%
  8. Claude Sonnet 5.588.8%
  9. Claude Fable 587%
  10. GPT-5.6 Terra86%
  11. Gemini 4 Argon86%
  12. Claude Opus 585.6%
  13. GPT-5.585.3%
  14. GPT-5.6 Luna82.1%
  15. Muse Wonder82.1%
  16. Claude Opus 4.880%
  17. GPT-6 Luna78.9%
  18. GPT-5.478.6%
  19. Claude Haiku 5.575.1%
  20. Muse Spark 1.374%
  21. Kimi K372.2%
  22. DeepSeek V4.1 Flash71.2%
  23. Claude Opus 4.770.2%
  24. Muse Spark Open69.8%
  25. GLM-5.368.8%
  26. Gemini 3.8 Flash68.4%
  27. Claude Opus 4.666%
  28. Grok 4.666%
  29. Claude Sonnet 565.6%
  30. Qwen3.8 Max65.6%
  31. DeepSeek V4 Pro64.6%
  32. Gemini 3.1 Pro Preview59.6%
  33. GLM-5.259.2%
  34. Gemini 3.6 Flash58.9%
  35. Grok 4.557.2%
  36. GLM-5.3-Flash55.8%
  37. Kimi K2.7 Code54%
  38. Grok 4.753%
  39. Claude Opus 4.534.4%
  40. Claude Sonnet 4.523.9%
  41. gpt-oss-120b88.9%Epoch AI's run, OTIS Mock AIME 2024-2025
  42. Claude Sonnet 4.671.1%Epoch AI's run, OTIS Mock AIME 2024-2025
  43. Claude Haiku 4.566.7%Epoch AI's run, OTIS Mock AIME 2024-2025
  44. Llama 4 Maverick20.6%Epoch AI's run, OTIS Mock AIME 2024-2025
  45. 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

  1. Gemini 4 Argon96.4%
  2. GPT-6 Astra95.8%
  3. Claude Sonnet 5.595.6%
  4. GPT-6.1 Sol95.4%
  5. Gemini 3.8 Flash95.4%
  6. Gemini 3.1 Pro Preview94.4%
  7. GPT-6.1 Terra94.3%
  8. GPT-6 Sol94.3%
  9. Gemini 3.6 Flash94.1%
  10. Claude Opus 593.9%
  11. GPT-5.6 Sol93.5%
  12. Grok 4.593.4%
  13. GPT-5.6 Terra93.3%
  14. GPT-5.493.3%
  15. DeepSeek V4.1 Flash93.2%
  16. Grok 4.693.2%
  17. Kimi K393.1%
  18. Grok 4.792.7%
  19. Qwen3.8 Max92.3%
  20. DeepSeek V4 Pro91.7%
  21. GPT-5.6 Luna91.6%
  22. Claude Opus 4.891%
  23. GLM-5.390.9%
  24. Claude Opus 5.590.6%
  25. GPT-6 Luna90.5%
  26. GLM-5.3-Flash90.2%
  27. Claude Haiku 5.589.6%
  28. Muse Spark Open88.6%
  29. Claude Opus 4.688.4%
  30. Kimi K2.7 Code87.9%
  31. Claude Opus 4.786.4%
  32. Claude Opus 4.586%
  33. Claude Fable 585.9%
  34. Claude Sonnet 4.582.3%
  35. Claude Sonnet 580.3%
  36. Claude Sonnet 4.678.8%
  37. gpt-oss-120b75.8%
  38. Claude Haiku 4.571.2%
  39. Llama 4 Maverick67%
  40. Llama 4 Scout51.8%
  41. Kimi K3.1 Pro94.2%Moonshot AI's own figure
  42. GPT-5.593.5%Artificial Analysis's run
  43. Muse Spark 1.393.5%Artificial Analysis's run, GPQA Diamond
  44. Step 5 Preview93.5%StepFun's own figure, GPQA Diamond
  45. Claude Fable 5.193.4%Vals.ai's run
  46. GPT-5.3-Codex91.5%Artificial Analysis's run
  47. Qwen3.8 27B90.5%Artificial Analysis's run, GPQA Diamond
  48. Gemma 4 31B85.7%Artificial Analysis's run, GPQA Diamond
  49. 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

  1. Claude Opus 5.561.4%
  2. Claude Fable 5.159.1%
  3. Gemini 4 Argon57.1%
  4. Claude Fable 555.5%
  5. Claude Sonnet 5.555%
  6. Claude Opus 554.9%
  7. GPT-6 Astra54.7%
  8. GPT-6.1 Sol52.9%
  9. Kimi K3.1 Pro52.5%
  10. Muse Wonder50.1%
  11. GPT-5.6 Sol49.5%
  12. MiMo-V2.6-Pro49.4%
  13. Claude Opus 4.848.7%
  14. Muse Spark 1.348.7%
  15. MiniMax M3.1 Flash48%
  16. GPT-6 Sol47.9%
  17. Gemini 3.8 Flash47.8%
  18. Gemini 3.1 Pro Preview47%
  19. Kimi K346.9%
  20. Step 5 Preview46.5%
  21. GPT-6.1 Terra46.2%
  22. GPT-5.545.8%
  23. Claude Haiku 5.544.4%
  24. Muse Spark Open43.9%
  25. GPT-5.443.7%
  26. Ling 3.1 Flash43.5%
  27. DeepSeek V4.1 Flash43.3%
  28. Grok 4.743.1%
  29. Qwen3.8 Max43.1%
  30. GPT-5.6 Terra42.9%
  31. Grok 4.642.9%
  32. Grok 4.542.7%
  33. GPT-5.3-Codex42.5%
  34. GLM-5.342.3%
  35. Claude Opus 4.742.3%
  36. Claude Sonnet 541.3%
  37. GLM-5.241.1%
  38. DeepSeek V4 Pro41%
  39. Gemini 3.6 Flash40.8%
  40. GLM-5.3-Flash39.9%
  41. Claude Opus 4.639.9%
  42. GPT-5.6 Luna39.5%
  43. GPT-6 Luna38.5%
  44. MiMo-V2.6-Flash35.1%
  45. Kimi K2.7 Code35%
  46. Qwen3.8 27B33.9%
  47. Claude Sonnet 4.633.6%
  48. Claude Opus 4.530.1%
  49. Gemma 4 31B23.6%
  50. gpt-oss-120b19.6%
  51. Claude Sonnet 4.517.8%
  52. Mistral Medium 3.513.8%
  53. Claude Haiku 4.510.4%
  54. Llama 4 Maverick4.9%
  55. Llama 4 Scout3.8%

The other models in the grid have no result on Humanity's Last Exam yet.

Tool useAutomationBench-AA

  1. Gemini 4 Argon77.5%
  2. Kimi K3.1 Pro73.3%
  3. MiniMax M3.1 Flash72.5%
  4. Claude Sonnet 5.571.8%
  5. Claude Opus 5.569.5%
  6. GPT-6 Astra68.5%
  7. Muse Wonder68.1%
  8. Ling 3.1 Flash67.5%
  9. Grok 4.666.7%
  10. DeepSeek V4.1 Flash66.6%
  11. Grok 4.765.6%
  12. GPT-6.1 Sol64.9%
  13. MiMo-V2.6-Flash64.1%
  14. GLM-5.362.2%
  15. GPT-6 Sol61.6%
  16. GLM-5.3-Flash60.4%
  17. GPT-6.1 Terra60.2%
  18. GPT-5.6 Sol60.1%
  19. Gemini 3.8 Flash59.9%
  20. GPT-5.6 Terra59.6%
  21. Claude Fable 5.159.4%
  22. MiMo-V2.6-Pro58.6%
  23. Kimi K358.3%
  24. Grok 4.557.9%
  25. Muse Spark 1.357.9%
  26. Mistral Large 457.8%
  27. DeepSeek V4 Pro56.7%
  28. Claude Opus 556.6%
  29. Qwen3.8 Max56.2%
  30. Claude Fable 554.1%
  31. GPT-6 Luna53.2%
  32. Gemini 3.6 Flash53%
  33. Step 5 Preview51%
  34. GPT-5.6 Luna50.2%
  35. Muse Spark Open49.7%
  36. Qwen3.8 27B48.2%
  37. GPT-5.547.3%
  38. Claude Opus 4.845.6%
  39. Claude Sonnet 536.5%
  40. Claude Haiku 5.535.4%
  41. Gemini 3.1 Pro Preview35.4%
  42. GLM-5.228.4%
  43. Kimi K2.7 Code24.5%
  44. Claude Sonnet 4.620.1%
  45. Claude Sonnet 4.514.1%
  46. Mistral Medium 3.56.3%
  47. Gemma 4 31B4.9%
  48. Claude Haiku 4.53.2%
  49. gpt-oss-120b0.2%
  50. Llama 4 Scout0.2%
  51. Llama 4 Maverick0.2%

The other models in the grid have no result on AutomationBench-AA yet.

