Comparison · Anthropic and OpenAI
Claude Opus 5.5 vs GPT-6 Astra
Listed API prices of Claude Opus 5.5 and GPT-6 Astra turned into the cost of the same three standard tasks, with speed from our own test requests in the United States and what each provider’s API documentation says about features that break on a switch. Output quality and answer length are not compared.
Calculated impact cost per task and per month
For an L2 task (deep analysis over a 50,000-token document, 3 steps), Claude Opus 5.5 costs $0.696 and GPT-6 Astra $1.74 at the prices listed in the United States on , so Claude Opus 5.5 is 60.0 % less than GPT-6 Astra, counting visible output only, without cache discounts or hidden reasoning.
| Task | Claude Opus 5.5, per task | GPT-6 Astra, per task | Claude Opus 5.5, per month | GPT-6 Astra, per month | Claude Opus 5.5 vs GPT-6 Astra, per task |
|---|---|---|---|---|---|
| L1Micro-task (classification or strict JSON extraction) | $0.010per L1 task | $0.025per L1 task | $146.00per month | $365.00per month | −60.0 % |
| L2Deep analysis over a 50,000-token document | $0.696per L2 task | $1.74per L2 task | $10,161.60per month | $25,404.00per month | −60.0 % |
| L3Agentic coding loop, 15 steps | $5.44per L3 task | $13.61per L3 task | $79,453.20per month | $198,633.00per month | −60.0 % |
Calculated Per month: 20 tasks an hour around the clock, 730 hours.
Prices USD per 1M tokens
| Price | Claude Opus 5.5 | GPT-6 Astra |
|---|---|---|
| Input, per 1M tokens | $4.00 | $10.00 |
| Output, per 1M tokens | $20.00 | $50.00 |
| Price from | Provider's price list · source page Published | Provider's price list · source page Published |
| Read | · the United States | · the United States |
| Context window | 1,000,000 tokens | 1,050,000 tokens |
What can I use it for? six common tasks, our data checks
Quality on your task: not measured by Measured Lab yet.
| Task | Claude Opus 5.5 | GPT-6 Astra |
|---|---|---|
| Answer customer messages | Fits on price and context | Fits on price and context |
| Pull data from invoices and documents with strict JSON and page images | Fits on price and context | Fits on price and context |
| Summarize documents and meetings | Fits on price and context | Fits on price and context |
| Translate content | Fits on price and context | Fits on price and context |
| Chat assistant on your website | Fits on price and context | Fits on price and context |
| Write and review code with tool calls | Fits on price and context | Fits on price and context |
| Tasks it fits | 6 of 6 | 6 of 6 |
Checks of our data for six common tasks: a published price, a context window and answer length for the task and, where the task needs them, strict JSON output, page images and tool calls; prices, cost a month and terms for each model on its model card. Compare models for your own task in the model picker.
Speed in the US Observed our test requests
In our test requests from the United States, the median time to the first answer token was 3,129 ms for Claude Opus 5.5 (Direct API · Anthropic) and 3,933 ms for GPT-6 Astra (Direct API · OpenAI).
| Model and route | Time to first answer token, median | Output speed, median | Answers |
|---|---|---|---|
| Claude Opus 5.5Direct API · Anthropic | 3,129 ms range 689 ms–4,982 ms | 154 tokens/s range 101 tokens/s–229 tokens/s | 115 answers on 23 dates |
| GPT-6 AstraDirect API · OpenAI | 3,933 ms range 1,537 ms–9,119 ms | 120 tokens/s range 38.4 tokens/s–214 tokens/s | 115 answers on 23 dates |
The same short request to every model, sent from a measurement point in the United States; medians of successful answers on at least 2 different dates, latest on . How we measure
What breaks when you switch Published provider documentation
Moving from Anthropic to OpenAI: every feature that Anthropic’s documentation lists as supported is also listed as supported by OpenAI.
Moving from OpenAI to Anthropic: every feature that OpenAI’s documentation lists as supported is also listed as supported by Anthropic.
| Feature of the API | Anthropic | OpenAI |
|---|---|---|
| Strict JSON schemas | Yes | Yes |
| Parallel tool calls | Yes | Yes |
| Streamed tool arguments | Yes | Yes |
| Reasoning with tools | Yes | Yes |
| Cache covers tool definitions | Yes | Yes |
What each provider’s own API documentation says, read ; “not stated” is not the same as “no”. Quotes and sources: what breaks when you switch AI APIs.