Claude Opus 5.5 vs GPT-6: Price and Task Fit
Compare verified token prices, context windows and repeatable task results for Claude Opus 5.5 and GPT-6 Astra, Sol and Luna.
This comparison was checked on September 26, 2026. Prices and context values come from the public BazaarLink catalogue and official model documentation. The benchmark figures are from a small independent Braintrust evaluation and are directional only.
Current price and context
| Model | Input / 1M | Output / 1M | Context | Official positioning |
|---|---|---|---|---|
| Claude Opus 5.5 | $4.00 | $20.00 | 1,000,000 | Long-running agentic coding and knowledge work |
| GPT-6 Astra | $10.00 | $50.00 | 1,050,000 | Highest-capability GPT-6 tier |
| GPT-6 Sol | $2.00 | $10.00 | 1,050,000 | Strong reasoning with a cost balance |
| GPT-6 Luna | $0.10 | $0.50 | 1,050,000 | Cost-sensitive, repeatable high-volume work |
These are base catalogue rows. The current catalogue also lists higher prompt tiers for Astra, Sol and Luna above 272,000 prompt tokens. Check the tier before estimating a very long request.
Simple cost examples
For 100,000 input tokens and 10,000 output tokens, the estimated totals are:
| Model | Estimated total |
|---|---|
| Claude Opus 5.5 | $0.60 |
| GPT-6 Astra | $1.50 |
| GPT-6 Sol | $0.30 |
| GPT-6 Luna | $0.015 |
The calculation is input tokens multiplied by the input rate plus output tokens multiplied by the output rate, with both token counts divided by 1,000,000.
What does the independent comparison show?
Braintrust’s public page describes a 175-task run with 25 examples from each of seven task families. It reports:
| Model | Problems solved | Writing quality |
|---|---|---|
| Claude Opus 5.5 | 80.1% | 84.4% |
| GPT-6 Luna | 78.0% | 83.8% |
| GPT-6 Sol | 76.8% | 84.1% |
The page says the differences were not statistically significant. Read this as a small directional signal, not a general ranking. Re-run the comparison with your own prompts, grading rubric and output limits before choosing a default.
Which GPT-6 tier should you compare with Opus?
Use Astra when the task is unusually difficult and the highest GPT-6 capability is worth the price. Use Sol as the first comparison for demanding work where cost matters. Use Luna for repeated classification, extraction, summaries or other tasks with a clear quality bar and high volume.
Opus 5.5 is a strong comparison point for long-context coding and knowledge work, especially when its adaptive reasoning and 128K maximum output match the task. The right comparison is therefore tier-specific: Opus versus Astra for maximum capability, Opus versus Sol for capability and cost balance, and Opus versus Luna for a cost-first baseline.
Decision table
| Your priority | Start with | Validate |
|---|---|---|
| Highest capability for difficult end-to-end work | Opus 5.5 or Astra | Correctness, tool use and long-answer quality |
| Strong reasoning with a more moderate token price | Sol | Quality on hard cases and total token cost |
| Large repeated workloads with a clear acceptance test | Luna | Error rate, format adherence and escalation rate |
| Long-context coding or knowledge work | Opus 5.5 | Context use, maximum answer length and cache mix |
Sources
FAQ
Which GPT-6 model is the fairest first comparison with Opus 5.5?
Start with Sol when you want a demanding-work comparison with a more moderate token price. Add Astra for the highest-capability comparison and Luna for a cost-first, high-volume baseline.
Does the Braintrust result prove that Opus 5.5 is the best model?
No. It was a 175-task directional run, and the page says the differences were not statistically significant. Re-test with your own tasks and rubric.
What is the current Opus 5.5 context and maximum output?
The current Anthropic documentation lists a 1,000,000-token context window and a 128,000-token maximum output.
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