GPT-6.1 Sol vs Astra: nearly the same smarts for a fifth of the price

GPT-6.1 Sol vs Astra is the comparison that matters since September 29, 2026, when OpenAI launched GPT-6.1 Sol at its DevDay: it costs one fifth of GPT-6 Astra and nearly ties on coding, but not on everything. The practical rule: Sol by default, and Astra only if your own test shows Sol fails where it matters.

What changed with GPT-6.1 Sol

Sol costs $2 per million input tokens and $10 per million output tokens; reading something already cached costs 10 cents. Astra costs $10 input and $50 output. According to one report, that's the same list price GPT-6 Sol already had since September 22: what's new isn't the price but what it does for that money. It's on the API, and per press reports also in ChatGPT Work and Codex, not yet in the regular chat. One source says OpenAI canceled the Astra 6.1 planned for October, so Astra remains the family's ceiling.

Price per token versus cost per task

Nobody pays for tokens: you pay for finished tasks. On Terminal-Bench Science, a test OpenAI published (per a The Next Web report), each task cost $5.47 with Sol, $23.80 with Astra and $23.21 with Opus 5.5. Across a hundred tasks, that's $547 with Sol against $2,380 with Astra: a $1,833 difference. But that same test shows Astra winning there.

For context: the FinOps Foundation's State of FinOps 2026 report, with 1,192 professionals, found that 73% of organizations exceeded what they had projected to spend on AI last year, and that the average AI budget went from $1.2 million in 2024 to $7 million in 2026.

Where GPT-6.1 Sol ties Astra

• Coding (DeepSWE): an analysis that reproduces the figures reports 75.2% for Sol against 74.1% for Astra. The gap is so small it reads as a tie.
• Computer use (OSWorld): Sol scores 71.4 against Astra's 73.5, two points lower, at about one seventh of the cost per task.
• AutomationBench: OpenAI claims Sol beats Opus 5.5 by two points at a third of the cost.

Where it doesn't: who it isn't for

On Terminal-Bench Science, Sol scores 57% and Astra 68.1%: eleven points, not a tie. Another analysis says Opus 5.5 at max effort beats Sol on AutomationBench (42.5 vs 36.1). Both can be true, because the result depends on the effort level. Sol isn't for people who need the deepest possible reasoning, like scientific research or decisions where an error is very costly. For that, Astra is still the tool.

Caching: the second calculation

Imagine an agent with a 20,000-token manual at the start of every query, 2,000 queries a day: 40 million input tokens. With Astra that's $400; with Sol, $80; and with caching, Sol charges 10 cents per million: about $4. Writing the cache does cost, $2.50 per million. It's an estimate with list prices and requires the start of the prompt to be identical on every call.

Three cases with our own math

These cases are our own analysis, not published results.

• Support bot: with 2,000 conversations a day, about $40 daily with Sol ($1,200 a month) against $6,000 a month with Astra.
• On-screen tasks: at one seventh of the cost, a thousand tasks a month cost about $200 with Sol against $1,400 with Astra, though Sol fails a bit more and a human has to review those failures.
• Router: sending 85 tasks to Sol and the 15 hard ones to Astra costs $160 against $500 all on Astra: a 68% saving.

The objection: what about Sonnet 5.5?

Sonnet 5.5 costs the same, $2 and $10. So price per token no longer breaks the tie: what changes is how many tokens each model spends per task and how many it solves correctly. No outside analysis replaces running your own tasks on both and comparing cost per approved task. See the Sonnet 5.5 vs Opus 5.5 breakdown for the same logic.

What breaks in practice

• It doesn't support fine-tuning or the Live, Realtime, Assistants or embeddings interfaces.
• If your prompt exceeds 272,000 tokens, input is charged at double.
• Safety: one analysis reports Sol skipped warnings in 23.5% of adversarial tests, against 17.4% for Astra. If you give it tools that delete data or move money, put a human in the loop.
• It's not yet in the regular chat.

How to apply it in your business?

Gather 50 real tasks with their correct result, change one line (the model name) to GPT-6.1 Sol, run all 50 and record successes, tokens and time; repeat with Astra and divide total spend by the tasks solved correctly. That's your cost per approved task. The rule: Sol by default; Astra only if your test shows Sol fails where it matters; keep the start of the prompt identical for caching; a human on irreversible actions; and repeat the test whenever the model changes.

For the quick version, read the GPT-6.1 Sol Short breakdown.

Frequently asked questions

How much does GPT-6.1 Sol cost compared to Astra?

Sol costs $2 input and $10 output per million tokens; Astra, $10 and $50.

Is GPT-6.1 Sol better than Astra?

No. It nearly ties on coding and computer use, but on science (Terminal-Bench Science) Astra wins by eleven points.

When should I use Astra?

When your 50-task test shows Sol fails where it matters, or for deep reasoning and decisions where an error is very costly.

Does GPT-6.1 Sol support fine-tuning?

No. Nor the Live, Realtime, Assistants or embeddings interfaces.

Is it in the regular ChatGPT chat?

Not yet, per reports: it's on the API, ChatGPT Work and Codex.

Conclusion

In GPT-6.1 Sol vs Astra, Sol isn't the smartest model, but for most tasks it's the one with the best math. Don't compare price per token: measure cost per approved task with your own work.