Claude Opus 5.5 vs GPT-6 Astra: Benchmarks, Price and Which to Choose

Side by side

Benchmark rows appear only where both models have a published score, so the comparison is like for like. Grades run from S (best) to D. Data as of October 2, 2026.

Price

• Claude Opus 5.5: $4 / $20 per 1M tokens, input / output.
• GPT-6 Astra: $10 / $50 per 1M tokens, input / output.

Output tokens cost several times more than input tokens on both, so the difference matters most for tasks that produce long answers: code generation, long documents, agent runs with many steps. For tasks that read a lot and answer briefly, such as classification or extraction, input price dominates.

A quick way to estimate: multiply your monthly input tokens by the input price and your output tokens by the output price. If the difference is small next to an engineer's time, choose on quality. If you run millions of requests, price will decide.

Context window

Claude Opus 5.5 accepts 1M tokens and GPT-6 Astra accepts 1M. A large window lets you send a whole codebase or a long contract in one request, but every token sent is paid for on every call, and accuracy tends to fall as prompts grow. Send what the task needs.

Where each one is the better fit

Use the grade rows in the table above:

• Coding and agents: compare the coding grade and, where both are published, SWE-bench Verified and Arena Elo (WebDev).
• Reasoning and analysis: compare GPQA Diamond and the reasoning grade.
• Math: compare the AIME row and the math grade.
• Writing and chat: compare Arena Elo (Text), which reflects human preference.
• Vision: compare the vision grade.

Where the grades are equal, the models are close enough that price, speed, rate limits and the tools around the model should decide. Those practical points often matter more than a one-point benchmark gap:

• Existing integration. Staying on the SDK and tooling your team already uses has real value.
• Rate limits and availability in your region and tier.
• Data terms. Read each provider's retention and training terms for your plan.

Test on your own work

Benchmarks describe average behaviour on public tests. Your prompts are not average. Before committing:

1. Collect 20 to 30 real tasks from your product.
2. Run both models with the same prompts.
3. Have someone judge the answers without knowing which model wrote which.
4. Record cost and latency for each run.

An afternoon of this tells you more than any leaderboard, including ours.

Is a flagship needed at all?

For much everyday work the answer is no. The cheapest model the leaderboard grades A or better overall is Qwen3.7 Flash ($0.03 / $0.13 per 1M tokens). Routing easy requests to a model like that and hard ones to a flagship is the most common way teams cut cost.

Other comparisons

The same table is available for any pair in Compare LLMs, and the current standings are in the LLM Leaderboard.

Frequently asked questions

Which is cheaper, Claude Opus 5.5 or GPT-6 Astra?

Per one million tokens, Claude Opus 5.5 costs $4 / $20 (input / output) and GPT-6 Astra costs $10 / $50.

Which has the larger context window?

Claude Opus 5.5 accepts 1M tokens and GPT-6 Astra accepts 1M.

Which is better for coding?

On the id8 leaderboard, Claude Opus 5.5 has a coding grade of S and GPT-6 Astra has S. Check the benchmark rows in the table for the scores both have published.

Should I pick one for everything?

Usually not. Many teams send hard tasks to a flagship model and routine tasks to a cheaper model graded A, which cuts cost substantially with little loss in quality.

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