GILD verdict
Choose the Claude API when the hardest work decides it: Opus 5.5 leads the Artificial Analysis Intelligence Index v5.0 at 58 (Oct 27, 2026).
Teams that run at scale buy both and route by task, as DoorDash and Replit do, so decide which one carries each workload. Choose the OpenAI API when cost per task decides it: GPT-6.1 Sol scores 55 at US$1.27 per task, against Sonnet 5.5's 54 at US$10.02 at the same list price. Of the two, only Claude is sold on Google Cloud. At GILD's workload of 60,000 cached tasks a month, Sonnet 5.5 and GPT-6.1 Sol both cost US$1,632, while 300,000-token prompts cost twice as much on OpenAI's long-context rates. Run your own evaluation set on both with caching on before you commit volume.
Teams at scale run both: DoorDash serves Claude and OpenAI models behind one gateway, each with a cloud fallback, and Replit uses Claude Sonnet for coding beside OpenAI models in other agent roles. On price, OpenAI's models finish the same work for less, and Claude's lead at the top costs more per task. On the Artificial Analysis Intelligence Index v5.0 (Oct 27, 2026), Sonnet 5.5 generated more tokens than any other current Claude model, and GPT-6 Astra outscored Fable 5.1, 57 to 50, at about 57% of its cost per task. The earlier Artificial Analysis index run of Oct 25, 2026 put Astra level with Fable 5.1 on coding agent tasks at about 60% of the cost. Fable 5.1 was the top public model in Arena's live agent sessions when Arena tested its router. That router still sent 67% of coding sessions to GPT-6 Astra at medium effort.
What teams report
Run both 2
DoorDash
DoorDash put OpenAI, Anthropic and Gemini models behind one API and SDK so product teams can swap models with one change. The Claude API falls back to Amazon Bedrock and OpenAI to Azure, and the gateway attributes cost to each department (InfoQ talk, Oct 3, 2026).
“As we started thinking about these scenarios, we need one surface that is usable with the whole company. The LLM Gateway is a concept that we came up with at that point. From an engineering point of view, we went to our users, understood what the problem was. This is early days. There are a lot of different providers. There is OpenAI models. Then came Claude models. Then came Gemini models. Then came open-source models. Now almost every product team is trying to experiment with them, they want to move fast. If they have to create their own plumbing for each of these different providers, each of these different types of models, then they were getting slow. From a velocity point of view, we quickly realized that we need one API and one SDK kind of approach where we do the plumbing for you. You get one API. You want to try an OpenAI model. At that point, new model frequency was also pretty good. Every couple of months, you'll see one model that is supposed to be better than the other. Any team who is experimenting, they want to quickly try that model to see, is this application performing better with this new open-source model versus Anthropic's model versus an OpenAI's model. One API, one SDK, you make one change, and you can try a new model for you.”
Siddharth Kodwani, Software Engineer, GenAI Platform, DoorDashInfoQ, , recording Replit
Replit's agent used Claude Sonnet as its coding model and OpenAI models for other roles in its multi-agent system, such as critique (Lenny's Podcast, Nov 21, 2024).
“The most important model that we use is the Sonnet model from Claude, from Anthropic, and it is the best model at coding. So that's the model we use for coding, but we use models from OpenAI as well because a multi-agent system.”
Amjad Masad, Co-founder and CEO, ReplitLenny's Podcast, , at 32:51
Choosing between Claude API and OpenAI API
Choose Claude API if
- Your cloud agreement runs through Google Cloud, which sells Claude; OpenAI lists only Amazon Bedrock and Microsoft Azure as cloud platforms for its models.
- Your workloads send long prompts: current Claude models bill the full 1M-token context at standard rates, except Haiku 5.5.
- A platform team wants Claude Code's agent loop as a library through the Agent SDK.
Choose OpenAI API if
- Your team already builds on the Responses API or the OpenAI Agents SDK.
- You want OpenAI's hosted Agents API, which runs the Codex harness and manages agent state.
- You need EU data residency on GPT-6 Luna, which is available with Standard, Fast, Flex, and Batch processing.
