GPT-6.1 Sol vs Claude Opus 5.5

GPT-6.1 Sol and Claude Opus 5.5 are both serious candidates for difficult coding work, but they optimize for different decisions. GPT-6.1 Sol is the lower-priced route when you want OpenAI's Responses API, Codex workflow and explicit reasoning controls. Opus 5.5 is a strong choice when a long investigation, ambiguous architecture or expensive correction makes the Anthropic tool loop worth testing.

The published evidence does not establish a universal winner. The vendors report different benchmarks, effort settings and harnesses. Compare the work your team accepts, the retries it pays for and the time a person spends correcting it.

GPT-6.1 Sol vs Opus 5.5: quick answer

Start with GPT-6.1 Sol when your integration already uses OpenAI Responses, Codex or OpenAI's tool schemas, and when lower direct token rates make repeated trials easier to fund. Its API model ID is gpt-6.1-sol, with explicit low through max effort levels and a 1.05M-token context window.

Start with Claude Opus 5.5 when the hard part is deciding what is wrong, tracing a problem across a large repository or avoiding a costly second pass. Its API model ID is claude-opus-5-5; adaptive thinking stays on and the standard API default is medium.

Those are routing hypotheses, not a ranking. A bounded task with strong tests may favor either model. A model that replies quickly can still lose if it needs more repairs.

Core specifications

The same medium label does not mean the same reasoning budget across providers. All prices here are in USD, and billed output includes reasoning or thinking tokens.

The figures are provider documentation, not an OmniaKey invoice. GPT-6.1 Sol's standard rates apply to requests up to 272K input tokens; its long-context tier changes the rate for the whole request. Anthropic's Opus prices above are the standard global direct API rates. Taxes, tools, gateway fees and account limits are separate.

Both models can read images and produce text. Neither row means native image or video generation. Both advertise a roughly one-million-token context, but a larger window does not prove better retrieval or fewer omissions on your repository.

What do the benchmarks actually show?

Published results are useful for choosing tasks to test, but they do not form one shared league table.

OpenAI's GPT-6.1 Sol announcement reports 73.0% on DeepSWE v1.1 at medium with an average recorded task cost of $0.42. GPT-6 Sol scored 56.6% at $0.38 in the same table, while GPT-6 Astra scored 72.8% at $3.08. Higher effort is not automatically better: the same announcement lists 75.2% at high and 71.9% at max for new Sol.

Anthropic's Opus 5.5 release reports 66.4% on Terminal-Bench 4.0 at xhigh, 54.4% on FrontierCode v1.1 Main at max, and 57.8% on CursorBench 4.0 at max. These are provider results with production safeguards enabled; the tool setups and tasks differ from DeepSWE. They do not establish whether Opus or Sol will finish your repository task faster or with fewer corrections.

Use the numbers as a test design. If your work resembles long-horizon repository repair, include that class of task. If it resembles a bounded change with hidden tests, include a bounded task. Keep the benchmark name, effort, harness and failure policy next to every result.

API price: Sol is cheaper per token, but task cost still depends on work

The standard direct rates make GPT-6.1 Sol easier to sample repeatedly. For the same billed token quantities:

The second row assumes a valid cache hit and does not include the earlier cache-write request. GPT's calculation is 0.2 × $0.10 + 0.02 × $2 + 0.01 × $10 = $0.16; Opus is 0.2 × $0.20 + 0.02 × $4 + 0.01 × $20 = $0.32.

This is a rate comparison, not a promise that both models use the same number of tokens. Thinking, tool calls, retries and output length can make the completed task much more expensive. Measure the cost of all attempts divided by the number of accepted results, then record human correction time separately.

GPT-6.1 Sol's cache-write rate is $2.50 per million tokens under the standard tier. Opus 5.5 lists $5 for a 5-minute write and $8 for a one-hour write. A cache hit must appear in the provider usage fields; sending the same prefix again does not prove that it was read from cache.

For GPT-6.1 Sol, more than 272K input tokens doubles the input and cache rates and raises output to 1.5 times the standard rate for the entire request. Do not apply that multiplier only to the excess tokens. Verify the current provider rules before using a long-context estimate for production.