Computer useOSWorld 2.1

  1. Claude Opus 577.7%
  2. Muse Spark 1.369.6%
  3. Qwen3.8 27B84.3%Alibaba's own figure, OSWorld-Verified
  4. Gemini 3.6 Flash83%Google's own figure, OSWorld-Verified
  5. MiMo-V2.6-Pro82%Xiaomi's own figure, OSWorld-Verified
  6. Claude Opus 5.581.8%Anthropic's own figure
  7. MiMo-V2.6-Flash80.8%Xiaomi's own figure, OSWorld-Verified
  8. Claude Fable 5.180.7%Anthropic's own figure
  9. Muse Spark Open80.4%Meta's own figure, OSWorld-Verified
  10. Claude Sonnet 5.580.1%Anthropic's own figure
  11. Kimi K3.1 Pro79.6%Moonshot AI's own figure, OSWorld 2.1
  12. GPT-6.1 Sol78%OpenAI's own figure, OSWorld 2.1
  13. GPT-6 Astra75.8%Meta's run
  14. GPT-6.1 Terra74.2%OpenAI's own figure, OSWorld 2.1
  15. Muse Wonder72.6%Meta's own figure
  16. Claude Haiku 5.572.4%Anthropic's own figure, OSWorld 2.1
  17. DeepSeek V4.1 Flash69.3%Moonshot AI's run, OSWorld 2.1
  18. Gemini 4 Argon69.2%Google's own figure, OSWorld 2.0
  19. GPT-6 Sol64.4%OpenAI's own figure, OSWorld 2.0
  20. GPT-5.6 Sol62.7%OSWorld team's run, OSWorld 2.0
  21. MiniMax M3.1 Flash61.7%MiniMax's own figure, OSWorld 2.0
  22. GLM-5.3-Flash59.1%Z.ai's own figure, OSWorld 2.0
  23. Gemini 3.8 Flash59%Google's own figure, OSWorld 2.0
  24. Claude Sonnet 557%Anthropic's own figure
  25. Claude Opus 4.854.8%OSWorld team's run, OSWorld 2.0
  26. GPT-5.549.5%OSWorld team's run, OSWorld 2.0
  27. Claude Opus 4.748.9%OSWorld team's run, OSWorld 2.0
  28. GPT-6 Luna48.9%Anthropic's run, OSWorld 2.1
  29. 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

  1. Kimi K388.7%
  2. Step 5 Preview88.3%
  3. Kimi K3.1 Pro86.3%
  4. MiMo-V2.6-Pro86.3%
  5. Claude Fable 5.185.3%
  6. Claude Opus 5.584.7%
  7. GPT-5.584.3%
  8. GPT-5.6 Sol84%
  9. GPT-5.6 Luna83.7%
  10. GPT-6 Sol83.7%
  11. GPT-6 Luna83.3%
  12. GPT-5.3-Codex83.3%
  13. GPT-6.1 Sol83%
  14. GPT-5.6 Terra83%
  15. Muse Spark 1.383%
  16. Claude Haiku 5.582.7%
  17. Claude Sonnet 5.582.7%
  18. GPT-6.1 Terra82.7%
  19. DeepSeek V4.1 Flash82.7%
  20. Claude Fable 582.3%
  21. Ling 3.1 Flash82.3%
  22. Claude Sonnet 582%
  23. GPT-5.482%
  24. Gemini 3.1 Pro Preview82%
  25. Qwen3.8 27B82%
  26. Gemini 3.8 Flash81.3%
  27. GPT-6 Astra80.7%
  28. Muse Wonder80.7%
  29. DeepSeek V4 Pro80.3%
  30. Grok 4.680.3%
  31. Qwen3.8 Max80.3%
  32. GLM-5.3-Flash80%
  33. Claude Sonnet 4.680%
  34. Gemini 3.6 Flash80%
  35. GLM-5.379.7%
  36. Gemini 4 Argon79.7%
  37. MiniMax M3.1 Flash79.7%
  38. Claude Opus 579.3%
  39. Kimi K2.7 Code79.3%
  40. Grok 4.579.3%
  41. Claude Opus 4.778.7%
  42. GLM-5.278.3%
  43. Muse Spark Open78.3%
  44. Claude Opus 4.678%
  45. Claude Opus 4.877.7%
  46. Claude Opus 4.577.3%
  47. Grok 4.776.7%
  48. Claude Haiku 4.574.3%
  49. MiMo-V2.6-Flash74.3%
  50. Claude Sonnet 4.572.3%
  51. Gemma 4 31B69.7%
  52. Mistral Medium 3.569.3%
  53. gpt-oss-120b52%
  54. Llama 4 Maverick50%
  55. Llama 4 Scout27.7%

The other models in the grid have no result on AA-LCR v1.1 yet.

MultimodalMMMU-Pro

  1. Claude Opus 5.587.7%
  2. GPT-6 Astra86.9%
  3. GPT-6.1 Sol86%
  4. Gemini 3.8 Flash85.6%
  5. Claude Opus 584.7%
  6. GPT-6.1 Terra84.7%
  7. Kimi K3.1 Pro83.9%
  8. GPT-5.6 Sol83.4%
  9. Gemini 3.6 Flash83.2%
  10. GPT-6 Sol82.9%
  11. Qwen3.8 Max82.8%
  12. Gemini 3.1 Pro Preview82.4%
  13. Muse Spark 1.382%
  14. Muse Spark Open80.9%
  15. GPT-5.6 Terra80.7%
  16. DeepSeek V4.1 Flash80.6%
  17. Kimi K380.5%
  18. Grok 4.580.4%
  19. GPT-5.579.9%
  20. GPT-6 Luna79.7%
  21. MiniMax M3.1 Flash79.1%
  22. Claude Opus 4.778.8%
  23. GPT-5.6 Luna78.6%
  24. GPT-5.3-Codex78.5%
  25. GPT-5.478.4%
  26. Claude Sonnet 577.3%
  27. Step 5 Preview76.4%
  28. Qwen3.8 27B76.3%
  29. Claude Opus 4.675.4%
  30. Claude Opus 4.574%
  31. Gemma 4 31B73.4%
  32. Claude Sonnet 4.673.3%
  33. MiMo-V2.6-Flash73.1%
  34. Claude Sonnet 4.568.7%
  35. Mistral Medium 3.564.9%
  36. Llama 4 Maverick62.1%
  37. Claude Haiku 4.558.6%
  38. Llama 4 Scout52.9%
  39. Claude Fable 5.186.3%Moonshot AI's run
  40. 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

  1. Claude Fable 5.168.4%
  2. Claude Opus 5.565.5%
  3. Claude Fable 565.4%
  4. GPT-6.1 Sol61.8%
  5. GPT-6 Astra61.6%
  6. Claude Opus 560.9%
  7. Gemini 3.1 Pro Preview54.9%
  8. GPT-6 Sol54.5%
  9. Claude Sonnet 5.551.5%
  10. Gemini 3.8 Flash50.5%
  11. Claude Opus 4.748.9%
  12. Kimi K3.1 Pro48.9%
  13. Claude Opus 4.848.8%
  14. Gemini 4 Argon48.3%
  15. GPT-6.1 Terra47.6%
  16. Claude Opus 4.647%
  17. Claude Opus 4.546.6%
  18. DeepSeek V4 Pro45.2%
  19. MiniMax M3.1 Flash45.1%
  20. Grok 4.742.7%
  21. GPT-5.6 Terra42.3%
  22. Muse Spark Open42%
  23. Claude Sonnet 4.640.9%
  24. Claude Sonnet 540.1%
  25. GPT-6 Luna39.6%
  26. DeepSeek V4.1 Flash39.6%
  27. Muse Wonder38%
  28. Muse Spark 1.338%
  29. Step 5 Preview36.4%
  30. MiMo-V2.6-Pro34.8%
  31. Claude Haiku 5.534.7%
  32. Claude Sonnet 4.532.9%
  33. GLM-5.332.3%
  34. Qwen3.8 Max29.5%
  35. GLM-5.3-Flash28.2%
  36. MiMo-V2.6-Flash27.2%
  37. Ling 3.1 Flash26.5%
  38. Mistral Large 426%
  39. Mistral Medium 3.523%
  40. Claude Haiku 4.518%
  41. Qwen3.8 27B17.3%
  42. GPT-5.6 Sol69.7%Epoch AI's run, SimpleQA Verified
  43. Gemini 3.6 Flash66.2%Epoch AI's run, SimpleQA Verified
  44. GPT-5.563%Epoch AI's run, SimpleQA Verified
  45. Kimi K350.6%Epoch AI's run, SimpleQA Verified
  46. Grok 4.648.9%Epoch AI's run, SimpleQA Verified
  47. Grok 4.548.3%Epoch AI's run, SimpleQA Verified
  48. GPT-5.6 Luna41%Epoch AI's run, SimpleQA Verified
  49. Kimi K2.7 Code36.5%Epoch AI's run, SimpleQA Verified
  50. GLM-5.234.2%Epoch AI's run, SimpleQA Verified
  51. gpt-oss-120b16.8%OpenAI's own figure, SimpleQA
  52. 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

  1. Grok 4.593.1%
  2. Gemini 3.6 Flash87.2%
  3. Gemini 4 Argon87%
  4. Gemini 3.8 Flash86.1%
  5. Grok 4.683.6%
  6. Claude Opus 5.582.8%
  7. Gemma 4 31B82.3%
  8. Claude Sonnet 5.581.6%
  9. Claude Haiku 5.578.8%
  10. Claude Opus 573.7%
  11. Claude Fable 5.172.4%
  12. DeepSeek V4 Pro72%
  13. GLM-5.371.5%
  14. Mistral Medium 3.570.5%
  15. Claude Opus 4.770.2%
  16. Llama 4 Scout70.1%
  17. GPT-6.1 Sol69.5%
  18. GPT-6 Astra68.2%
  19. Claude Opus 4.867.4%
  20. MiMo-V2.6-Flash66.9%
  21. MiMo-V2.6-Pro66.5%
  22. Kimi K365.9%
  23. GLM-5.3-Flash65.4%
  24. Qwen3.8 Max65.3%
  25. Claude Fable 564.8%
  26. Grok 4.764.1%
  27. GPT-6 Sol64%
  28. GPT-6 Luna63.4%
  29. Claude Opus 4.659.9%
  30. Gemini 3.1 Pro Preview59.9%
  31. Step 5 Preview59.7%
  32. Llama 4 Maverick58.3%
  33. Claude Opus 4.557.5%
  34. Muse Wonder42.7%
  35. gpt-oss-120b36.3%
  36. Claude Sonnet 4.635.8%
  37. Claude Sonnet 4.531.4%
  38. Muse Spark 1.325.8%
  39. Claude Haiku 4.524.7%

The other models in the grid have no result on SpeechMap yet.

Instruction followingMultiChallenge

  1. GPT-6.1 Sol82%
  2. Gemini 4 Argon78.7%
  3. DeepSeek V4.1 Flash74.9%
  4. GPT-6.1 Terra74.2%
  5. Muse Spark Open74.2%
  6. Gemini 3.1 Pro Preview71.4%
  7. Claude Haiku 5.567.7%
  8. Claude Opus 4.559%
  9. Claude Sonnet 4.555.3%
  10. Claude Haiku 4.550.5%
  11. gpt-oss-120b45.3%
  12. Claude Opus 4.637.2%
  13. Qwen3.8 27B79.5%Alibaba's own figure, IFBench
  14. GPT-5.575.9%Artificial Analysis's run, IFBench
  15. Gemma 4 31B75.6%Artificial Analysis's run, IFBench
  16. GPT-5.3-Codex75.4%Artificial Analysis's run, IFBench
  17. GPT-5.473.9%Artificial Analysis's run, IFBench
  18. GLM-5.273.3%Artificial Analysis's run, IFBench
  19. GPT-5.6 Sol72.7%Artificial Analysis's run, IFBench
  20. GPT-5.6 Terra71.2%Artificial Analysis's run, IFBench
  21. Mistral Medium 3.568.8%Artificial Analysis's run, IFBench
  22. Claude Fable 563.5%Artificial Analysis's run, IFBench
  23. Kimi K2.7 Code63.1%Artificial Analysis's run, IFBench
  24. Claude Opus 4.862.2%Artificial Analysis's run, IFBench
  25. Claude Opus 4.758.6%Artificial Analysis's run, IFBench
  26. Claude Sonnet 4.656.6%Artificial Analysis's run, IFBench
  27. Llama 4 Maverick43%Artificial Analysis's run, IFBench
  28. 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 GLM-5.3-Flash 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.

