GPT-6 Sol vs Claude Opus 5.5: Pricing Per Million Tokens Compared
GPT-6 Sol and Claude Opus 5.5 both launched September 22. Here's how their per-million-token pricing and benchmarks stack up.
What do GPT-6 Sol and Claude Opus 5.5 cost per million tokens?
GPT-6 Sol charges $2 per million input tokens and $10 per million output tokens. Claude Opus 5.5 charges $4 per million input tokens and $20 per million output tokens. Sol is exactly half the price of Opus on both ends of the API. Cached input reads are identical on both models at $0.20 per million tokens, so the caching discount doesn’t change the relative gap. Both models launched on September 22, and both were pitched by their respective labs as cheaper and faster than their predecessors.
TL;DR
- GPT-6 Sol costs half as much as Opus 5.5 on standard API rates: $2/$10 per million input/output tokens versus $4/$20.
- Cached input pricing is identical at $0.20 per million tokens for both models, so caching doesn’t widen or narrow the price gap.
- Opus 5.5 leads on public benchmarks at both medium and max effort settings on artificial analysis’s intelligence index, but the cost per evaluation task is roughly 5.4 times higher than Sol’s.
- Context and output limits are close but not identical: both offer roughly a million tokens of context and 128,000 tokens of standard max output, though Sol’s exact context window is 150,000 and its rates increase past 272,000 input tokens.
- Hands-on app-building tests favored Opus on functionality and detail, particularly on 3D interaction tasks and larger multi-feature app builds, while Sol matched it on several simpler tasks.
- The cheaper model isn’t automatically the cheaper choice once you factor in how much cleanup work a lower-quality output requires.
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How does the pricing structure actually break down?
The headline numbers are straightforward: Sol at $2/$10 per million tokens, Opus at $4/$20. That’s a clean 2x price ratio on both input and output, which lines up with Anthropic’s own claim that Opus 5.5 workloads cost around 40% less than Opus 5 at default settings, and OpenAI’s claim that Sol’s rate is half of GPT 5.6 Sol’s previous promotional pricing. Both companies are moving in the same direction: cheaper access to their frontier-tier models.
There’s a wrinkle on the Sol side worth flagging. Sol’s pricing steps up once input length crosses 272,000 tokens. If you’re running short prompts, this never comes up. If you’re feeding in long documents, large codebases, or extended conversation history, the “$2 per million” figure stops being the whole story. Anyone estimating cost at scale needs to model where their typical request sits relative to that threshold, not just quote the base rate.
Anthropic also flagged a behavior change with Opus 5.5: adaptive thinking is now always on, and the default effort level is “medium.” If you’re migrating from an older Opus version and carrying over an effort setting from a previous config, you may not get the same behavior or the same cost you expected. That’s a small operational detail, but it matters for anyone with existing pipelines tuned to specific effort levels.
What do the context and output limits look like side by side?
Both models offer roughly a million tokens of context, which puts them in the same tier for long-document and long-session work. Standard maximum output is 128,000 tokens on both. The one concrete difference in the transcript is that Sol’s exact context limit is stated as 150,000 tokens, a smaller figure than the “roughly a million” headline, so the precise ceiling depends on which configuration or mode you’re running.
Prompt caching is where OpenAI is pushing a specific improvement with Sol. The company says GPT-6 brings better caching across the board, meaning agents that repeatedly read the same instructions or context can reuse more of that prior work instead of reprocessing it from scratch. For anyone running long coding sessions or agent loops that repeat the same system prompt across dozens or hundreds of calls, that’s a real cost lever, since cached tokens are priced far below fresh input tokens on both platforms.
Is Opus 5.5 worth paying roughly double for?
On public benchmark numbers, yes, at least in terms of raw capability. Using artificial analysis’s intelligence index (version 4.3.2), GPT-6 Sol scores 40 at medium effort while Opus 5.5 scores 51. At max effort, Sol reaches 48 and Opus reaches 58. Opus leads at every setting tested.
