Decision summary

Claude API / Bedrock / Vertex / Foundry -> Claude Opus 5.5
OpenAI Responses, Codex, or Bedrock gpt-6-astra -> GPT-6 Astra
Lower published token price -> Claude Opus 5.5
OpenAI computer-use and max effort -> GPT-6 Astra
Need both -> pilot the same repo; do not rank vendor tables against each other

Frontier flagship comparison

Frontier comparison

Claude Opus 5.5 vs GPT-6 Astra

Name: Claude Opus 5.5 vs GPT-6 Astra
Keywords: Frontier flagship comparison

Claude Opus 5.5 and GPT-6 Astra are the current hardest generally documented models from Anthropic and OpenAI.

Featured · Updated today · Last verified: September 2026

Choose Claude Opus 5.5 when

Claude-stack long-running coding and knowledge work at a lower list price than Fable 5.1 or Astra.

Choose GPT-6 Astra when

OpenAI-stack teams that need the hardest coding, computer-use, and document agents.

Claude API / Bedrock / Vertex / Foundry: Claude Opus 5.5
OpenAI Responses, Codex, or Bedrock gpt-6-astra: GPT-6 Astra
Lower published token price: Claude Opus 5.5

Decision axes: Best for · Access and platforms · Context window · Input and output

Reasoning

Opus 5.5 thinking is always on. Astra effort runs from low through max.

Coding

Anthropic's table is vendor-reported. Astra leads Terminal-Bench-Science; Opus 5.5 leads several coding rows.

Multimodal

Both are text and image in, text out.

Speed

Opus 5.5 Fast mode is $8/$40. Astra Fast mode is 2× Standard.

Enterprise

Claude multi-cloud IDs versus OpenAI Critical-cyber gates.

Curated matrix for Claude Opus 5.5 vs GPT-6 Astra — confirm live limits and pricing on each vendor’s official pages.

How they compare

Criterion-by-criterion notes from the catalog—not a ranking. Validate on your own gold set.

Criterion	Claude Opus 5.5	GPT-6 Astra
Best for	Claude-stack long-running coding and knowledge work at a lower list price than Fable 5.1 or Astra.	OpenAI-stack teams that need the hardest coding, computer-use, and document agents.
Access and platforms	API ID claude-opus-5-5 on Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, and Claude Platform on AWS. Released September 22, 2026.	API ID gpt-6-astra. ChatGPT paid rollout; Amazon Bedrock. Free API tier unsupported.
Context window	1M tokens; 128K synchronous max output. Reliable knowledge cutoff June 2026.	1,050,000 input tokens; 128,000 max output. Knowledge cutoff April 30, 2026.
Input and output	Text and image input; text output.	Text and image input; text output.
Reasoning control	Adaptive thinking is always on and cannot be disabled. Default effort is medium.	reasoning.effort from low through max, including xhigh. Fast mode is 2× Standard.
Coding and agents	Anthropic positions it for long-running agentic coding. Published scores use Anthropic's harness and are not interchangeable with OpenAI tables.	Responses API tools include computer use, hosted shell, apply patch, code interpreter, and MCP.
Cost posture	$4 input / $20 output per 1M. Cache reads $0.20; 5-minute cache writes $5; 1-hour cache writes $8. Fast mode is $8/$40.	$10 input / $1 cached / $12.50 cache write / $50 output per 1M. Requests over 272K input tokens use long-context multipliers.
Operational risk	Forced tool use errors, thinking blocks tied to the conversation, and Fable-like biology and cybersecurity safeguards.	OpenAI's safety overview marks Astra at Preparedness Critical cyber. Workspace enablement is gated.

Key insights

Concrete technical or product signals.

List price is not workload cost. Opus 5.5 is $4/$20; Astra is $10/$50, with different cache math.
On Anthropic's September 22 table, Opus 5.5 leads several coding rows and Astra leads Terminal-Bench-Science 0.1 (64.6 vs 58.7). Those figures are vendor-reported.
Do not treat a higher score on one harness as a universal winner.

Use cases

Where this shines in production.

Choosing a September 2026 cross-vendor flagship
Pricing a Claude Opus route against OpenAI Astra
Long-horizon coding-agent procurement

Limitations & trade-offs

What to watch for.

Product-fit comparison only. No single winner is awarded.
Anthropic's table mixes its own runs with figures it attributes to OpenAI.
ChatGPT and Claude consumer availability can lag the API cards.

Watch-outs

Who should pass, and what to validate before rollout.

Do not choose Astra for a free API tier.
Do not choose Opus 5.5 when you need OpenAI computer-use on the Responses API.
Do not treat Anthropic's score table as an OpenAI leaderboard.

Final recommendation

What to do after reading the table.

Pilot both on the same repo. Standardize on the stack you already operate unless the eval clearly prefers the other ID.

FAQ

Is Claude Opus 5.5 better than GPT-6 Astra?

Pilot both on the same repo. Standardize on the stack you already operate unless the eval clearly prefers the other ID.

Which is better for coding: Claude Opus 5.5 or GPT-6 Astra?

Claude Opus 5.5: Anthropic positions it for long-running agentic coding. Published scores use Anthropic's harness and are not interchangeable with OpenAI ta. GPT-6 Astra: Responses API tools include computer use, hosted shell, apply patch, code interpreter, and MCP. The catalog does not declare a winner on coding and agents.

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

Listed unit prices: Claude Opus 5.5 $4 input / $20 output per 1M. Cache reads $0.20; 5-minute cache writes $5; 1-hour cache writes $8. Fast mode is $8/$40; GPT-6 Astra $10 input / $1 cached / $12.50 cache write / $50 output per 1M. Requests over 272K input tokens use long-context multipliers. A lower listed unit price is not the same as a lower total workload cost—tokens, session minutes, caching, introductory rates, and add-on modes can reverse the ranking. Confirm live rates on each vendor’s official page.

Can I use both Claude Opus 5.5 and GPT-6 Astra?

Yes. Many teams keep both and route by constraint. Use Claude Opus 5.5 for Claude-stack long-running coding and knowledge work at a lower list price than Fable 5.1 or Astra, and GPT-6 Astra for OpenAI-stack teams that need the hardest coding, computer-use, and document agents. Confirm vendor terms before mixing them in one production workflow.