Every measure on record for GLM-5.3-Flash: the value, the run it comes from, the publisher and the day it was read
MeasureGLM-5.3-FlashRunSourceRead
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.1MArtificial AnalysisOct 27, 2026
Max outputThe most tokens the model writes in one response.944Kno Z.ai figure in the stored maker sources; OpenRouter lists 943,717OpenRouterOct 29, 2026
ReadsThe kinds of input the model accepts.Text, ImagesArtificial AnalysisOct 27, 2026
LicenceThe terms the weights or the service are offered under.MITArtificial AnalysisOct 27, 2026
Open weightsWhether the maker publishes the weights for anyone to run.YesArtificial AnalysisOct 27, 2026
Routes offering itOf the 19 clouds, gateways and coding products GILD tracks, how many offer the release today, preview included.7 of 19GILD route matrixOct 29, 2026
Cost
Answer tokens per taskLower is better. Answer tokens per Intelligence Index task, reasoning excluded.22,024GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Cost per task, cache readsLower is better. The cache-read share of AA's cost per Intelligence Index task.$0.23GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Cost per task, cache writesLower is better. The cache-write share of AA's cost per Intelligence Index task.$0.018GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Cost per task, inputLower is better. The input-token share of AA's cost per Intelligence Index task.$0.25GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Cost per task, outputLower is better. The answer-token share of AA's cost per Intelligence Index task.$0.034GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Cost per task, reasoningLower is better. The reasoning-token share of AA's cost per Intelligence Index task.$0.023GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Cost per taskLower is better. What one Intelligence Index task cost AA at list price, input, cache, output and reasoning tokens together.$0.28GLM-5.3-FlashArtificial AnalysisOct 27, 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.$305.52GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Output tokens per taskLower is better. Tokens the model wrote per Intelligence Index task, reasoning included: how verbose it is.68,673GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Reasoning tokens per taskLower is better. Reasoning tokens per Intelligence Index task.46,649GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Speed
End-to-end time, 100K promptLower is better. Median seconds for a whole response with a 100,000-token prompt.36 sGLM-5.3-FlashArtificial AnalysisOct 27, 2026
End-to-end response timeLower is better. Seconds AA measures for a whole response on its standard prompt.37 sGLM-5.3-FlashArtificial AnalysisOct 27, 2026
Time to first answer token, 100K promptLower is better. Median seconds to the first answer token with a 100,000-token prompt.31 sGLM-5.3-FlashArtificial AnalysisOct 27, 2026
Time to first answer tokenLower is better. Seconds until the first answer token, after any reasoning, on AA's API runs.30 sGLM-5.3-FlashArtificial AnalysisOct 27, 2026
Output speed, 100K promptMedian output tokens per second when the prompt is about 100,000 tokens long.90 tokens/sGLM-5.3-FlashArtificial AnalysisOct 27, 2026
Output speedMedian output tokens per second on AA's API runs, with the 5th and 95th percentile when AA prints them.73 tokens/sGLM-5.3-FlashArtificial AnalysisOct 27, 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.724 sGLM-5.3-FlashArtificial AnalysisOct 27, 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.8.7 sGLM-5.3-FlashArtificial AnalysisOct 27, 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.2.8 sGLM-5.3-FlashArtificial AnalysisOct 27, 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.38.3%Max effortArtificial AnalysisOct 29, 2026
Omniscience accuracyShare of AA-Omniscience knowledge questions answered correctly.28.2%Max effortArtificial AnalysisOct 29, 2026
Omniscience indexAA-Omniscience index: correct answers minus wrong answers, declines neutral, -100 to 100.7.5GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Knowledge work
AA-BriefcaseAA-Briefcase overall Elo for long business deliverables, with AA's 95% interval.1,450GLM-5.3-FlashArtificial AnalysisOct 27, 2026
AnalystAgent (AA run)AA's AnalystAgent score for analyst workflows.25%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
APEX-Agents (maintainers)APEX-Agents pass@1 from the maintainers, as Epoch AI republishes it.52.8%Mercor (via Epoch AI)Oct 26, 2026
GDP-PDF (AA run)Share of GDP-PDF document tasks where every check passes.15.4%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
GDPval-AAGDPval-AA Elo: AA's run of economically valuable knowledge-work tasks, graded pairwise, with AA's interval.1,650GLM-5.3-FlashArtificial AnalysisOct 27, 2026
MLCR (AA run)AA's MLCR overall score for long, multi-document reading.11.7%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Capability beyond the chart
AA-LCRAA long-context reasoning accuracy.80%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
ALE-BenchALE-Bench performance rating on long optimization contests, as Epoch AI republishes it.304High effortSakana AI (via Epoch AI)Oct 26, 2026
ARC-AGI-2ARC-AGI-2 score from the ARC Prize maintainers, as Epoch AI republishes it.65.8%Max effortARC Prize Foundation (via Epoch AI)Oct 26, 2026
Chess puzzles (Epoch)Share of chess puzzles solved, Epoch AI's run.21%Max effortEpoch AIOct 13, 2026
CritPt (AA run)Share of CritPt research-level physics problems solved in AA's run.15.4%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
CursorBenchCursorBench score from Cursor, as Epoch AI republishes it.36.8%Max effortCursor (via Epoch AI)Oct 26, 2026
DeepSWEDeepSWE pass@1, as Epoch AI republishes it.63.4%Max effortDatacurve (via Epoch AI)Oct 26, 2026
EnterpriseOps-Gym (AA run)AA's EnterpriseOps-Gym score for enterprise operations agents.33.2%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Epoch Capabilities IndexEpoch Capabilities Index: Epoch AI's composite across its benchmark hub, with its confidence interval.153GLM-5.3-FlashEpoch AIOct 26, 2026
FrontierCode (AA run)AA's FrontierCode score: hard, unsaturated coding tasks.41.0%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
FrontierMath Tier 4 (Epoch)Share of FrontierMath Tier 4 research math problems solved, Epoch AI's run.17.1%Max effortEpoch AIAug 27, 2026
FrontierMath Tiers 1 to 3 (Epoch)Share of FrontierMath Tiers 1 to 3 problems solved, Epoch AI's run.55.8%Max effortEpoch AIAug 27, 2026
FrontierSWEFrontierSWE score, as Epoch AI republishes it.18.1%Max effortFrontierSWE (via Epoch AI)Oct 26, 2026
GPQA Diamond (AA run)GPQA Diamond accuracy in AA's run.91.2%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
GPQA Diamond (Epoch)GPQA Diamond accuracy, best scorer, Epoch AI's run.90.2%Max effortEpoch AIAug 26, 2026
Humanity's Last Exam (AA run)Share of Humanity's Last Exam questions answered correctly in AA's run.39.9%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Intelligence IndexArtificial Analysis Intelligence Index: AA's composite of its own evaluation runs, 0 to 100 points.41.8GLM-5.3-FlashArtificial AnalysisOct 27, 2026
IT-Bench SRE (AA run)AA's IT-Bench SRE score for incident and reliability agents.51.2%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Mystery game puzzles (Epoch)Share of mystery game puzzles solved, Epoch AI's run.8%Max effortEpoch AIAug 28, 2026
OTIS Mock AIME (Epoch)Share of OTIS Mock AIME 2024 to 2025 problems solved, Epoch AI's run.93.9%Max effortEpoch AIAug 26, 2026
ProofBenchShare of ProofBench proofs graded correct, from Vals AI as Epoch AI republishes it.21%Max effortVals AI (via Epoch AI)Oct 26, 2026
SciCode (AA run)SciCode subproblem accuracy in AA's run.51.6%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
tau-Banking (AA run)AA's tau-Banking tool-use score.47.2%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Terminal-Bench 4.0 (AA run)Share of Terminal-Bench 4.0 tasks solved in AA's own harness.32.8%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Terminal-Bench Science (AA run)Share of Terminal-Bench Science tasks solved in AA's harness.4.8%GLM-5.3-FlashArtificial AnalysisOct 27, 2026
WebDev ArenaWebDev Arena score from human pairwise votes on web builds, with its 95% interval, as Epoch AI republishes it.1,607LMArena (via Epoch AI)Oct 26, 2026
Domains
EconomicsArtificial Analysis capability score for economics questions, 0 to 100 points.48.9GLM-5.3-FlashArtificial AnalysisOct 27, 2026
EngineeringArtificial Analysis capability score for engineering questions, 0 to 100 points.43.8GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Finance and accountingArtificial Analysis capability score for finance and accounting questions, 0 to 100 points.42GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Healthcare and medicalArtificial Analysis capability score for healthcare and medical questions, 0 to 100 points.38.9GLM-5.3-FlashArtificial AnalysisOct 27, 2026
LegalArtificial Analysis capability score for legal questions, 0 to 100 points.39.9GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Strategy and operationsArtificial Analysis capability score for strategy and operations questions, 0 to 100 points.45.1GLM-5.3-FlashArtificial AnalysisOct 27, 2026
Harvey Legal Lab (AA run)AA's run of Harvey's legal task lab.0.8%GLM-5.3-FlashArtificial AnalysisOct 27, 2026

Routes, prices and uptime

Where GLM-5.3-Flash is offered, what each route charges, and how each endpoint is holding up.

Where it is offered

7 of 19 routes GILD tracks offer GLM-5.3-Flash today, preview included. Read Oct 29, 2026 from each route's own pages.

