GPT-6.1 Sol vs Claude Opus 5.5: benchmarks and cost

Claude Opus 5.5 scores higher in Artificial Analysis's Terminal-Bench 4.0 and FrontierCode 2.0 at Max effort. GPT-6.1 Sol has the lower standard token tariff on API Stock: $0.054 versus $0.144 for an assumed 20,000-input, 2,000-output workload without cache. Use the published scores to shortlist a model, then measure the cost of an accepted fix.

What do the published benchmarks show?

Artificial Analysis provides the results below, retrieved 5 November 2026. The configurations are GPT-6.1 Sol: Max and Claude Opus 5.5: Max, Default Fallback. The fallback label belongs to the evaluated configuration; do not assume our API reproduces it.

Terminal-Bench 4.0 runs 66 terminal tasks through mini-swe-agent, averaging pass@1 over three repeats; all verifier tests must pass. FrontierCode 2.0 uses 132 tasks, three attempts and Stirrup, with held-out tests and an LLM-judged rubric. Their percentages describe different tasks and scoring rules.

Intelligence Index v5.0.1 combines nine evaluations: index points are not percentage accuracy. Max is each model's maximum effort setting, not an equal compute budget. We preserve the source's displayed precision. The comparison page does not supply row-level confidence intervals or exact run timestamps; the date above is our retrieval date.

GDP.pdf tests answers grounded in long PDFs: 100 tasks, five repeats and LLM rubric grading; All-pass requires every rubric check. It measures document work separately from terminal pass@1.

Opus scores higher on the two coding evaluations; Sol scores higher on GDP.pdf. That makes Opus worth testing for repository work, while document workflows deserve a separate trial. These results do not measure Russian-language quality or latency on API Stock.

What does Agent Arena add?

The Agent Arena leaderboard dated 4 November 2026 offers a separate view of real sessions:

Arena's method estimates model-selection effects from session signals using randomized components. Net Improvement is a treatment-effect estimate, not a task-completion rate. It cannot be combined with Terminal-Bench percentages.

Opus has the higher estimate in this session sample; the size of that gap cannot be projected directly onto your tasks. These are external evaluations, not measurements of API Stock or its routing. The dated evidence snapshot preserves the numbers and conditions.

What does the same token workload cost?

The live catalog, checked 5 November 2026, lists the following API Stock USD per million tokens. These service tariffs are separate from the benchmark operators' native API costs.

For 20,000 uncached input and 2,000 billed output tokens, Sol calculates to 0.02 × 1.80 + 0.002 × 9 = $0.054; Opus to 0.02 × 4.80 + 0.002 × 24 = $0.144. One thousand equal-token requests therefore cost $54 or $144. Real models need not consume equal tokens, including billed reasoning.

Three paid Sol attempts at that assumed size cost $0.162, exceeding one Opus attempt. This is a budget illustration; we did not measure either model's number of attempts. Record retries and rejected patches before deciding that the lower token rate makes a finished fix cheaper.

What happens with long prompts or cache?

Sol's catalog tariff changes above 272,000 input tokens to $3.60 input and $15.75 output per million. An assumed 300,000-input, 10,000-output request calculates to $1.2375 without cache; Opus calculates to $1.68 at its listed rates. The catalog lists no separate Opus long-context tariff in this snapshot. That does not guarantee every large payload will be accepted.

Cache reads and writes are separate billing categories. Use actual usage rather than treating repeated text as a guaranteed hit. The dated price snapshot retains the rates; check current pricing before a batch.

Can both models use the same API client?

Both Sol and Opus 5.5 list OpenAI compatibility in this catalog. Opus also lists Anthropic Messages; Sol lists OpenAI only.

The OpenAI protocol guide describes the shared request contract. These minimal examples were schema-checked, not run as paid benchmark trials. They do not recreate the external Max configuration or its agent harness.

curl https://api.api-stock.com/api/v1/chat/completions   -H "Authorization: Bearer $API_KEY"   -H "Content-Type: application/json"   -d '{"model":"gpt-6.1-sol","messages":[{"role":"user","content":"Suggest a minimal test for a payment callback received twice."}],"max_tokens":1024}'

curl https://api.api-stock.com/api/v1/chat/completions   -H "Authorization: Bearer $API_KEY"   -H "Content-Type: application/json"   -d '{"model":"claude-opus-5-5","messages":[{"role":"user","content":"Suggest a minimal test for a payment callback received twice."}],"max_tokens":1024}'

An API response supplies generated text and usage. To compare fixes, give both models the same failing test and repository context, run the proposed patch, and count every paid attempt. The developers' Sol documentation and Opus announcement describe their products; native hosted tools are not automatically part of this endpoint.

FAQ

Which model should I try first for coding?

Opus leads the two cited coding evaluations. Try it for difficult fixes; include Sol when the lower token tariff matters, and judge both on passing tests in your project.

Is Sol always cheaper per finished task?

Its standard rates are lower. Extra tokens, retries, cache usage and Sol's long-context tariff can change the total.

Are the benchmark results measured on this service?

They are published third-party results with named configurations and dated sources. They do not establish our endpoint latency or reproduce its routing.

Can I switch from Sol to Opus in Chat Completions?

Both exact model IDs support that protocol in the dated catalog. Check the response and current limits; an Anthropic Messages integration uses a different request contract.