Claude Opus 5.5
- Input / 1M tokens
- $4.00
- Output / 1M tokens
- $20.00
API models · Side by side
Claude Opus 5.5 vs GPT-6.1 Sol
Opus 5.5 and GPT-6.1 Sol both support document, image and tool-driven work. Their ordinary token rates differ enough that a coding assistant or document service should budget complete tasks before choosing.
GPT-6.1 Sol
- Input / 1M tokens
- $2.00
- Output / 1M tokens
- $10.00
The short answer
GPT-6.1 Sol has lower base input and output rates and slightly more context capacity. Long-prompt pricing narrows some of the cost difference; the response length and number of calls still matter.
Compare the details
Compare context, supported inputs and usage costs. Highlighted rows show a difference.
| Feature | Claude Opus 5.5 | GPT-6.1 Sol |
|---|---|---|
| Input formats | file, image, text | file, image, text |
| Output formats | text | text |
| Context / position limit | 1,000,000 | 1,050,000 |
| Maximum output tokens | 128,000 | 128,000 |
| Developer features | Reasoning output, Completion limit, Output limit, Reasoning controls, Thinking effort, Response format, Stop sequences, Structured output, Tool selection, Tool calling, Response length | Reasoning output, Completion limit, Output limit, Reasoning controls, Thinking effort, Response format, Seed, Structured output, Tool selection, Tool calling, Response length |
| Cached input / 1M tokens | $0.20 | $0.10 |
| Cache write / 1M tokens | $5.00 | $2.50 |
| USD input / 1M tokens | $4.00 | $2.00 |
| USD output / 1M tokens | $20.00 | $10.00 |
| Model identifier | anthropic/claude-opus-5.5 | openai/gpt-6.1-sol |
API pricing
Prices are in USD per million tokens. Input is what you send; output is what the model generates. Claude and ChatGPT subscriptions are billed separately.
GPT-6.1 Sol: long-prompt rates
- Long-prompt threshold: 272,000 prompt tokens. Input: $4.00 / 1M. Output: $15.00 / 1M.
Tools, images and additional reasoning can add charges. The examples below cover input and output tokens only.
Compare local modelsWhat those prices mean in practice
Each example uses the same token budget for both models. These are calculations, not measured workloads. Cache discounts, media, tools and extra reasoning are excluded.
| Workload | Claude Opus 5.5 | GPT-6.1 Sol |
|---|---|---|
| 1,000 short requestsPer request: 2,000 input + 500 output tokens | $18.00 | $9.00 |
| 100 document summariesPer request: 20,000 input + 1,000 output tokens | $10.00 | $5.00 |
| 10 long-document requestsPer request: 300,000 input + 2,000 output tokens | $12.40 | $12.30 |
Estimate = requests × (input tokens × input price + output tokens × output price) ÷ 1,000,000. Long-prompt rates are applied where the pricing data provides them. Real requests can use different amounts of output.
Beyond the price tag
Count the whole coding task
A coding assistant may inspect files, call tools, attempt a change and then review it. Every model call contributes to the task cost. Comparing only the first prompt misses the history sent on later calls and the generated code or reasoning. Use a defined change with a clear acceptance condition when deciding between these models. Record total calls and the final result rather than assuming the more expensive endpoint always solves the task better.
Reading a repository or document collection
Both models allow large contexts and the same maximum output allowance in this comparison. Sol has additional context capacity, but its long-prompt tier changes the bill. Send the relevant files and documents instead of filling the window simply because space is available. For a repository, include dependencies and interfaces the change touches; for document analysis, include the passages needed to check the answer.
Repeated reference material
Cache-read pricing can matter when requests reuse the same instructions or source files. The cache must actually be eligible and reused for the lower read rate to apply. Updating the material, writing a cache or changing the request arrangement can affect the economics. Compare uncached and cached work separately so a favorable cached rate does not conceal the cost of the first request or frequent refreshes.
When switching is worth the work
If a GPT integration already handles your tools and schemas reliably, a move to Opus needs a measurable benefit on your tasks. If a Claude workflow already works, Sol’s lower base rates give a reason to examine the budget, but still require checking responses. Keep the model identifier configurable and retain a way to return to the previous choice. A migration includes error handling and output validation, not only a new model name.
Common questions
Is GPT-6.1 Sol cheaper in every case?
Its base rates are lower in this comparison. Total task cost also depends on long-prompt tiers, output length, reasoning, cache use and repeated calls.
Which is better for coding?
The specifications do not establish a universal coding winner. Compare completed changes against your project’s acceptance checks and include the effort needed to review or repair them.