RouteStatusModel IDLimits and lifecycleSources
Claude CodeAvailableglm-5.3-flashZ.ai documents this route and Anthropic doesn't: point ANTHROPIC_BASE_URL at Z.ai's Anthropic-compatible endpoint; GLM Coding Plan sets glm-5.3-flash[1m] for HaikuZ.aiAnthropic
CursorAvailablenot statedpicker label GLM 5.3 FlashHidden by default; US$0.15 input, US$0.029 cache read, US$0.50 outputCursor
DatabricksAvailabledatabricks-glm-5-3-flashModel service name system.ai.glm-5-3-flash; text and image input; always reasonsDatabricks
The maker's own APIAvailableglm-5.3-flashVideo, image, text and file input; 1M context, 128K output; the faster glm-5.3-flashx is listed beside itZ.ai
GitHub ModelsRetirednot applicableservice retiredservice retired July 30, 2026GitHub
OpenCodeAvailableglm-5.3-flashglm-5.3-flash-freeOpenCode Zen and OpenCode Go; a free glm-5.3-flash-free runs on Zen for a limited timeOpenCode, ModelsOpenCode, Go
OpenRouterAvailablez-ai/glm-5.3-flashz-ai/glm-5.3-flash:batch1,048,576 context for the model; top provider 1,048,575 context and 943,717 output; batch variant listedOpenRouter
Windsurf / DevinAvailableglm-5-3-flash-highglm-5-3-flash-lowglm-5-3-flash-maxPro tier only (Low, High, Max); US$0.15 input, US$0.50 outputCognition

Not offered on 8 routes: Amazon Bedrock, Antigravity, Claude on Google Cloud (Anthropic managed), Claude Platform on AWS, GitHub Copilot, Google Cloud Vertex / Agent Platform (Google managed), Microsoft Foundry and Snowflake Cortex.

3 routes do not say: Codex, Gemini CLI and Grok Build.

What each of these 11 routes' pages say, with sources
RouteStatusModel IDLimits and lifecycleSources
Amazon BedrockNot offeredBedrock lists GLM 4.7, GLM 4.7 Flash, GLM 5 and GLM 5.3; GLM-5.3-Flash isn't among themAmazon Web Services
AntigravityNot offeredAntigravity lists Gemini, Claude and GPT-OSS models onlyGoogle
Claude on Google Cloud (Anthropic managed)Not offeredTakes the Claude API's model IDs, which name Anthropic's Claude models onlyAnthropic, Claude Models on Google Cloud (Anthropic managed)Anthropic, Models overview
Claude Platform on AWSNot offeredClaude Platform on AWS lists Anthropic's Claude models onlyAnthropic
GitHub CopilotNot offeredAbsent from the Copilot supported-models table, which lists each supported modelGitHub
Google Cloud Vertex / Agent Platform (Google managed)Not offeredGoogle lists GLM 5.2, GLM 5 and GLM 4.7; GLM-5.3-Flash isn't among themGoogle Cloud
Microsoft FoundryNot offeredFoundry names no Z.ai model; its other collections come from Cohere, DeepSeek, Meta, Mistral AI and xAIMicrosoft
Snowflake CortexNot offeredCortex lists GLM-5.3; GLM-5.3-Flash isn't on itSnowflake
CodexNot statedZ.ai's Codex guide declares glm-5.3 only; OpenAI documents no other maker's modelZ.aiOpenAI
Gemini CLINot statedGemini CLI's model page names Gemini models onlyGoogle
Grok BuildNot statedGrok Build names only grok-4.7; its docs say it supports any custom model added to its config filexAI (SpaceXSI), Grok Build: SpaceXSI's Coding AgentxAI (SpaceXSI), Bring Grok to

Prices by route

The lowest standard rate is OpenInference (fp4) through OpenRouter, at $0.019 / $0.052 per million input and output 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.

RouteStandard, in / outCache readOther ratesSource, or uptime (1 day / 30 min) and limits
Z.ai APIMaker$0.15 / $0.5Z.ai, Oct 29, 2026
Artificial Analysis price record$0.15 / $0.5$0.03Blended $0.24, 3 input : 1 output, no cacheBlended $0.1, 7 cache-hit : 2 input : 1 outputArtificial Analysis, Oct 27, 2026
OpenRouter$0.15 / $0.5$0.03OpenRouter, Oct 29, 2026
Through OpenRouter, 40 provider endpoints. OpenRouter, Oct 30, 2026
OpenInference (fp4)Lowest$0.019 / $0.052$0.017Up 100% / 100%1.05M context, 944K output
Relace$0.018 / $0.14$0.018Up 99.9% / 100%1.05M context, 131K output
Wafer (us)$0.048 / $0.2$0.047Up 100% / 100%1.05M context, 131K output
Wafer (nvfp4)$0.054 / $0.2$0.053Up 100% / 100%1.05M context, 131K output
Darkbloom (fp4)$0.065 / $0.18$0.012Up 97.7%262K context, 66K output
Morph (fp8)$0.042 / $0.3$0.018Up 100% / 100%1.05M context, 944K output
GMICloud (fp8)50% discount included$0.075 / $0.25$0.015Up 99.3% / 100%1.05M context, 944K output
Novita (fp8)44% discount included$0.084 / $0.28$0.017Up 99.9% / 100%1.05M context, 131K output
StreamLake (fp8)41% discount included$0.088 / $0.29$0.018Up 99.9% / 100%1.02M context, 128K output
Reka$0.12 / $0.3$0.03Up 100% / 100%1.05M context, 944K output
InferenceNet (fp4)$0.11 / $0.35$0.032Up 100% / 100%1.05M context, 262K output
InferenceNet (us)$0.11 / $0.35$0.032Up 100% / 100%1.05M context, 262K output
DigitalOcean$0.11 / $0.38$0.03Up 100% / 100%1.05M context, 131K output
CoreWeave (nvfp4)$0.12 / $0.38$0.02Up 99.9% / 100%1.05M context, 944K output
Near AI (fp8)$0.08 / $0.5$0.012Up 100% / 100%1.05M context, 944K output
Phala (nvfp4)15% discount included$0.13 / $0.42$0.025Up 99.8% / 100%1.05M context, 131K output
Makora$0.14 / $0.47$0.024Up 100% / 100%1.05M context, 131K output
AtlasCloud (fp8)$0.15 / $0.5$0.03Up 98.7%1.05M context, 131K output
BaseTen (fp8, us)$0.15 / $0.5$0.031.05M context, 131K output
BaseTen (fp8)$0.15 / $0.5$0.03Up 98.9% / 100%1.05M context, 131K output
Cohere$0.15 / $0.5$0.03Up 100% / 100%1.05M context, 131K output
Crusoe (fp4)$0.15 / $0.5$0.03Up 100% / 100%1.05M context, 944K output
DeepInfra (fp4)$0.15 / $0.5$0.03Up 100% / 100%1.05M context, 944K output
Fireworks$0.15 / $0.5$0.03Up 100% / 100%1.05M context, 944K output
Friendli$0.15 / $0.5$0.03Up 100% / 100%1.05M context, 944K output
Mistral$0.15 / $0.5$0.03Up 97.8% / 100%1.05M context, 131K output
Modal (fp4)$0.15 / $0.5$0.03Up 100% / 100%1.05M context, 944K output
Nebius (fp4)$0.15 / $0.5$0.03Up 95.7%1.05M context, 944K output
NextBit (fp4)$0.15 / $0.5$0.03Up 98.8% / 100%1.05M context, 128K output
Parasail (fp4)$0.15 / $0.5$0.03Up 100% / 100%1.05M context, 944K output
Particle AI (nvfp4)$0.15 / $0.5$0.03Up 100% / 100%1.05M context, 131K output
PrimeIntellect$0.15 / $0.5$0.03Up 100% / 100%1.05M context, 131K output
Sail Research (fp4)$0.15 / $0.5$0.03Up 100%1.05M context, 131K output
Sail Research (us)$0.15 / $0.5$0.03Up 100%1.05M context, 131K output
SiliconFlow (fp8)$0.15 / $0.5$0.03Up 97.6% / 100%1.05M context, 262K output
Together$0.15 / $0.5$0.03Up 97.4% / 100%1.05M context, 944K output
Venice$0.15 / $0.5$0.03Up 100% / 100%1.05M context, 131K output
Z.AI (fp8)$0.15 / $0.5$0.03Up 100% / 100%1.05M context, 131K output
Inceptron (fp8)$0.19 / $0.6$0.06Up 99.9% / 100%1.05M context, 944K output
Fireworks (us)$0.23 / $0.75$0.045Up 99.8% / 100%1.05M context, 944K output

Lineup

Z.ai's 3 releases in 1 family, oldest to newest along each tier, with list price and speed. GLM-5.3-Flash 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 28, 2026 and Oct 31, 2026.

  1. GLM

    Z.ai's GLM family of open-weight models for coding, agents, and reasoning.

    3 releases

    1. 5.2Current. In 2 products we coverPrice$1.40 / $4.40Speed80.3 tok/s
    2. 5.3Current. In 3 products we coverPrice$1.40 / $4.40Speed85.7 tok/s
    3. This page5.3-FlashCurrent. In 2 products we coverPrice$0.15 / $0.50Speed70.5 tok/s

Where it fits

The layers of Agentic Software Development that GLM-5.3-Flash can power. A product supplies the rest.

Product & Engineering, Software Development

Agentic Software Development

Primary role
Foundation Model / Reasoning Engine

GLM-5.3-Flash can power coding, reasoning, and validation work inside an agentic software-development product. The Model does not itself provide the surrounding interface, execution environment, or system of record.

  1. Work planning & task decomposition

    The Model can assist with work planning & task decomposition when a Product supplies the necessary context and tools.

    Supporting

  2. Codebase context & intelligence

    The Model can assist with codebase context & intelligence when a Product supplies the necessary context and tools.

    Supporting

  3. Coding & implementation

    The Model can assist with coding & implementation when a Product supplies the necessary context and tools.

    Supporting

  4. Debugging & investigation

    The Model can assist with debugging & investigation when a Product supplies the necessary context and tools.

    Supporting

  5. Testing & verification

    The Model can assist with testing & verification when a Product supplies the necessary context and tools.

    Supporting

  6. Code review & quality

    The Model can assist with code review & quality when a Product supplies the necessary context and tools.

    Supporting

Products

We cover 2 products that offer GLM-5.3-Flash today, all subscriptions.

