The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →GPT-6.1 Sol is substantially cheaper than GPT-6 Astra on standard API token rates, but the available evidence does not show that the two models have the same accuracy across tasks. OpenAI lists Sol at one-fifth of Astra’s input and output prices. It also reports near-Astra results on selected evaluations, including a computer-use benchmark. Those are vendor-reported, task-specific comparisons—not a guarantee that Sol will match Astra on your workload.
Is GPT-6.1 Sol as good as GPT-6 Astra?
It depends on the task and the quality bar. OpenAI describes GPT-6.1 Sol as delivering near-Astra performance for complex coding, computer use, and professional work, while recommending that users compare the models on their own tasks. That positioning supports testing Sol as a lower-cost option; it does not establish equal accuracy across all uses. OpenAI’s GPT-6.1 Sol model documentation and its model-selection guidance are the relevant starting points.
For a specific result, OpenAI reports that on the offline set of OSWorld 2.0, Sol came within 2.1 percentage points of Astra at maximum reasoning effort, at roughly one-seventh of Astra’s cost per task. This is a vendor-reported result for that evaluation and setting, not a general accuracy gap or a prediction for every computer-use workflow. The reviewed material does not provide an independent head-to-head test across a representative range of user tasks. OpenAI’s Sol announcement describes the benchmark.
Benchmark results are most useful as a reason to run your own comparison. Use representative prompts, tools, and success criteria from your actual workflow, and judge both whether the answer or action is correct and how much it costs to obtain.
#1 Best Overall
How much cheaper is GPT-6.1 Sol?
OpenAI’s standard short-context API pricing lists Sol at 20% of Astra’s input and output token rates—an 80% reduction, not 18%. These are per-token prices; they do not by themselves predict the total cost of completing a task.
| Model | Input per 1 million tokens | Output per 1 million tokens |
|---|---|---|
| GPT-6.1 Sol | $2 | $10 |
| GPT-6 Astra | $10 | $50 |
Rates are OpenAI’s listed standard short-context API prices, accessed in 2026; check the current API pricing page for the route and pricing mode you plan to use. A model that needs more tokens, retries, or tool calls to finish a job can narrow its apparent price advantage. Conversely, if it completes the same work successfully with comparable usage, lower token rates can translate into meaningful savings.
Rank #2
What do benchmark task costs show?
Task-cost figures are different from per-million-token prices: they estimate the cost of running a particular benchmark under its stated setup. OpenAI reports the following comparisons at maximum reasoning effort:
| Evaluation and setting | GPT-6.1 Sol | GPT-6 Astra | What the figures describe |
|---|---|---|---|
| OSWorld 2.0 offline set, maximum reasoning effort | Roughly one-seventh of Astra’s cost per task | Reference cost | OpenAI’s reported cost-per-task comparison; Sol’s score was within 2.1 percentage points of Astra’s on this set. |
| Terminal-Bench Science 0.1, maximum reasoning effort | $5.47 average task cost | $23.80 average task cost | OpenAI’s reported average task costs for this benchmark setup. |
These are OpenAI-published benchmark results, not independently reproduced measurements or estimates for an arbitrary production workload. The task cost in your application will depend on the prompts, output length, reasoning settings, and any tools or retries involved. See OpenAI’s benchmark and launch details alongside the live API pricing.
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Which model should you use for coding or computer-use tasks?
Start with Sol when cost matters and the task is a good fit
Sol is a sensible candidate for complex coding, computer use, or professional work when the expected quality is acceptable and lower API spend matters. Its benchmark results make it worth evaluating, particularly for repeatable workflows where you can measure success and cost on a representative sample.
Prefer Astra when the highest capability is the priority
If errors are costly, the work is unusually demanding, or Sol fails your evaluation, use Astra for those tasks. You can also route only the cases that need stronger performance to Astra rather than selecting one model for every request.
Compare outcomes, not just price tags
OpenAI’s guidance for Sol is: “Compare it with Astra on your tasks to assess the tradeoff between quality and cost.” A practical comparison should hold the task, instructions, tools, and success criteria constant. Track successful completion, corrections or retries, and total usage cost; a cheaper attempt is not a saving if it requires substantial rework.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are GPT-6.1 Sol’s context and output limits?
OpenAI’s model documentation lists a 1,050,000-token context window, a maximum output of 128,000 tokens, and an April 30, 2026 knowledge cutoff for GPT-6.1 Sol. These are model specifications, not a promise that every application or access route exposes the full limits. Confirm current limits and availability in the live model documentation before designing around them.
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OpenAI documents API access for GPT-6.1 Sol and describes API and other rollout channels for Astra. The available route, pricing, and access can differ by product, account, region, or processing mode. Check the official Sol model page, API pricing, and Astra announcement for current availability in the product you intend to use.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
