Claude Opus 5.5 vs GPT-6 Astra
Claude Opus 5.5 scores higher on the Models at Work leaderboard (91 vs 86 out of 100). Claude Opus 5.5 is about 2.5× cheaper per blended million tokens ($8 vs $20). On Artificial Analysis' independent Intelligence Index, Claude Opus 5.5 leads 58 to 53.
| Claude Opus 5.5 · Anthropic | GPT-6 Astra · OpenAI | |
|---|---|---|
| Leaderboard rank | #1 | #2 |
| Score (0–100) | 91 | 86 |
| Tier | Frontier | Frontier |
| Price per 1M tokens | $4 in · $20 out | $10 in · $50 out |
| Context window | 1M | 1M |
| Open weights | No | No |
| Artificial Analysis Intelligence Index | 58 | 53 |
| Arena text score | 1507 | — |
| Humanity's Last Exam, Diamond (Scale) | 55.0 | 60.6 |
| Epoch Capabilities Index | 167 | — |
| Humanity's Sixth Sense (Scale) | — | 53.6 |
| FrontierMath Erdős (Epoch) | — | 3% (2 of 68) |
| Best for | Agentic coding, Long-running agents, Enterprise knowledge work | Hard reasoning, Research-grade analysis, When cost is not the constraint |
Choose Claude Opus 5.5 if…
- you need agentic coding
- you need long-running agents
- you need enterprise knowledge work
The model people actually ship agents on: top of the independent index, a price that does not need a budget meeting, and a community that complains mainly about how much it has to be used.
Caveats: Arena puts Gemini 4 Argon ahead on chat preference; the gap is inside two scores' error bars.
Choose GPT-6 Astra if…
- you need hard reasoning
- you need research-grade analysis
- you need when cost is not the constraint
The reasoning heavyweight: leads the hardest exams and the cipher-cracking party tricks, and charges like it.
Caveats: Long-context pricing doubles above 272K input tokens. Speed is the lowest of the frontier set in Artificial Analysis' measurement.
What practitioners say
Claude Opus 5.5: The launch thread was the biggest Claude thread of the year on Hacker News, a port of the TypeScript compiler to Rust credited the model with a working build in ten hours, and a Vals AI materials-discovery run used it for agentic simulation. A 'has it been nerfed yet' tracker is the most-upvoted scepticism.
GPT-6 Astra: The launch drew the largest model thread of 2026 on Hacker News, and the stories that followed were about capability rather than cost: an Enigma message unsolved since 2005, a WWI cipher, a driving benchmark.
Questions people ask
Which is better, Claude Opus 5.5 or GPT-6 Astra?
Claude Opus 5.5 scores higher on the Models at Work leaderboard (91 vs 86 out of 100). Claude Opus 5.5 is about 2.5× cheaper per blended million tokens ($8 vs $20). On Artificial Analysis' independent Intelligence Index, Claude Opus 5.5 leads 58 to 53.
When should I choose Claude Opus 5.5 over GPT-6 Astra?
Choose Claude Opus 5.5 for agentic coding, long-running agents, enterprise knowledge work. The model people actually ship agents on: top of the independent index, a price that does not need a budget meeting, and a community that complains mainly about how much it has to be used.
When should I choose GPT-6 Astra over Claude Opus 5.5?
Choose GPT-6 Astra for hard reasoning, research-grade analysis, when cost is not the constraint. The reasoning heavyweight: leads the hardest exams and the cipher-cracking party tricks, and charges like it.
Which is cheaper, Claude Opus 5.5 or GPT-6 Astra?
Claude Opus 5.5 costs $4 per million input tokens and $20 per million output tokens on Anthropic's list price (Claude pricing, read Oct 10, 2026). GPT-6 Astra costs $10 per million input tokens and $50 per million output tokens on OpenAI's list price (OpenAI API pricing, read Oct 10, 2026).
Scores come from the Models at Work leaderboard: Score is 0 to 100: 50% independent evals normalised within this table, 20% price-performance, 15% practitioner sentiment from Signals and community threads, 15% operational fit (context, speed, availability, open weights). Models within three points are a tie in practice. Vendor-published figures are labelled as such and never counted as independent. Prices are vendor list prices where published; blended figures assume three input tokens per output token.