Through a subscription

  1. 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.Go, Go PlusPlans that include it
  2. Cline+39 PositiveOpen-source coding agent for your editor and terminal, on the models you choose.ClinePassPlans that include it

Limits each vendor states

OpenCodeGo, Go Plus
Estimated requests per 5 hours: Go 6,320, Go Plus 18,960; monthly usage: Go $60, Go Plus $180
ClineClinePass
2 to 5 times the usage the standard API rate buys on popular open coding models, measured against a 5-hour rolling window, a weekly limit and a monthly limit; Cline publishes no figures for those limits.

Plan access as each vendor's page states it: Anomaly (Oct 28, 2026), Cline (Oct 28, 2026).

Comparisons

GILD comparisons that name GLM-5.3-Flash, then those of the clouds and APIs that serve it.

What practitioners report

Strongest on Cost, weakest on Speed.

Each figure is the share of accounts naming GLM-5.3-Flash 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.

Share of practitioner accounts favorable to GLM-5.3-Flash on each of seven axes, with the accounts behind each figure
AxisFavorableAccounts
Quality and reasoningWhether its answers and code are right on the author's own task: reasoning, accuracy, instruction following, and consistency.67%14 favorable, 7 not, 21 authors
Tool use and structured outputWhether it calls tools correctly, returns valid structured output, and keeps agent loops on track.33%2 favorable, 4 not, 6 authors
Long contextHow it holds up over long documents, large codebases, long sessions, and many images or PDFs.No accounts yetNone yet
Developer experienceHow it is to build with: prompting, steering, effort settings, and support across SDKs and providers.Only concerns on record0 favorable, 3 not, 3 authors
CostWhat the work costs: price per task, token use, and what caching and batch save.76%16 favorable, 5 not, 21 authors
SpeedResponse time, time to first token, and throughput.33%3 favorable, 6 not, 9 authors
ReliabilityWhether requests keep succeeding: overloaded errors, outages, rate limits, and changes that broke what worked.Only concerns on record0 favorable, 7 not, 7 authors
Every model on these seven axes
Share of accounts favorable on each axis for GLM-5.3-Flash and every other model GILD has accounts for
AxisGLM-5.3-FlashGLM-5.3GLM-5.2Claude Haiku 5.5Claude Sonnet 5.5Claude Opus 5.5Claude Fable 5.1Claude Opus 5Claude Sonnet 5Claude Fable 5Claude Opus 4.8Claude Opus 4.7Claude Sonnet 4.6Claude Opus 4.6Claude Opus 4.5Claude Haiku 4.5Claude Sonnet 4.5GPT-6.1 TerraGPT-6.1 SolGPT-6 LunaGPT-6 AstraGPT-5.6 SolGPT-5.6 TerraGPT-5.6 LunaGPT-5.5GPT-5.4gpt-oss-120bGPT-6 SolGPT-5.3-CodexGemini 4 ArgonGemini 3.8 FlashGemma 4 31BGemini 3.1 Pro PreviewGemini 3.6 FlashDeepSeek V4.1 FlashDeepSeek V4 ProKimi K3.1 ProKimi K3Kimi K2.7 CodeMistral Large 4Mistral Medium 3.5Grok 4.7Grok 4.6Grok 4.5MiniMax M3.1 FlashMercury 3Muse Spark OpenMuse WonderMuse Spark 1.3Llama 4 ScoutLlama 4 MaverickQwen3.8 27BQwen3.8 MaxMiMo-V2.6-ProMiMo-V2.6-FlashLing 3.1 FlashStep 5 Preview
Quality and reasoning67%14 favorable, 7 not, 21 authors76%47 favorable, 15 not, 61 authors71%5 favorable, 2 not, 7 authors73%19 favorable, 7 not, 26 authors64%14 favorable, 8 not, 22 authors68%17 favorable, 8 not, 25 authors54%15 favorable, 13 not, 28 authors50%6 favorable, 6 not, 12 authors56%9 favorable, 7 not, 16 authors68%13 favorable, 6 not, 18 authors68%17 favorable, 8 not, 25 authors57%12 favorable, 9 not, 21 authors67%12 favorable, 6 not, 18 authors70%19 favorable, 8 not, 27 authors62%13 favorable, 8 not, 20 authors58%14 favorable, 10 not, 24 authors69%11 favorable, 5 not, 16 authors33%3 favorable, 6 not, 8 authors55%12 favorable, 10 not, 22 authors55%16 favorable, 13 not, 29 authors44%12 favorable, 15 not, 26 authors33%3 favorable, 6 not, 9 authors38%3 favorable, 5 not, 8 authorsNeeds 5 accounts from 3 authors3 favorable, 1 not, 4 authors33%4 favorable, 8 not, 12 authors33%2 favorable, 4 not, 6 authors58%22 favorable, 16 not, 38 authors31%10 favorable, 22 not, 32 authorsNeeds 5 accounts from 3 authors1 favorable, 3 not, 4 authors63%22 favorable, 13 not, 35 authors56%27 favorable, 21 not, 45 authors80%24 favorable, 6 not, 28 authors56%20 favorable, 16 not, 36 authorsNeeds 5 accounts from 3 authors1 favorable, 3 not, 4 authors61%25 favorable, 16 not, 40 authorsOnly concerns on record0 favorable, 4 not, 4 authors59%20 favorable, 14 not, 32 authorsOnly praise on record3 favorable, 0 not, 3 authors50%3 favorable, 3 not, 6 authors58%11 favorable, 8 not, 18 authors36%5 favorable, 9 not, 14 authors43%16 favorable, 21 not, 36 authors86%6 favorable, 1 not, 7 authors60%3 favorable, 2 not, 5 authors62%13 favorable, 8 not, 21 authors86%18 favorable, 3 not, 21 authorsNeeds 5 accounts from 3 authors2 favorable, 1 not, 3 authors20%1 favorable, 4 not, 5 authorsOnly praise on record2 favorable, 0 not, 2 authors53%8 favorable, 7 not, 15 authors44%8 favorable, 10 not, 17 authors71%24 favorable, 10 not, 34 authors57%4 favorable, 3 not, 7 authors22%2 favorable, 7 not, 9 authors40%4 favorable, 6 not, 10 authorsOnly concerns on record0 favorable, 1 not, 1 authorNeeds 5 accounts from 3 authors2 favorable, 1 not, 3 authors
Tool use and structured output33%2 favorable, 4 not, 6 authors57%4 favorable, 3 not, 7 authorsNo accounts yet67%6 favorable, 3 not, 9 authors40%4 favorable, 6 not, 10 authors46%6 favorable, 7 not, 13 authors30%3 favorable, 7 not, 10 authorsNeeds 5 accounts from 3 authors3 favorable, 1 not, 4 authors70%7 favorable, 3 not, 10 authors57%4 favorable, 3 not, 7 authors50%4 favorable, 4 not, 8 authors50%4 favorable, 4 not, 8 authors29%2 favorable, 5 not, 7 authors33%4 favorable, 8 not, 12 authors70%7 favorable, 3 not, 10 authors43%3 favorable, 4 not, 6 authors50%3 favorable, 3 not, 6 authors40%2 favorable, 3 not, 5 authors40%4 favorable, 6 not, 10 authors33%3 favorable, 6 not, 9 authors62%8 favorable, 5 not, 13 authorsNo accounts yetOnly concerns on record0 favorable, 2 not, 2 authorsOnly praise on record1 favorable, 0 not, 1 authorOnly praise on record1 favorable, 0 not, 1 authorNo accounts yet27%4 favorable, 11 not, 15 authors56%5 favorable, 4 not, 9 authorsNo accounts yet25%2 favorable, 6 not, 8 authors47%7 favorable, 8 not, 15 authors67%4 favorable, 2 not, 6 authors54%7 favorable, 6 not, 12 authorsOnly concerns on record0 favorable, 1 not, 1 author59%10 favorable, 7 not, 17 authorsOnly concerns on record0 favorable, 1 not, 1 authorNeeds 5 accounts from 3 authors3 favorable, 1 not, 4 authorsOnly concerns on record0 favorable, 1 not, 1 authorNo accounts yet38%5 favorable, 8 not, 13 authorsOnly concerns on record0 favorable, 5 not, 5 authors38%6 favorable, 10 not, 16 authorsOnly praise on record2 favorable, 0 not, 2 authorsOnly concerns on record0 favorable, 1 not, 1 author67%4 favorable, 2 not, 6 authors80%8 favorable, 2 not, 10 authorsNo accounts yetOnly concerns on record0 favorable, 1 not, 1 authorNeeds 5 accounts from 3 authors1 favorable, 1 not, 2 authorsNeeds 5 accounts from 3 authors1 favorable, 2 not, 3 authorsOnly concerns on record0 favorable, 4 not, 4 authors46%6 favorable, 7 not, 13 authorsOnly praise on record1 favorable, 0 not, 1 authorOnly concerns on record0 favorable, 5 not, 5 authorsOnly concerns on record0 favorable, 1 not, 1 authorOnly concerns on record0 favorable, 1 not, 1 authorOnly concerns on record0 favorable, 1 not, 1 author
Long contextNo accounts yet40%2 favorable, 3 not, 5 authorsNo accounts yet29%2 favorable, 5 not, 7 authors60%3 favorable, 2 not, 5 authors80%8 favorable, 2 not, 10 authors63%5 favorable, 3 not, 8 authors67%4 favorable, 2 not, 6 authors50%3 favorable, 3 not, 6 authors33%2 favorable, 4 not, 6 authors60%6 favorable, 4 not, 10 authors50%4 favorable, 4 not, 7 authors71%5 favorable, 2 not, 7 authors64%7 favorable, 4 not, 11 authors40%4 favorable, 6 not, 10 authors40%2 favorable, 3 not, 5 authors50%4 favorable, 4 not, 8 authorsOnly concerns on record0 favorable, 1 not, 1 author38%3 favorable, 5 not, 8 authors20%1 favorable, 4 not, 5 authors27%3 favorable, 8 not, 11 authorsNo accounts yetNo accounts yetOnly concerns on record0 favorable, 1 not, 1 authorOnly concerns on record0 favorable, 1 not, 1 authorNeeds 5 accounts from 3 authors2 favorable, 1 not, 3 authors25%2 favorable, 6 not, 8 authorsOnly concerns on record0 favorable, 3 not, 3 authorsNo accounts yet25%2 favorable, 6 not, 8 authors38%5 favorable, 8 not, 13 authors20%1 favorable, 4 not, 5 authors44%7 favorable, 9 not, 16 authorsOnly praise on record1 favorable, 0 not, 1 author58%7 favorable, 5 not, 12 authorsNo accounts yetNeeds 5 accounts from 3 authors3 favorable, 1 not, 4 authorsOnly praise on record1 favorable, 0 not, 1 authorOnly concerns on record0 favorable, 1 not, 1 authorNeeds 5 accounts from 3 authors1 favorable, 3 not, 4 authorsNo accounts yet42%5 favorable, 7 not, 12 authorsNo accounts yetNo accounts yet40%2 favorable, 3 not, 5 authors60%3 favorable, 2 not, 5 authorsNo accounts yetOnly concerns on record0 favorable, 1 not, 1 authorOnly concerns on record0 favorable, 1 not, 1 authorOnly concerns on record0 favorable, 3 not, 3 authorsNeeds 5 accounts from 3 authors2 favorable, 1 not, 3 authors75%6 favorable, 2 not, 8 authorsOnly praise on record1 favorable, 0 not, 1 authorNeeds 5 accounts from 3 authors1 favorable, 1 not, 2 authorsNeeds 5 accounts from 3 authors1 favorable, 1 not, 2 authorsNo accounts yetNo accounts yet