Which model fits which coding task?

GPT-6.1 Sol is a practical first test for:

• OpenAI Responses and Codex based agents;
• repeated coding trials where direct token cost matters;
• image-and-text bug reports that must return a text patch or explanation;
• workflows that need a visible, explicit effort setting;
• teams already using OpenAI request, tracing and tool schemas.

Opus 5.5 is a practical first test for:

• ambiguous failures that cross services or packages;
• long refactors where the diagnosis is harder than the edit;
• architecture work where an incorrect early assumption causes expensive rework;
• teams already invested in Anthropic Messages and Claude Code;
• tasks where reducing human review time can justify a higher token bill.

These are starting points. A clear bug with a reliable test suite can be cheaper and safer on either model. A long context window does not remove the need for citations, targeted file selection and acceptance tests.

Compatibility checks before changing the model ID

GPT-6.1 Sol

1. Reasoning settings: none and minimal are not supported. Move to a supported effort level and remeasure latency and usage.
2. Tools: tool calling requires the Responses API. Chat Completions remains available for requests without tools, but an existing agent may need a real interface migration.
3. Sampling: remove parameters such as temperature and top_p when the selected reasoning mode does not support them. Check what your SDK or gateway adds automatically.

Opus 5.5

1. Adaptive thinking: do not send the old disabled-thinking or manual budget_tokens forms. Use adaptive thinking and output_config.effort.
2. Tool choice: forced tool_choice types any and tool are unsupported. Strict schemas validate arguments; they do not force a call.
3. Thinking blocks: preserve thinking blocks and keep the conversation append-only. Editing earlier history can invalidate those blocks; cross-model reuse follows the provider’s compatibility rules.

Read the GPT-6.1 Sol guide and the Opus 5.5 migration guide before changing a production agent. A model-selection article cannot certify every SDK or gateway.

A fair comparison you can run

Choose tasks before looking at the answers. A small pilot can contain six tasks: two reproduced bugs, two cross-file changes and two evidence-based document or visual questions. Run each model three times from a fresh workspace.

Keep the repository commit, prompt, tools, permissions, region, timeout and retry budget fixed. Start both models at explicit medium; report tuned-effort runs separately. Record accepted results, failures, retries, tool errors, refusal or fallback behavior, billed tokens, elapsed time and human corrections. Count failed attempts in cost. If no run passes, report “no accepted result” rather than a zero cost per success.

The pilot cannot establish a universal success rate. It can answer a useful local question: which model reaches your acceptance bar with less total work and a bill your team accepts?

Frequently asked questions

Is GPT-6.1 Sol better than Claude Opus 5.5 for coding?

The official sources reviewed here do not provide a matched GPT-6.1 Sol versus Opus 5.5 test. Start with Sol for OpenAI-native workflows and lower direct token rates; test Opus for difficult investigation and quality-sensitive work. Keep the same acceptance checks.

Is GPT-6.1 Sol half the price of Opus 5.5?

Its listed standard uncached input and output rates are half Opus 5.5's. A completed task can use different thinking, tool and retry tokens, so the total bill is not guaranteed to be half.

Do both models support a one-million-token context?

They do, with GPT-6.1 Sol listing 1.05M and Opus 5.5 listing 1M. Capacity is not a quality guarantee, and each request still has output, rate and account limits.

Which one should I use in OmniaKey?

Check the current model catalog for the exact route and quote. Provider release status does not establish that every gateway channel is enabled. Test the route with your own prompts and acceptance checks.

Sources and related reading

• OpenAI GPT-6.1 Sol model page
• OpenAI: introducing GPT-6.1 Sol
• OpenAI API pricing
• Anthropic Opus 5.5 specifications
• Anthropic: introducing Claude Opus 5.5
• Anthropic API pricing

For model-specific background, read our GPT-6.1 Sol review and Claude Opus 5.5 review.

Check the GPT-6.1 Sol model page and Claude Opus 5.5 model page for live OmniaKey routes. If you are choosing within Claude instead, use the Opus 5.5 vs Sonnet 5.5 comparison.