Side by side
| Dimension | Claude API | OpenAI API |
|---|---|---|
| Independent scoresArtificial Analysis Intelligence Index v5.0 (Oct 27, 2026) at each model's highest-scoring reasoning effort, xhigh for Fable 5.1 and max for the others, and its average API cost per index task at list prices with caching, checked Oct 28, 2026. | Opus 5.5 58 at US$10.04 per task; Sonnet 5.5 54 at US$10.02; Fable 5.1 50 at US$10.89; Haiku 5.5 36 at US$0.4113456 | GPT-6 Astra 57 at US$6.24 per task; GPT-6.1 Sol 55 at US$1.27; GPT-6.1 Terra 51 at US$0.96; GPT-6 Luna 28 at US$0.151789 |
| Monthly bill at one workloadGILD's calculation from published rates, fetched Oct 28, 2026: 2,000 tasks a day for 30 days, each with 2,000 new input tokens, 1,000 cache-write tokens, 7,000 cache-read tokens and 2,000 output tokens, at standard list prices. The long-prompt line sends 300,000 uncached input tokens and 2,000 output tokens per task. Taxes and negotiated discounts are excluded. | US$1,632 a month on Claude Sonnet 5.5 with caching; US$37,200 if each task sends a 300,000-token prompt12 | US$1,632 a month on GPT-6.1 Sol with caching; US$73,800 if each task sends a 300,000-token prompt, at its long-context rates24 |
| Current lineupStandard list prices in US dollars per million input / output tokens. | Fable 5.1 US$10 / US$50; Opus 5.5 US$4 / US$20; Sonnet 5.5 US$2 / US$10; Haiku 5.5 US$0.10 / US$0.50 (prompts up to 100,000 tokens) | GPT-6 Astra US$10 / US$50; GPT-6.1 Sol US$2 / US$10; GPT-6.1 Terra US$1 / US$5; GPT-6 Luna US$0.10 / US$0.50 (prompts up to 272,000 tokens) |
| Context window | 1M tokens on all four current models, with up to 128K output tokens | 1,050,000 tokens on GPT-6 Astra, GPT-6.1 Sol, GPT-6.1 Terra, and GPT-6 Luna, with up to 128,000 output tokens |
| Long prompts | Standard rates across the full 1M context; Haiku 5.5 costs 5x more above 100,000 prompt tokens | Long-context rates above 272,000 input tokens (GPT-6 Astra: US$20 input, US$75 output) |
| Cached input | Writes cost 1.25x the input price (5-minute cache) or 2x (1 hour); reads cost 0.05x on Opus 5.5 and Sonnet 5.5, 0.025x on Fable 5.1, and 0.1x on other models | Writes cost 1.25x the input price for the default 30-minute cache on GPT-5.6 and later, or 2.5x for a 12-hour cache; reads cost 0.1x, or 0.05x on GPT-6.1 Sol and GPT-6.1 Terra |
| Batch | 50% off input and output; most batches finish within 1 hour, and every batch ends within 24 hours | 50% off with a 24-hour completion window; Flex processing at the same 50% rate |
| Where it runs | Claude API, Claude Platform on AWS, and Microsoft Foundry at Anthropic's rates. Claude Models on Google Cloud (Anthropic managed) through Google Cloud Marketplace. Amazon Bedrock and Google Cloud (Google managed) at the cloud provider's prices | OpenAI API at OpenAI's rates, with regional processing by approval at a 10% uplift on models released on or after March 5, 2026. Microsoft Azure and Amazon Bedrock sell the same GPT-6 models on their own bills |
| Default retention | Inputs and outputs deleted within 30 days; zero data retention by request, except Fable 5.1, Fable 5, and the Mythos models, which require 30-day retention unless Anthropic expressly authorizes zero data retention | Abuse monitoring logs kept up to 30 days; Zero Data Retention or Modified Abuse Monitoring by approval |
| Self-serve spend caps | Start US$500, Build US$1,000, Scale US$200,000 per month; Custom tier by arrangement | Build US$500, Launch US$5,000, Grow US$200,000 per month; Scale Tier and Reserved Tier for committed capacity |
| Agent tooling | Agent SDK (Claude Code's agent loop as a library), Managed Agents (beta, US$0.08 per session-hour plus tokens), and the MCP connector (beta). Managed Agents can run multi-agent workflows (beta). Its web_fetch tool fetches only URLs that already appeared in the session, and a limited-network environment applies its allowed_hosts list to web search and web fetch | Agents SDK, the hosted Agents API running the Codex harness, and remote MCP servers in the Responses API |
What practitioners report, side by side
Each product's figures come from its own practitioners, for the group you pick.
Overall signal
Teams and leaders rate Claude API +25, 22 points above OpenAI API at +3. Most of the gap is in complaints, with 20 of OpenAI API's 65 sources negative against 9 of Claude API's 52.
The score counts the authors from teams and leaders that take a side, one vote each with their latest view, and is how far positive votes lead negative ones, per 100 votes, so Claude API's +25 rests on votes split 20 positive, 20 mixed and 8 negative and OpenAI API's +3 on 21, 22 and 19.
A lead of a fifth of the votes reads positive and a lead of half strong positive, with the same lines below zero. The dots show each source, positive, mixed, negative. Run a pilot on your own prompts to measure output quality and cost per task.