Developer experienceOnly concerns on record0 favorable, 3 not, 3 authors33%5 favorable, 10 not, 15 authorsNo accounts yet25%2 favorable, 6 not, 8 authors38%5 favorable, 8 not, 13 authors38%6 favorable, 10 not, 16 authors50%7 favorable, 7 not, 14 authors25%3 favorable, 9 not, 12 authors29%2 favorable, 5 not, 7 authors25%3 favorable, 9 not, 12 authors53%10 favorable, 9 not, 19 authors29%4 favorable, 10 not, 14 authors57%4 favorable, 3 not, 7 authors45%5 favorable, 6 not, 11 authors67%8 favorable, 4 not, 12 authorsNeeds 5 accounts from 3 authors2 favorable, 2 not, 4 authors50%3 favorable, 3 not, 6 authorsOnly concerns on record0 favorable, 2 not, 2 authors27%3 favorable, 8 not, 11 authors25%2 favorable, 6 not, 8 authors41%7 favorable, 10 not, 17 authorsNo accounts yetOnly concerns on record0 favorable, 1 not, 1 authorNo accounts yetNo accounts yetNo accounts yet14%1 favorable, 6 not, 7 authors25%3 favorable, 9 not, 12 authorsNo accounts yet33%3 favorable, 6 not, 9 authors39%7 favorable, 11 not, 17 authorsOnly concerns on record0 favorable, 7 not, 7 authors40%8 favorable, 12 not, 20 authorsNo accounts yet35%8 favorable, 15 not, 23 authorsNo accounts yet9%1 favorable, 10 not, 11 authorsNo accounts yetOnly concerns on record0 favorable, 1 not, 1 author13%2 favorable, 14 not, 16 authorsOnly concerns on record0 favorable, 9 not, 9 authors25%5 favorable, 15 not, 20 authorsNo accounts yetNo accounts yetOnly concerns on record0 favorable, 7 not, 7 authors11%1 favorable, 8 not, 9 authorsOnly concerns on record0 favorable, 1 not, 1 authorOnly concerns on record0 favorable, 1 not, 1 authorNo accounts yetOnly concerns on record0 favorable, 6 not, 6 authorsOnly concerns on record0 favorable, 1 not, 1 author17%1 favorable, 5 not, 6 authorsOnly concerns on record0 favorable, 1 not, 1 authorOnly concerns on record0 favorable, 1 not, 1 authorOnly concerns on record0 favorable, 1 not, 1 authorNo accounts yetNo accounts yet
Cost76%16 favorable, 5 not, 21 authors35%8 favorable, 15 not, 23 authors71%5 favorable, 2 not, 7 authors69%20 favorable, 9 not, 29 authors45%10 favorable, 12 not, 22 authors56%15 favorable, 12 not, 27 authors20%4 favorable, 16 not, 20 authors43%3 favorable, 4 not, 7 authors39%7 favorable, 11 not, 17 authors8%1 favorable, 11 not, 12 authors40%6 favorable, 9 not, 14 authors50%7 favorable, 7 not, 13 authors50%6 favorable, 6 not, 12 authors33%7 favorable, 14 not, 20 authors53%8 favorable, 7 not, 15 authors29%4 favorable, 10 not, 12 authors38%3 favorable, 5 not, 8 authors80%8 favorable, 2 not, 9 authors74%14 favorable, 5 not, 19 authors79%22 favorable, 6 not, 26 authors29%7 favorable, 17 not, 23 authors57%4 favorable, 3 not, 7 authorsOnly concerns on record0 favorable, 1 not, 1 author50%3 favorable, 3 not, 6 authorsNeeds 5 accounts from 3 authors1 favorable, 3 not, 4 authors63%5 favorable, 3 not, 8 authors60%3 favorable, 2 not, 5 authors55%12 favorable, 10 not, 21 authorsNeeds 5 accounts from 3 authors2 favorable, 1 not, 3 authors35%7 favorable, 13 not, 20 authors58%14 favorable, 10 not, 23 authorsOnly praise on record7 favorable, 0 not, 7 authors50%8 favorable, 8 not, 16 authorsOnly concerns on record0 favorable, 1 not, 1 author66%25 favorable, 13 not, 37 authorsOnly praise on record2 favorable, 0 not, 2 authors40%10 favorable, 15 not, 25 authors71%5 favorable, 2 not, 7 authorsNeeds 5 accounts from 3 authors1 favorable, 3 not, 4 authors27%3 favorable, 8 not, 11 authorsOnly concerns on record0 favorable, 8 not, 8 authors19%5 favorable, 21 not, 26 authors20%1 favorable, 4 not, 5 authorsOnly praise on record4 favorable, 0 not, 4 authors80%12 favorable, 3 not, 15 authors50%6 favorable, 6 not, 12 authorsOnly concerns on record0 favorable, 1 not, 1 authorOnly concerns on record0 favorable, 9 not, 9 authorsNeeds 5 accounts from 3 authors2 favorable, 1 not, 3 authorsOnly praise on record1 favorable, 0 not, 1 authorOnly praise on record1 favorable, 0 not, 1 authorNeeds 5 accounts from 3 authors2 favorable, 2 not, 4 authorsOnly concerns on record0 favorable, 2 not, 2 authorsNeeds 5 accounts from 3 authors1 favorable, 1 not, 2 authorsOnly praise on record3 favorable, 0 not, 3 authorsOnly praise on record1 favorable, 0 not, 1 authorOnly praise on record1 favorable, 0 not, 1 author
Speed33%3 favorable, 6 not, 9 authors20%3 favorable, 12 not, 15 authorsNeeds 5 accounts from 3 authors1 favorable, 1 not, 2 authors74%14 favorable, 5 not, 19 authors88%14 favorable, 2 not, 16 authors46%6 favorable, 7 not, 13 authors44%4 favorable, 5 not, 9 authors60%3 favorable, 2 not, 5 authors44%4 favorable, 5 not, 9 authors57%4 favorable, 3 not, 7 authors31%4 favorable, 9 not, 13 authors63%5 favorable, 3 not, 8 authors17%1 favorable, 5 not, 6 authors36%4 favorable, 7 not, 11 authors73%8 favorable, 3 not, 11 authors78%7 favorable, 2 not, 9 authors75%6 favorable, 2 not, 8 authors83%5 favorable, 1 not, 5 authors37%7 favorable, 12 not, 19 authors45%5 favorable, 6 not, 11 authors41%7 favorable, 10 not, 17 authorsOnly praise on record1 favorable, 0 not, 1 authorNeeds 5 accounts from 3 authors1 favorable, 1 not, 2 authors80%4 favorable, 1 not, 5 authorsNeeds 5 accounts from 3 authors1 favorable, 1 not, 2 authorsNo accounts yet85%23 favorable, 4 not, 27 authors57%4 favorable, 3 not, 7 authorsOnly praise on record1 favorable, 0 not, 1 author45%5 favorable, 6 not, 11 authors61%19 favorable, 12 not, 31 authors53%10 favorable, 9 not, 17 authors35%6 favorable, 11 not, 16 authorsNeeds 5 accounts from 3 authors2 favorable, 1 not, 3 authors68%21 favorable, 10 not, 30 authorsOnly concerns on record0 favorable, 1 not, 1 author21%4 favorable, 15 not, 19 authorsOnly concerns on record0 favorable, 2 not, 2 authorsNo accounts yet21%3 favorable, 11 not, 14 authorsOnly concerns on record0 favorable, 2 not, 2 authors29%5 favorable, 12 not, 17 authorsNeeds 5 accounts from 3 authors1 favorable, 2 not, 3 authorsOnly praise on record2 favorable, 0 not, 2 authors44%4 favorable, 5 not, 9 authors65%13 favorable, 7 not, 20 authorsOnly concerns on record0 favorable, 1 not, 1 authorOnly concerns on record0 favorable, 1 not, 1 authorNeeds 5 accounts from 3 authors1 favorable, 1 not, 2 authorsNeeds 5 accounts from 3 authors3 favorable, 1 not, 4 authorsNo accounts yet33%7 favorable, 14 not, 21 authorsNeeds 5 accounts from 3 authors1 favorable, 3 not, 4 authorsOnly concerns on record0 favorable, 4 not, 4 authorsOnly concerns on record0 favorable, 1 not, 1 authorOnly concerns on record0 favorable, 2 not, 2 authorsOnly concerns on record0 favorable, 1 not, 1 author
ReliabilityOnly concerns on record0 favorable, 7 not, 7 authorsOnly concerns on record0 favorable, 4 not, 4 authorsOnly concerns on record0 favorable, 1 not, 1 author20%1 favorable, 4 not, 5 authors18%2 favorable, 9 not, 11 authors13%1 favorable, 7 not, 8 authors9%1 favorable, 10 not, 11 authors13%1 favorable, 7 not, 8 authors63%5 favorable, 3 not, 7 authors22%2 favorable, 7 not, 9 authors17%2 favorable, 10 not, 12 authors18%2 favorable, 9 not, 11 authors17%1 favorable, 5 not, 6 authors20%3 favorable, 12 not, 15 authors38%6 favorable, 10 not, 16 authors29%2 favorable, 5 not, 7 authors40%4 favorable, 6 not, 10 authorsOnly concerns on record0 favorable, 1 not, 1 author13%1 favorable, 7 not, 8 authorsOnly concerns on record0 favorable, 5 not, 5 authors14%2 favorable, 12 not, 14 authorsOnly concerns on record0 favorable, 4 not, 4 authorsOnly concerns on record0 favorable, 1 not, 1 authorNo accounts yetOnly concerns on record0 favorable, 2 not, 2 authorsOnly concerns on record0 favorable, 4 not, 4 authorsOnly concerns on record0 favorable, 4 not, 4 authors17%1 favorable, 5 not, 6 authorsOnly concerns on record0 favorable, 3 not, 3 authors5%1 favorable, 18 not, 19 authors15%3 favorable, 17 not, 20 authorsOnly concerns on record0 favorable, 5 not, 5 authors14%3 favorable, 19 not, 22 authorsOnly concerns on record0 favorable, 1 not, 1 author42%5 favorable, 7 not, 12 authorsNeeds 5 accounts from 3 authors1 favorable, 1 not, 2 authors8%1 favorable, 12 not, 13 authorsOnly concerns on record0 favorable, 1 not, 1 authorNeeds 5 accounts from 3 authors1 favorable, 1 not, 2 authors38%3 favorable, 5 not, 8 authorsOnly concerns on record0 favorable, 4 not, 4 authors22%2 favorable, 7 not, 9 authorsOnly concerns on record0 favorable, 1 not, 1 authorNo accounts yet60%3 favorable, 2 not, 5 authors33%2 favorable, 4 not, 5 authorsNo accounts yetOnly concerns on record0 favorable, 2 not, 2 authorsOnly concerns on record0 favorable, 1 not, 1 authorNeeds 5 accounts from 3 authors1 favorable, 3 not, 4 authorsOnly concerns on record0 favorable, 1 not, 1 authorOnly concerns on record0 favorable, 9 not, 9 authorsOnly concerns on record0 favorable, 1 not, 1 authorOnly concerns on record0 favorable, 2 not, 2 authorsNo accounts yetNo accounts yetOnly concerns on record0 favorable, 1 not, 1 author
The 55 accounts behind GLM-5.3-Flash's figures

