Two frontier models launched on the same day in September 2026, and the pricing comparison reveals that the rate card tells only part of the story. GPT-6 Sol from OpenAI carries a significantly lower price per token than Claude Opus 5.5 from Anthropic. But Anthropic's own data suggests Opus 5.5 needs fewer steps to complete difficult tasks, which can narrow or even reverse the gap in practice. The real question for any team evaluating these models is not what the API costs per million tokens but what a completed, verified task costs.

The Rate Card: Sol Wins on Paper

At equal token volumes, the pricing gap is straightforward. GPT-6 Sol charges $2 per million input tokens, $0.20 per million for cached input, $2.50 per million for cache writes, and $10 per million for output. Claude Opus 5.5 charges $4 per million input, $0.20 per million cache reads, $5 per million for five-minute cache writes, and $20 per million for output. Anthropic also charges an additional $8 per million for one-hour cache writes.

That makes Opus 5.5 twice as expensive as Sol on fresh input, cache writes, and output. Cache reads cost the same for both. Sol also applies a higher rate when a request exceeds 272,000 input tokens, while Opus 5.5 publishes a single standard rate across a 1 million token context window.

Why Cost Per Task Tells a Different Story

An agent's bill is not one prompt multiplied by one rate. The effective cost of a task includes model tokens, cache writes, tool calls, retries, failed runs, and the time a human reviewer spends fixing mistakes. A model that costs twice as much per token but completes a task in half the calls can break even.

Anthropic reports that Opus 5.5 is 20 percent cheaper than its predecessor Opus 5 on standard input and output rates, 60 percent cheaper on cache reads, and roughly 40 percent cheaper on a typical default-settings workload because it also consumes fewer tokens per task. The company is explicit that this figure is a provider estimate based on its own assumptions, not a universal discount that applies to every workload.

A Controlled Test Shows the Gap

A direct comparison on a deterministic test suite is more revealing than either vendor's marketing numbers. Opus 5.5 and GPT-6 Sol both scored 49 out of 49 with zero errors on the same test. But Opus 5.5 cost $0.05422 per correct answer, while Sol cost $0.015922. That is a 3.4 times premium for Opus on these short, machine-graded tasks. Opus used 5,330 prompt tokens and 1,645 completion tokens; Sol used 3,426 and 907. On this kind of workload, Opus did not recover its price difference through efficiency.

The same test did show progress against the previous generation. Opus 5.5 scored 49 out of 49 for $0.05422, while Opus 5 scored 48 out of 49 for $0.09366. That is a 43 percent reduction in cost per correct answer, close to Anthropic's own 40 percent estimate, though it says nothing about how either model performs at repository scale.

Benchmarks and the Limits of Comparisons

Anthropic publishes benchmark results showing Opus 5.5 at 66.4 percent on Terminal-Bench 4.0, 54.4 percent on FrontierCode, and 57.8 percent on CursorBench. GPT-6 Sol scores 37.3 percent, 47.5 percent, and 41.7 percent on the same benchmarks respectively. These figures offer useful directional evidence, but they are not a neutral head-to-head test. Anthropic publishes its own table, effort settings differ by result, and each vendor uses its own harness and cost assumptions.

Anthropic also claims that default-effort Opus 5.5 beats GPT-6 Astra at roughly one-fifth the cost on selected FrontierCode and GDPval comparisons. This applies to specific comparison points, not to every Opus request. At equal token volumes, Opus 5.5's standard input and output prices are 60 percent below Astra's short-context rates, so any larger saving has to come from the task trace itself: fewer tokens, fewer calls, fewer retries, or fewer failures.

Subscription Reality and Migration Risks

The buying route matters as much as the model price. On launch day, the OpenClaw build had not yet listed Opus 5.5 in its Anthropic model catalog, which means users considering that integration should wait for a tested compatibility path. Hermes offers an experimental Claude Subscription DirectSDK plugin that requires version 0.21.4 or newer and a logged-in Claude Code CLI. Its own testing found the metering was equivalent to running Claude through the standard command-line interface, but at about 1.7 times the interactive Claude Code TUI meter, while sending roughly 0.6 times as many tokens on identical coding tasks. Those two effects can offset each other, and only a paired workload test produces a definitive answer.

Opus 5.5 also introduces migration considerations that affect cost. The model always uses adaptive thinking and rejects requests that try to disable it. Forced tool choice is unsupported, thinking blocks are tied to the model and conversation, and one older computer-use tool version is rejected on both Anthropic's API and Google Cloud. A harness that does not handle these changes correctly can trigger retries, lose reasoning state, or fail tool rounds. Each extra loop raises the effective cost, which undermines the reason for choosing a more expensive model in the first place.

Which Model to Pick

GPT-6 Sol is the safer choice when predictable API economics and broad daily throughput matter most. Its rate card is simpler and cheaper at equal volumes. Claude Opus 5.5 makes sense only when a team's own data shows that its higher completion rate or fewer steps actually reduces the cost per accepted result compared to cheaper alternatives.

The practical advice from the pricing analysis is to run a controlled pilot with the same task set, repository, tool list, permissions, cache state, effort level, and stop policy. Record successful tasks, total input, cache usage, output tokens, tool calls, wall time, retries, failures, and human review minutes. Compare dollars per accepted result, not dollars per first attempt. And keep GPT-6 Luna or Claude Sonnet 5 as baseline controls. A premium model should beat them on cost per accepted task, not merely on a launch benchmark.