Footprint on the AI Application Platform stack
They differ on two of 9 layers. On Observability, Claude API touches it and OpenAI API supports it. On Guardrails, Claude API touches it and OpenAI API supports it. Coverage is each product's placement on this stack, as its product page shows it. Core is a job the product owns; Supports and Touches are jobs it helps with. Coverage says nothing about how well a product does them.
| Layer | Claude API | OpenAI API |
|---|---|---|
| Operate | ||
| Observability | Touches | Supports |
| Guardrails | Touches | Supports |
The 7 layers where they match
| Layer | Claude API | OpenAI API |
|---|---|---|
| Model | ||
| Model APIs | Core | Core |
| Model gateway | Touches | Touches |
| Build | ||
| Orchestration | Supports | Supports |
| Tool use | Core | Core |
| Data | ||
| Retrieval | Supports | Supports |
| Evaluate | ||
| Evaluation | Touches | Touches |
| Operate | ||
| Cloud deployment | Supports | Supports |
What practitioners praise
Among teams and leaders, OpenAI API alone has three praise themes that reach 5 sources from 3 authors. Stability and Tool Calling & Structured Output lead, at 2 of 5 and 1 of 8. Of the three themes both carry, Cost Efficiency differs most. It is praised in all 8 Claude API sources that discuss it, against 12 of 14 for OpenAI API. Tool Use & Structured Output reaches 5 sources from 3 authors only for Claude API, at 10 of 12. OpenAI API needs a pilot on the same prompts before you choose on it.
| Praised for | Claude API | OpenAI API |
|---|---|---|
| Model Quality | 16 of 20 | 14 of 18 |
| Cost Efficiency | 8 of 8 | 12 of 14 |
| Speed | 8 of 10 | 6 of 8 |
| Tool Use & Structured Output | 10 of 12 | Not a theme in its sources |
| Stability | 0 of 1 so far | 2 of 5 |
| Tool Calling & Structured Output | Not a theme in its sources | 1 of 8 |
| Long Context | 4 of 4 so far | 0 of 5 |
Each count is the sources that praise a theme, or raise it as a concern, out of those that discuss it. A row pairs the two products when both carry a theme under the same or an equivalent name.
A count ending in "so far" has under 5 sources from 3 authors among teams and leaders. "Not a theme in its sources" means GILD found no theme of that name in that product's sources.
What practitioners raise as concerns
Among teams and leaders, OpenAI API alone has five concern themes that reach 5 sources from 3 authors. Inconsistent Results and Model & API Changes lead, at 7 of 7 and 7 of 8. Of the three themes both carry, Setup & SDK Friction differs most. It is raised in 3 of the 5 Claude API sources that discuss it, against 4 of 5 for OpenAI API. Model Changes and Tool Calling Failures reach 5 sources from 3 authors only for Claude API, at 8 of 11 and 5 of 7. OpenAI API needs a pilot on the same prompts before you choose on either.
| Raised as a concern | Claude API | OpenAI API |
|---|---|---|
| Token Cost | 11 of 12 | 7 of 7 |
| Latency | 3 of 5 | 9 of 12 |
| Setup & SDK Friction | 3 of 5 | 4 of 5 |
| Model Changes | 8 of 11 | Not a theme in its sources |
| Tool Calling Failures | 5 of 7 | Not a theme in its sources |
| Inconsistent Results | 3 of 3 so far | 7 of 7 |
| Model & API Changes | Not a theme in its sources | 7 of 8 |
| Long-Context Limits | 3 of 3 so far | 6 of 6 |
| Data Handling | 2 of 3 so far | 5 of 7 |
| Rate Limits & Reliability | Not a theme in its sources | 4 of 8 |
31 sources
- Artificial Analysis: LLM Leaderboard (Oct 28, 2026)
- Artificial Analysis: Changelog (Oct 28, 2026)
- Artificial Analysis: Claude Opus 5.5 analysis (Oct 28, 2026)
- Artificial Analysis: Claude Sonnet 5.5 analysis (Oct 28, 2026)
- Artificial Analysis: Claude Fable 5.1 analysis (Oct 28, 2026)
- Artificial Analysis: Claude Haiku 5.5 analysis (Oct 28, 2026)
- Artificial Analysis: GPT-6 Astra analysis (Oct 28, 2026)
- Artificial Analysis: GPT-6.1 Sol analysis (Oct 28, 2026)
- Artificial Analysis: GPT-6 Luna analysis (Oct 28, 2026)
- Arena: Introducing Max in Agent Arena (Oct 12, 2026)
- Claude API docs: Models overview
- Claude API docs: Pricing
- Claude API docs: Prompt caching
- Claude API docs: Batch processing
- Claude API docs: Rate limits
- Claude API docs: API and data retention
- Anthropic Privacy Center: How long do you store my organization's data?
- Claude API docs: Features overview
- Claude API docs: Claude Models on Google Cloud (Anthropic managed)
- Claude Code docs: Agent SDK overview
- Claude API docs: Managed Agents overview
- Claude API docs: Claude Platform release notes
- OpenAI API docs: Models
- OpenAI API docs: Pricing
- OpenAI API docs: Prompt caching
- OpenAI API docs: Batch API
- OpenAI API docs: Rate limits
- OpenAI API docs: Data controls
- OpenAI API docs: Agents
- OpenAI API docs: MCP servers
- OpenAI API docs: OpenAI on Amazon Bedrock