GILD searched Hacker News, GitHub, Reddit and X for accounts that name GLM-5.3-Flash.

Quality and reasoning 67%

  • Favorable
    glm-5.3-flash one-shotted it correctly. Luna max couldn't achieve parity even after 3 total attempts.

    GLM-5.3-Flash. freakynit, Hacker News commenter, self-described, Aug 27, 2026. glm-5.3-flash, used regularly for 4 days, writing Java bindings for latticedb against Luna max.

  • Favorable
    ox-alpha (GLM 5.3 flash) was very close to K3.

    GLM-5.3-Flash. glub, Hacker News commenter, self-described, Aug 26, 2026. ox-alpha (GLM 5.3 flash) on UI design from the author's own data, against GPT 5.6 models, Claude models, GLM 5.3 and Kimi K3.

  • Favorable
    the GLM 5.3 Flash descriptions were the easiest to read, they flow well while also being accurate & including necessary keywords

    MiMo v2.6. SyneRyder, Hacker News commenter, self-described, Sep 21, 2026. GLM 5.3 Flash against Claude, Luna and Deepseek on cleaning up a few thousand page descriptions and titles in a personal search engine index.

  • Favorable
    Review test: caught 6 of 6 planted P1 defects, zero false positives

    My local model setup on an M4 Pro Mac Mini. taylorhou, Hacker News commenter, self-described, Sep 2, 2026. GLM-5.3-Flash at true 8-bit on a 512GB M3 Ultra Mac Studio.

  • Favorable
    GLM 5.3-flash instead was up for it and the code wasn't bad at all.

    Gemini 3.8 Flash and 3.8 Flash Cyber. gere, Hacker News commenter, self-described, Sep 2, 2026. GLM 5.3-flash on a Garmin watch app, after Gemini 3.7 Flash failed.

  • Favorable
    Then I've switched to GLM-5.3-Flash which for this use case has been clearly better at finding suspected causes and following tracks.

    Muse Spark 1.3. WASDx, Hacker News commenter, self-described, Sep 2, 2026. GLM-5.3-Flash on bug hunting while reverse engineering and reimplementing an old game binary, after 1.2 got stuck.

  • Favorable
    with our evals showing we've had no loss in recall, precision etc

    Corporate America is getting hooked on open-source AI. bnchrch, Hacker News commenter, self-described, Sep 4, 2026. GLM 5.3-flash on an operationalized, repeatable workflow.

  • Unfavorable
    It sometimes gets stuck in loops. I once asked it to do a code review of a simple pattern and it went off on a 45 odyssey trying to analyze too much.

    DeepSeek-v4.1-Flash-1017. drob518, Hacker News commenter, self-described, Oct 17, 2026. GLM 5.3 Flash as a daily driver for a couple of months, after DS 4 Flash.

  • Unfavorable
    Both DeepSeek-V4.1-Flash and GLM-5.3-Flash failed to decode your embedded example text.

    Proof of Capture: Apple Reference Image, but open source and using steganography. gpugreg, Hacker News commenter, self-described, Sep 11, 2026. GLM-5.3-Flash on decoding a steganography example.

  • Favorable
    Of the 467 with surviving manifests, 93% were implemented by open-weight models: glm-5.3-flash 72%

    Fagan: frontier models plan and review, open-weight models write code. motock, Hacker News commenter who presents Fagan as the author, self-described, Sep 25, 2026. glm-5.3-flash implementing pull requests in the Fagan pipeline.

  • Unfavorable
    I'd run it on some long running task and come back to it repeating the same tokens or tool calls endlessly, just doing nothing.

    Sonnet 5.5. jchw, Hacker News commenter, self-described, Sep 28, 2026. GLM 5.3 Flash on long running tasks.

  • Unfavorable
    it seems to spiral off into some kind of LLM trance-like state.

    Ask HN: My agent went haywire, does this output mean anything?. nativeit, Hacker News commenter, self-described, Oct 1, 2026. Z.ai's GLM 5.3 Flash in a fresh Hermes agent on a headless Debian server.

  • Favorable
    It's been handling these tasks pretty well.

    One month coding with GLM 5.3 Flash. UncleOxidant, Hacker News commenter, self-described, Oct 2, 2026. GLM 5.3 Flash for a month of coding, on Julia ports and FPGA work with Verilog.

  • Favorable
    Something GLM-5.3 Flash, prior OpenAI models including original GPT-5, anything from Anthropic in the recent years, etc. do not fail at.

    I quit OpenAI because its culture is broken. Topfi, Hacker News commenter, self-described, Oct 4, 2026. GLM-5.3 Flash on the author's eval git tree, against GPT-5.6 Sol and GPT-6 Astra.

  • Favorable
    The best regular LLM judge I tried was GLM-5.3-flash with reasoning which flagged 83.9%

    Decision models like Jev don't beat LLM-as-a-judge or traditional classifiers. rickintoplace, Hacker News commenter, self-described, Oct 6, 2026. GLM-5.3-flash as an LLM judge on 240 ALCE citation examples.

  • Favorable
    Opus 5.5 and GLM 5.3 Flash came out with pretty reasonable code that was fairly fast.

    Recent AI models struggled to match a human algorithmic innovation. nijave, Hacker News commenter, self-described, Oct 11, 2026. GLM 5.3 Flash on an async divide and conquer S3 lister in Python.

  • Unfavorable
    glm 5.3 flash NVFP4 via TP2 DGX Sparks: took 1h 5m to find a p3 documentation issue

    DeepSeek-v4.1-Flash-1017. AmazingTurtle, Hacker News commenter, self-described, Oct 17, 2026. glm 5.3 flash NVFP4 via TP2 DGX Sparks reviewing a dirty C++ game engine code base.

  • Unfavorable
    Least intelligent group but cheapest; solved.

    Root shell on Samsung TV. nekitamo, Hacker News commenter, self-described, Oct 24, 2026. GLM 5.3 Flash / OpenRouter on a crackme reverse-engineering benchmark through Ghidrasql.

  • Favorable
    the resulting UI looked really good.

    GLM-5.3-Flash. terhechte, Hacker News commenter, self-described, Aug 26, 2026. GLM-5.3-Flash, the subject of the launch thread, on a fairly complex UI refactor in a SwiftUI and AppKit app.

  • Favorable
    Successfully spent the first half of the month on just that model.

    One month on GLM 5.3 Flash. Thibaud Colas, Wagtail core team, self-described, Oct 2, 2026. GLM 5.3 Flash for a month of Wagtail engineering on European-hosted inference providers.

  • Favorable
    It passed 23 of 30, at $0.29 per pass.

    Mouse on GLM-5.3-Flash. Pete, Mouse, a coding agent harness built on OpenCode that states it is not affiliated with Z.ai, self-described, Sep 14, 2026. GLM-5.3-Flash (glm-5p3-flash served by Fireworks) in the Mouse harness on the 30 FrontierHarness tasks.

  • Unfavorable
    One way to look at it is that the gap between them on the original environment was largely a gap in exploit finding ability rather than in engineering ability

    Your coding benchmark scores are probably wrong: your eval environment is part of your score. imec aistack team, research group benchmarking self-hosted AI stacks, self-described, Sep 15, 2026. GLM-5.3-Flash (GLM5.3-Flash-FP8) on 4 x H200 at xhigh reasoning across five harnesses on 64 SWE-Bench Pro tasks.

  • Unfavorable
    so far none of them are able to finish the task

    Grok 4.7. includenotfound, Hacker News commenter, self-described, Sep 22, 2026. GLM-5.3 Flash, with DS V4.1 Flash and MiMo V2.6 Flash, on reverse engineering FFT code in an image manipulation library.

Tool use and structured output 33%

Developer experience Only concerns on record

  • Unfavorable
    A lot of inference providers use vllm which makes it hard to find a good provider for GLM.

    MiMo v2.6. joshheitzman, Hacker News commenter, self-described, Sep 21, 2026. GLM-5.3 Flash from friendli.ai as an AI coding agent.

  • Unfavorable
    It was not a model substitution. It was a systems migration.

    Why I Hate Benchmarks: Moving From GPT to GLM-5.3 Flash. Ahmed Elsamadisi, Polyform, in a post labeled Founder argument, self-described, Sep 3, 2026. GLM-5.3 Flash through OpenRouter and a native Z.ai path in Polyform's product, Poly.

