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GPT Astra vs Sol vs Claude Opus 5.5: what your money buys

Opus leads the independent Index; Sol has the lowest measured task cost. Compare Sol, Astra, Opus and Fable, then check API costs and subscription quotas separately.

EiliyaSeptember 30, 20267 min read

Claude Opus 5.5 leads Artificial Analysis's Intelligence Index, but GPT-6.1 Sol has the lowest measured cost per Index task. In GPT Astra vs Sol, Astra buys you a slightly higher score at a much higher task cost.

The September 30 evidence gives each model a place on your shortlist. Keep your buying question straight: price per million tokens, cost to finish a benchmark task and subscription allowance are three different comparisons.

GPT Astra vs Sol: the independent numbers

This Artificial Analysis snapshot was checked on September 30, 2026. The speed column reports the evaluator's output-generation readings, separate from OpenAI's Ultrafast claims.

Model and evaluated configuration	Intelligence Index	Cost per Index task	Output tokens per second
GPT-6.1 Sol, max	52	$0.72	69.3
GPT-6 Astra, max	53	$3.26	57.0
Claude Opus 5.5, max with fallback	58	$5.98	92.2
Claude Fable 5.1, max with fallback	53	$7.63	67.9

Source pages: Sol, Astra, Opus and Fable. Speed measurements can change as the evaluator collects new observations.

Opus leads Sol by six points; Astra and Fable each lead it by one. The Index combines scores rather than reporting task-success percentages. Claude's configurations include default fallbacks, so keep that qualification when sharing the table.

Sol's benchmark task cost is about 22% of Astra's and 12% of Opus's. Those ratios describe this evaluation. Your app's savings depend on its task mix, and a different mix can produce a different winner.

I'd shortlist Sol for economical agents and Opus when aggregate capability matters most. On this composite measure, Fable's higher bill gives you little reason to expect better results than Opus.

Opus has cheaper tokens than Astra, but higher task costs

The following prices are US dollars per million tokens. OpenAI's rows use Standard processing with at most 272,000 input tokens. Opus prices come from Anthropic's product page; Fable's input and output rates come from Artificial Analysis.

Model	Ordinary input	Output
GPT-6.1 Sol	$2	$10
GPT-6 Astra	$10	$50
Claude Opus 5.5	$4	$20
Claude Fable 5.1	$10	$50

Sources: OpenAI API pricing, Anthropic's Opus page and the Fable evaluator page above.

Opus costs half as much as Astra per ordinary input or output token, yet its measured Index task cost is higher. Longer reasoning, more output and fallback behavior can all change what you pay to finish.

A token-rate table starts the comparison. For a coding job, collect the bill for the accepted patch, including failed attempts. A cheap first reply is less impressive if you spend another session repairing its work.

What the same token budget costs on each model

For an invented workload with caching disabled, assume 20,000 input tokens and 5,000 billable output tokens per request. Sol costs $0.09, Opus costs $0.18, and Astra and Fable each cost $0.45 at the listed rates.

This calculation holds token usage constant and excludes tools, retries, fallback charges, regional premiums and long-context pricing. Real models can use different amounts of reasoning or take different numbers of turns. You can see the rate difference here; forecasting a finished software feature takes more work.

Faster tokens and bigger windows still need a task test

Opus generates output faster than the other models in the table. Your agent can still spend time preparing an answer, calling tools, waiting for tests or producing a long explanation. Tokens per second measure generation throughput, and a fast stream can still take a long time to deliver a usable result.

Time the usable result: a patch you can review with the relevant tests completed, or a checked document. The first paragraph arriving on screen isn't the finish line.

The evaluator describes Astra, Opus and Fable as having roughly one-million-token context windows. OpenAI's Sol specification gives an exact 1,050,000-token window, with a 922,000-token input ceiling and 128,000-token output ceiling.

A context window tells you capacity, not retrieval quality. Before paying to load a large codebase, test whether the model finds the relevant function and handles conflicting instructions correctly. Choosing a smaller context may make the job cheaper and easier to assess.

