Claude Opus 5.5 vs. GPT-6 Sol: Cost per correct task, not price per token

Now I have all the source facts. Here is the article:

Anthropic and OpenAI both shipped new flagship models on September 22, 2026. Anthropic released Claude Opus 5.5, a coding-focused model with adaptive thinking built in and a 1M-token context window. OpenAI launched GPT-6 Sol on the same day, positioning it for complex coding and agentic workflows with a 1.05M-token context window. Both carry rate cards that look dramatically different at first glance, and both claim serious coding credentials — yet neither number printed on a pricing page tells a builder what a finished task will actually cost.

This piece cuts through the rate-card comparison to the number that matters: dollars per accepted result. It is for engineers running coding agents, teams choosing between API pricing tiers, and anyone sizing up the real invoice before committing a daily workload. The comparison draws on published rates from Anthropic and OpenAI, a 49-task deterministic route-check run by AI Pricing Guru, and documented integration details for the OpenClaw and Hermes subscription routes.

The test: Claude Opus 5.5 vs GPT-6 Sol

AI Pricing Guru ran both models through the same 49-task deterministic suite to establish a controlled, same-prompt baseline. The tasks were machine-graded, the route was fixed, and the token counts were recorded at the prompt and completion level for each model. Claude Opus 5.5 used 5,330 prompt tokens and 1,645 completion tokens to finish the suite. GPT-6 Sol used 3,426 prompt tokens and 907 completion tokens on identical inputs.

That token gap matters because both models charge more for output than for input. Opus 5.5 is priced at $4 per 1M input tokens and $20 per 1M output tokens. GPT-6 Sol sits at $2 input and $10 output per 1M tokens — exactly half on both dimensions for standard short-context work. Both models charge $0.20 per 1M for cached input reads, which is the one rate where they are equal. Opus 5.5 charges $5 per 1M for five-minute cache writes and $8 per 1M for one-hour cache writes. Sol charges $2.50 per 1M for cache writes.

How Claude Opus 5.5 and GPT-6 Sol each did

Both models scored 49/49 on the suite with zero errors. On accuracy alone, neither model distinguished itself from the other on this particular task set. The divergence appeared entirely in cost: Opus 5.5 cost $0.05422 to complete all 49 tasks, while GPT-6 Sol cost $0.015922. That gap is 3.4 times, in Sol's favor, on a narrow but controlled benchmark.

Opus 5.5 also compared favorably to its predecessor within the Anthropic family. Opus 5 scored 48/49 on the same suite and cost $0.09366. Opus 5.5 therefore improved accuracy by one task while cutting cost by 43%, close to Anthropic's reported 40% typical-workload saving. Anthropic also claims Opus 5.5 scores 66.4% on Terminal-Bench 4.0 and 54.4% on FrontierCode, versus 37.3% and 47.5% for GPT-6 Sol, and 57.8% on CursorBench versus Sol's 41.7%. Those figures come from Anthropic's own launch table and use effort settings and harnesses that differ across results; they are useful for shortlisting but not a neutral third-party head-to-head.

Claude Opus 5.5 vs GPT-6 Sol, side by side

GPT-6 Sol adds a long-context surcharge when requests exceed 272,000 input tokens, raising input to $4 per 1M and cached input to $0.40 per 1M; Opus 5.5 publishes a single rate regardless of context length.

See the Claude Opus 5.5 vs GPT-6 Sol output

The route-check result is the most concrete available data point. On 49 machine-graded tasks: both models returned the same score, but Opus 5.5 generated nearly 56% more prompt tokens and 81% more completion tokens than Sol did. At equal token prices that gap would already favor Sol; at Opus 5.5's 2× rate card it becomes a 3.4× cost difference. Anthropic's own cost-per-task argument runs in the other direction: it claims Opus 5.5 is about 40% cheaper per task than Opus 5 because it tends to use fewer tokens, fewer retries, and fewer tool calls on harder tasks than its predecessor did.

Neither company's framing settles the question for a builder's actual repository. Anthropic's claim that Opus 5.5 beats GPT-6 Astra at roughly one-fifth of the cost on selected FrontierCode and GDPval comparisons is configuration-specific and does not apply uniformly across workloads. The principle that total agent bill equals model tokens plus cache writes plus tool fees plus retries plus failed runs plus reviewer time is the right framework. If Opus 5.5 reduces that sum in your harness, its rate premium can disappear or invert. If both models finish in the same number of calls and tokens, as they did on the 49-task suite, Sol keeps the advantage.

The verdict on Claude Opus 5.5 vs GPT-6 Sol

There is one more layer that affects cost before any token is sent: the subscription and tooling route. The OpenClaw build checked on September 22 did not yet list Opus 5.5 in its Anthropic model catalog; users can run it through the Claude CLI using claude -p, which consumes the signed-in subscription's Agent SDK limits, but a missing catalog entry and the model's breaking changes around adaptive thinking and tool-call handling mean that path should be validated before production use. Hermes 0.21.4 or newer adds an experimental Claude Subscription DirectSDK plugin that lists claude-opus-5-5[1m]; its own test found that Hermes sent about 0.6× as many tokens as native Claude Code on identical tasks, while metering ran at about 1.7× the interactive Claude Code TUI rate, meaning the two effects can roughly offset each other.

GPT-6 Sol is the safer rate-card choice for teams that need predictable per-token economics, high daily throughput, and a path with no migration surprises. Opus 5.5 is the rational choice only when your own traces show it completing difficult tasks in substantially fewer calls, tokens, or retries than Sol does — enough to overcome its 2× rate. A controlled pilot with the same task set, repository commit, tool list, cache state, effort setting, and stop policy, with costs recorded per accepted result rather than per first attempt, is the only way to know which model wins on your workload.