  • Unfavorable
    This model always engages in thinking and cannot be disabled; please use low, high, or max

    zai provider: glm-5.3-flash rejects reasoning_effort=medium on the standard endpoint (HTTP 400 / code 1210). marcosayo13, GitHub issue author, self-described, Oct 6, 2026. glm-5.3-flash on Z.AI's standard pay-as-you-go endpoint through Hermes Agent's zai provider.

Cost 76%

  • Favorable
    And at this price point, it's already configured as "designer" model in my oh-my-pi.

    GLM-5.3-Flash. glub, Hacker News commenter, self-described, Aug 26, 2026. ox-alpha (GLM 5.3 flash) on UI design from the author's own data, against GPT 5.6 models, Claude models, GLM 5.3 and Kimi K3.

  • Unfavorable
    price: DS4 wins... $0.0235 vs $0.0242 for ten tasks

    GLM-5.3 is now open-weight. rgbrgb, Hacker News commenter, self-described, Aug 28, 2026. zai-org/GLM-5.3-Flash via together.ai against DeepSeek-V4-Flash on ad detection in transcripts.

  • Unfavorable
    Tasks that would normally cost $0.08 on DSV4-Flash have cost me $0.30+ on GLM-5.3-Flash.

    GLM-5.3 is now open-weight. dw_arthur, Hacker News commenter, self-described, Aug 28, 2026. GLM-5.3-Flash on High through OpenRouter, against DSV4-Flash on Max through DeepSeek's API.

  • Favorable
    It's practically impossible to spend more than $5-$10 per day if you're only working on a single project

    Claude Fable 5.1 and Claude Mythos 5.1. sz4kerto, Hacker News commenter, self-described, Sep 1, 2026. GLM 5.3 Flash on a single coding project, at first always instructed by Fable.

  • Favorable
    I'm finding it mindblowing having entire software features built out for 5 cents, 20 cents.

    Elevated Errors for Multiple Models. SyneRyder, Hacker News commenter, self-described, Sep 3, 2026. GLM 5.3 Flash with vision, thinking on Chinese servers.

  • Favorable
    came back after it had done so and the API cost was a fraction of a cent.

    We must pace the frontier. SturgeonsLaw, Hacker News commenter, self-described, Sep 13, 2026. GLM-5.3-flash in Pi, priced at $0.5/1M output tokens on OpenRouter.

  • Favorable
    now, with GLM 5.3-flash, we've brought the cost down to $7k-9k

    Corporate America is getting hooked on open-source AI. bnchrch, Hacker News commenter, self-described, Sep 4, 2026. GLM 5.3-flash on an operationalized, repeatable workflow.

  • Favorable
    And you can't beat the price. Even Deepseek 4.1 Flash is more expensive.

    DeepSeek-v4.1-Flash-1017. drob518, Hacker News commenter, self-described, Oct 17, 2026. GLM 5.3 Flash as a daily driver for a couple of months, after DS 4 Flash.

  • Favorable
    I use glm-5.3-flash every day for a ludicrous 8.5USD/month

    Why isn't the industry freaking out about DeepSeek 4.1 Flash?. alexfortin, Hacker News commenter, self-described, Oct 8, 2026. glm-5.3-flash every day on the Z.ai lite subscription.

  • Favorable
    API may be expensive, but I do 900m tokens (95% cached, ~0.4% output) on Z.ai's $18/mo coding plan

    Xiaomi Mimo 2.6 live post-training dashboard. ignoramous, Hacker News commenter, self-described, Sep 17, 2026. GLM 5.3 Flash on Z.ai's US$18 a month coding plan.

  • Favorable
    The max plan will provide ~1,100 USD of GLM-5.3 or ~260 USD of GLM-5.3-flash per month for 168 USD. I can personally attest to these numbers

    How GLM built its own inference infrastructure. _aavaa_, Hacker News commenter, self-described, Sep 17, 2026. GLM-5.3-flash on the Z.ai max plan through omp.

  • Unfavorable
    using GLM-5.3 Flash from them is more expensive then using DS V4 Flash from deepinfra.com

    MiMo v2.6. joshheitzman, Hacker News commenter, self-described, Sep 21, 2026. GLM-5.3 Flash from friendli.ai as an AI coding agent.

  • Favorable
    I would say that I hit a daily or weekly limit MAYBE once a month.

    Tokens too cheap to meter. alexpotato, Hacker News commenter, self-described, Sep 24, 2026. GLM-5.3-Flash on Ollama Cloud's US$20 a month plan.

  • Unfavorable
    the cache rates for GLM 5.3 flash are quite a bit more expensive than for DeepSeek, and a simple runs ended up costing me three bucks in oversight.

    Best LLM for every budget, updated daily. vardalab, Hacker News commenter, self-described, Sep 24, 2026. GLM 5.3 flash on Fireworks' API, in OMP runs with an advisor model.

  • Unfavorable
    Max did get about 5% higher scores, but at >2X the $/pass.

    One month coding with GLM 5.3 Flash. lhl, Hacker News commenter, self-described, Oct 3, 2026. GLM-5.3 Flash at low, medium, xhigh and max on about 300 agentic and coding tasks.

  • Favorable
    Lately I've been using GLM 5.3 Flash (from Fireworks), and my spend is $1-$2 per day when I use it for coding

    Why isn't the industry freaking out about DeepSeek 4.1 Flash?. kelnos, Hacker News commenter, self-described, Oct 9, 2026. GLM 5.3 Flash from Fireworks for coding.

  • Favorable
    GLM 5.3 Flash all achieve about the same results but for ~1/10th the cost.

    Gemini 4 Argon. chriddyp, Hacker News commenter, self-described, Oct 14, 2026. GLM 5.3 Flash on the commenter's 23 question data analytics exam.

  • Favorable
    | GLM 5.3 Flash / OpenRouter | Least intelligent group but cheapest; solved. | 340,006 | 11,205 | $0.01 |

    Root shell on Samsung TV. nekitamo, Hacker News commenter, self-described, Oct 24, 2026. GLM 5.3 Flash / OpenRouter on a crackme reverse-engineering benchmark through Ghidrasql.

  • Favorable
    GLM-5.3 Flash was dramatically cheaper than the models we were using.

    Why I Hate Benchmarks: Moving From GPT to GLM-5.3 Flash. Ahmed Elsamadisi, Polyform, in a post labeled Founder argument, self-described, Sep 3, 2026. GLM-5.3 Flash through OpenRouter and a native Z.ai path in Polyform's product, Poly.

  • Favorable
    That model's usage was well within our budget ($68, about 4kWh of energy use / 365 grams of carbon emissions).

    One month on GLM 5.3 Flash. Thibaud Colas, Wagtail core team, self-described, Oct 2, 2026. GLM 5.3 Flash for a month of Wagtail engineering on European-hosted inference providers.

  • Favorable
    so Mouse on Flash comes within one or two tasks of them at a seventh to a ninth of the cost per pass.

    Mouse on GLM-5.3-Flash. Pete, Mouse, a coding agent harness built on OpenCode that states it is not affiliated with Z.ai, self-described, Sep 14, 2026. GLM-5.3-Flash (glm-5p3-flash served by Fireworks) in the Mouse harness on the 30 FrontierHarness tasks.

  • Favorable
    Did 30M tokens through glm 5.3 flash today for 52c

    Agent memory as a file format. zackify, Hacker News commenter, self-described, Aug 31, 2026. glm 5.3 flash in pi with a few extensions, initial context 4k at most.

Speed 33%

  • Favorable
    latency: GLM wins... 108s total against 154s

    GLM-5.3 is now open-weight. rgbrgb, Hacker News commenter, self-described, Aug 28, 2026. zai-org/GLM-5.3-Flash via together.ai against DeepSeek-V4-Flash on ad detection in transcripts.

  • Unfavorable
    Also GLM-5.3-Flash is so slow compared to DSV4-Flash.

    GLM-5.3 is now open-weight. dw_arthur, Hacker News commenter, self-described, Aug 28, 2026. GLM-5.3-Flash on High through OpenRouter, against DSV4-Flash on Max through DeepSeek's API.

  • Favorable
    I could still have this model running at 50-60 tokens/s with 1M context, indefinitely

    Muse Spark Open. colingauvin, Hacker News commenter, self-described, Oct 16, 2026. GLM 5.3 Flash as a ~4 bpw EXL3 quant on 2x DGX Sparks.

  • Unfavorable
    It does feel slow, thinking away on the Chinese servers

    Elevated Errors for Multiple Models. SyneRyder, Hacker News commenter, self-described, Sep 3, 2026. GLM 5.3 Flash with vision, thinking on Chinese servers.

  • Unfavorable
    recently tried switching from GLM 5.3 Flash to Gemini 3.8 Flash because of higher tok/s and better rate limits

    Gemini 3.8 Live and 3.8 Live Extended Thinking. lostmsu, Hacker News commenter, self-described, Sep 15, 2026. GLM 5.3 Flash in a voice assistant that drives AI harnesses.

  • Unfavorable
    They're serving me 5.3 at ~40 tok/s and 5.3-flash at 30 tok/s (according to omp).

    How GLM built its own inference infrastructure. _aavaa_, Hacker News commenter, self-described, Sep 17, 2026. GLM-5.3-flash on the Z.ai max plan through omp.

  • Unfavorable
    I was getting 300+ tokens/sec with DS vs ~100 with GLM in my testing.

    One month coding with GLM 5.3 Flash. RussianCow, Hacker News commenter, self-described, Oct 2, 2026. GLM 5.3 Flash against DeepSeek V4.1 Flash in the author's testing.

  • Favorable
    It was also quite fast.

    GLM-5.3-Flash. terhechte, Hacker News commenter, self-described, Aug 26, 2026. GLM-5.3-Flash, the subject of the launch thread, on a fairly complex UI refactor in a SwiftUI and AppKit app.

  • Unfavorable
    A longer generation (a few hundred words) has hit `postOnce wall-clock deadline 300s exceeded` on the first attempt, then retried.

    glm-5.3-flash reasoning time hits the 300s deadline on long replies. bkataru, owner of the zeptoclaw repository, self-described, Oct 30, 2026. z-ai/glm-5.3-flash as zeptoclaw's primary model since 0.9.1.

Reliability Only concerns on record

No published account yet for Long context.