For OpenAI models, input above 272,000 tokens changes the full request's price. Sol's ordinary input becomes $4 and output becomes $15 per million tokens. Watch that threshold as your agent's history grows: the opening rate doesn't cover every context length.

OpenAI still recommends Astra for the hardest science

OpenAI's Sol launch post reports lower Terminal-Bench Science task costs for Sol than Astra or Opus, while saying Astra achieves the highest score among tested models at 68.1%. It recommends Astra for the hardest scientific research tasks.

OpenAI measures these results in its research environment or API, with competitor evaluations drawn from public reports. Artificial Analysis's Index measures something different. Both can be useful without settling which model wins in your repository or office workflow.

Anthropic's Opus page recommends the model for coding, agents and professional work. Treat that vendor advice as a way to choose tests, then check whether Opus wins them.

A lower API bill can still use more subscription quota

The launch discussion in r/opencodeCLI captures the difficulty of keeping up. Odd_Championship1509 says they are tired of tracking the best OpenAI or Anthropic option on cost. Another participant, Comrade-Porcupine, reports worse results from Sol 6.1 on a large source-base prompt than Astra or Sol 5.6, and says they plan to keep using Astra for design.

That's one user's experience, without a published benchmark set to verify the ranking.

A separate r/ChatGPT comparison by Background-Web-6312 reports that Sol at xhigh consumed more subscription quota for their workload than Opus at medium, despite Sol's lower API task cost. Different effort settings and billing systems limit what you can conclude about quality or general value.

If you're buying a subscription, measure allowance use on your actual workflow. If you're buying API usage, measure dollars per accepted result. Switching between those questions mid-comparison gives you a misleading answer.

Which model should you try first?

Your main constraint	Sensible starting point	What to verify
Repeated work with a tight inference budget	GPT-6.1 Sol	Acceptance rate and retries
Highest score in this independent comparison	Claude Opus 5.5	Whether its extra task cost improves your results
Difficult scientific work	GPT-6 Astra	Its advantage on your research tasks
Existing Fable workflow	Compare Fable with Opus	Whether changing models breaks useful behavior

Use the evidence above to choose your first test. Keep prompts, files, tools and acceptance criteria fixed, try the effort settings you'll actually deploy, and count manual repair time alongside the model bill.

A cheap model that completes your task is a good default. Pay more when another model consistently finishes work the cheaper one can't. Record those cases so you can route tasks on evidence instead of launch-day enthusiasm.

One balance for testing, a markup for your app

AI Pass is an independent, user-funded wallet and multi-model gateway: pay-as-you-go, no required subscription. Its coding tools share one balance across providers, including gpt-6-sol, gpt-6-astra and claude-opus-5-5. GPT-6.1 Sol isn't listed yet, leaving this comparison incomplete. Keep tests consistent and check actual gateway bills before assuming direct-provider prices.

Sign in with ChatGPT makes your app's AI free to the user through their plan and cuts your cost without paying you; AI Pass lets you add a markup and earn on eligible paid usage under the current AI Pass terms. Start today: pick your tool in the docs, paste the brief, and your agent inspects and sets up your project for browser approval.

FAQ

Is GPT-6.1 Sol better than Claude Opus 5.5?

Opus leads the cited Intelligence Index, 58 to 52. Sol has the lower measured task cost. Your workload determines which advantage matters more.

Is GPT-6 Astra worth more than Sol?

Test difficult tasks where failures or repair cost you more than inference. Astra's slightly higher composite score alone does not justify using it for every request.

How does Astra compare with Claude Fable 5.1?

Both score 53 in the cited configurations. Astra's measured Index task cost is $3.26 versus Fable's $7.63, with Fable evaluated using default fallback.

Can I infer subscription value from API prices?

No. Subscription allowances have their own accounting. Compare quota use separately from API dollars per accepted task.