Claude Opus 5.5 vs GPT-6 Sol

What the independent comparison actually shows

Artificial Analysis Intelligence Index v4.3.2 reports 58 for Opus 5.5 with adaptive reasoning, max effort and default fallback, versus 48 for GPT-6 Sol at max effort. Its index combines ten evaluations, including coding, automation, scientific reasoning, and long-context tests. This is evidence for that evaluator and configuration, not proof that Opus wins every repository task. Effort settings and fallback behavior differ, and the scores should not be mixed with provider launch benchmarks.

• Use the same acceptance tests, permitted tools, prompt context, and review standard for a local trial.
• Track completed tasks and corrections, not only benchmark rank or first response quality.

What the API bill can hide

Sol's standard base rates are half of Opus 5.5's for fresh input and output. Both list $0.20 per million cached-input reads, while five-minute cache writes are $2.50 for Sol and $5 for Opus. Sol charges 2x input and cache rates and 1.5x output above 272K input tokens for the full request. Opus does not use that context-size price step in Anthropic's current pricing explanation. Neither list-price comparison settles task cost: reasoning output, turns, cache hits, tool fees, retries, and human repair can outweigh a token-rate advantage.

Tooling and access are different choices

Sol supports text and image input, text output, and OpenAI Responses API tools such as web and file search, hosted shell, apply patch, and computer use. Opus 5.5 supports text and image input, text output, and Anthropic tool use; its adaptive thinking cannot be disabled. Anthropic lists Claude API plus Bedrock, Google Cloud, and Microsoft Foundry routes. OpenAI lists Sol in its API and launched it in ChatGPT Work and Codex for eligible paid plans while Chat rollout remained separate. Confirm the actual endpoint, account, permissions, and tool harness before assuming one product experience transfers to another.

A practical two-model trial

Pick three representative tasks: one bounded feature, one multi-file debugging job, and one long-context review. Run each model at a documented effort setting with identical repository access, tools, instructions, time budget, and acceptance checks. Count accepted outcomes, failed attempts, input and output usage, cache behavior, latency, and review minutes. If Sol passes the bar, its lower standard list rates make it a sensible default; escalate to Opus where its higher capability saves enough rework to justify the actual bill.

• Claire Vo's blind task video in Sources illustrates output review across real workflows; its mixed tasks and tools do not establish a controlled model ranking.