The cost-per-task numbers make the tradeoff sharper. Artificial analysis reports a weighted cost of about $0.25 per evaluation task for Sol at medium effort, compared with $1.34 for Opus, roughly 5.4 times the cost. That gap shows up unevenly across individual benchmarks. On terminal-bench 4.0, Opus scores 53% against Sol’s 19%, a large gap on tasks that require working through a terminal. On SciCode, it’s 59% versus 54%, a much smaller difference. On Humanity’s Last Exam, 55% versus 41%. On a tool-use benchmark referred to as “automation bench,” the gap narrows to 61% versus 58%. So Opus wins everywhere in these numbers, but the size of the win varies a lot depending on the task type, which matters if your use case leans toward one of those categories.
How did the two models perform in hands-on testing?
One creator ran both models through a structured benchmark of eight scored tasks (each out of 10, covering functionality, visual quality, and how well the output followed instructions) plus four longer “app build” tests, all at medium effort.
Opus won or tied on every task, finishing 75 out of 80 (93.75%) against Sol’s 66 out of 80 (82.5%), an 11.25 percentage point gap. The biggest split came on a 3D contact lens case task: Opus scored 10/10 with a complete, correctly behaving object, while Sol scored 6/10 because the lid caps clipped through the case geometry instead of behaving like solid objects. A bow-and-arrow game task showed a similar pattern (Opus 9, Sol 6), with Sol’s arrows lacking a curved trajectory and Opus adding extra difficulty mechanics.
But Sol wasn’t uniformly behind. Both models scored full marks on four of the eight tasks, including an SVG illustration test, a combinatorics math problem, a Panda-fact fine-tuning task, and a 3D wristwatch clock. That’s half the benchmark where the cheaper model matched the pricier one exactly.
The gap widened again on the larger app-build tests. A terminal-based movie tracker using the TMDB API came out largely non-functional from Sol (posters failed to load), while Opus produced a working version. A 3D Blu-ray collection browser and a notes app with an embedded coding agent both went to Opus as well, though by smaller margins. A poster-creation app was closer to a tie, with a slight edge to Opus on presentation.
The pattern across these tests: Sol tended to produce a generic, visually similar front end across different apps, and struggled more when a task depended on precise interactive behavior (objects passing through each other, physics-like mechanics, multi-step API integrations). Opus was more consistent at getting the small details right that make an app feel finished rather than half-built.
How should you weigh price against output quality?
The math isn’t just “half the price, mostly comparable results.” If a model’s output needs manual fixing before you can ship it, the token savings can get eaten up by your own time. That’s especially relevant for anything involving 3D interactivity, game mechanics, or multi-feature app builds where small behavioral bugs (like objects clipping through each other) aren’t just cosmetic, they break the thing you asked for.
For high-volume, simpler API tasks, especially anything close to the categories where the benchmark gap was small (SciCode, general tool use, structured data tasks), Sol’s roughly 5x lower cost per evaluation is a legitimate reason to default to it. For front-end-heavy or interaction-heavy work where visual and behavioral polish matters, the benchmark data and hands-on testing both point toward Opus 5.5 delivering more consistently finished results, at roughly double the token cost.
Frequently Asked Questions
What is the exact price difference between GPT-6 Sol and Claude Opus 5.5?
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Sol costs $2 per million input tokens and $10 per million output tokens. Opus 5.5 costs $4 per million input tokens and $20 per million output tokens, exactly double Sol’s rates on both ends.
Does caching change the price comparison?
No. Cached input tokens cost $0.20 per million on both models, so the caching discount is identical in absolute terms and doesn’t shift the relative pricing gap between the two.
Which model scores higher on benchmarks?
Opus 5.5 scores higher across every benchmark cited, including artificial analysis’s intelligence index at both medium and max effort, terminal-bench, SciCode, Humanity’s Last Exam, and a tool-use benchmark. The margin varies by task, from a few points to over 30 percentage points.
Is GPT-6 Sol’s context window the same as Opus 5.5’s?
Both are described as offering roughly a million tokens of context with a 128,000 token standard max output, but Sol’s stated exact context limit is 150,000 tokens, and its pricing increases once input exceeds 272,000 tokens.
Is the cheaper model, Sol, ever the better choice?
Yes, in specific cases. Sol matched Opus with full marks on four out of eight benchmark tasks in hands-on testing, including a fine-tuning task and a 3D clock build, and its benchmark gap on tasks like SciCode and tool-use scoring was relatively small. For high-volume or less interaction-heavy tasks, its lower per-token cost can outweigh the quality gap.
