6.1 Sol vs 6 Astra: Pricing, Coding & Which to Choose
Compare 6.1 Sol vs 6 Astra on API pricing, coding benchmarks and task costs. Use worked examples to decide when Astra is worth paying more for your workload.
6.1 Sol vs 6 Astra is a choice between a lower API bill and paying more for demanding work. GPT-6.1 Sol costs one-fifth as much per standard input or output token; GPT-6 Astra remains OpenAI’s highest-capability option. Start by testing Sol on routine production tasks, then compare Astra where failed attempts or human corrections are expensive. OpenAI model comparison.
The names can be misleading: the higher version number does not make Sol the replacement for Astra. These are different model tiers. This guide compares GPT-6.1 Sol (gpt-6.1-sol) and GPT-6 Astra (gpt-6-astra), with API budgets, public coding results, and a practical way to decide when an upgrade pays for itself.
Checked October 10, 2026. Kavello has not run a private head-to-head benchmark. Published results are attributed below; budget and routing examples are our calculations, not measured API runs.
6.1 Sol vs 6 Astra at a glance
| Specification | GPT-6.1 Sol | GPT-6 Astra |
|---|---|---|
| Context window | 1,050,000 tokens | 1,050,000 tokens |
| Maximum output | 128,000 tokens | 128,000 tokens |
| Native inputs | Text and images | Text and images |
| Native output | Text | Text |
| Reasoning effort | low, medium, high, xhigh, max | low, medium, high, xhigh, max |
Sources: Sol specifications and Astra specifications. Tool support is separate from native output: access to an image-generation tool does not make the underlying model a native image-output model.
The shared context limit is a capacity limit. It does not establish equal accuracy when extracting one detail from a large collection of documents. Include long-input tasks in your own evaluation if that is how you plan to use either model.
API pricing: where the 5× gap applies
These are Standard USD prices per one million tokens for prompts with up to 272K input tokens.
| Token category | GPT-6.1 Sol | GPT-6 Astra |
|---|---|---|
| Uncached input | $2.00 | $10.00 |
| Cached input | $0.10 | $1.00 |
| Cache write | $2.50 | $12.50 |
| Output | $10.00 | $50.00 |
The input and output rates have a 5× gap; cached input has a 10× gap. A mixed request therefore does not automatically cost exactly one-fifth as much on Sol. Different output lengths, cache usage and retries can change the ratio. Official API pricing.
For prompts above 272K input tokens, both models charge double their input and cache rates and 1.5× their output rates for the whole request, not just the excess. That makes uncached input/output $4/$15 for Sol and $20/$75 for Astra per million tokens. Sol long-context rates, Astra long-context rates.
Two budgets you can reproduce
Assume identical billed token counts, Standard processing, no caching and no paid tools. Include all billed output tokens in the calculation. These examples exclude retries, regional premiums and taxes.
| Request | Sol | Astra |
|---|---|---|
| 20,000 input + 2,000 output | $0.06 | $0.30 |
| 300,000 input + 10,000 output | $1.35 | $6.75 |
For the first row, Sol costs 0.02 × $2 + 0.002 × $10 = $0.06. At 10,000 identical requests, the token budgets are $600 and $3,000.
For the second row, the long-input rule applies. Sol costs 0.3 × $4 + 0.01 × $15 = $1.35; Astra costs 0.3 × $20 + 0.01 × $75 = $6.75.
Use this calculation to set a starting budget. Use recorded usage to decide whether the two models consume similar numbers of tokens on your workload.
Coding benchmarks: the effort setting changes the answer
Cognition’s FrontierCode 1.1 Main results show why one winner label is unhelpful. Each cell below gives the weighted score followed by mean USD cost per rollout. Both entries use the Codex harness in the published dataset.
| Effort | Sol: score / cost | Astra: score / cost |
|---|---|---|
| low | 45.46% / $0.246 | 45.27% / $1.700 |
| medium | 50.23% / $0.361 | 48.83% / $2.426 |
| high | 47.98% / $0.498 | 50.94% / $3.015 |
| xhigh | 49.29% / $0.560 | 50.62% / $3.276 |
| max | 47.57% / $0.848 | 53.26% / $4.586 |
Source: Cognition’s original data, v1_1, main, checked October 10, 2026. Scores and costs are rounded for display. The weighted score is distinct from pass rate. It assesses rubric performance, including correctness, tests and repository conventions. FrontierCode methodology.
Sol is slightly ahead at low and medium in this snapshot; Astra leads at high, xhigh and max. The low-effort gap is only 0.19 percentage points. These point estimates alone do not establish statistical significance. The table also shows that increasing effort does not guarantee a higher score on every benchmark.
OpenAI’s own launch evaluation describes Sol as matching Astra on DeepSWE v1.1 at roughly one-fifth the cost, while recommending Astra for its hardest scientific-research tasks. That is a vendor-reported result on other tests, not a contradiction of the FrontierCode table and not proof of parity across all work. OpenAI’s Sol evaluation.
Which model is faster?
There is no defensible universal speed winner in the evidence above. The FrontierCode snapshot includes task durations for Sol but no corresponding Astra duration field, so it cannot support a direct time comparison.
For an interactive product, record time to first useful output and time to an accepted answer separately. A fast first response can still require two correction rounds. For an automated job, measure total wall-clock time, including tool calls and retries.
Keep the processing tier, effort, tools and input size fixed during the comparison. Then run a second experiment with the settings you would actually deploy. The first isolates more variables; the second answers your business question.
When is Astra worth the extra cost?
Consider a request with the $0.06 versus $0.30 budget above. Astra adds $0.24 in model cost. If you value review time at $60 an hour, saving more than 14.4 seconds of expected review time per request offsets that difference, before other costs.
This is a break-even calculation, not a claim that Astra saves those seconds. Measure the actual correction time and acceptance rate. A harder task with larger outputs will have a different threshold.
A useful test plan is to divide work by its acceptance criteria:
| Workload | Suggested starting experiment |
|---|---|
| Routine changes with clear tests | Start with Sol; measure first-pass acceptance |
| Ambiguous changes across several modules | Compare both on architecture, regressions and review time |
| Document extraction | Score missed details and unsupported answers against a reference set |
| Difficult research or analysis | Test Astra alongside Sol; verify sources and intermediate results |
These are evaluation recommendations, not promises of model performance.
Test a Sol-first escalation policy
Under the same illustrative token budget, sending every task to Sol and then rerunning 20% with Astra costs 0.06 + 0.20 × 0.30 = $0.12 per incoming task. Sending everything directly to Astra costs $0.30.
That policy only works if you can detect unacceptable results. A passing syntax check may miss a broken requirement. Define the escalation trigger before measuring savings, and include the extra latency and any larger retry prompts. Do not let the model’s confidence alone decide whether its work is correct.
How to compare Sol and Astra on your own tasks
Select representative completed tasks with known acceptance criteria. Include ordinary examples and the failures that consume the most review time. Run both models with the same instructions, repository state, tools and output budget, repeating tasks to observe variability.
For each run, record model ID, effort, processing tier, billed usage, latency, test results, reviewer acceptance and correction time. Calculate total spend divided by accepted outcomes, rather than comparing only the cheapest individual response.
If you use tools through the API, use Responses: OpenAI documents tool calling there for these models, while Chat Completions does not support their tool calling. Reasoning-model guidance.
Comparing vendors as well? Our Opus 5.5 vs Sol 6.1 comparison covers that separate choice. For a smaller-model pricing reference, see Haiku 5.5 API costs.
Frequently asked questions
Does 6.1 mean Sol is better than 6 Astra?
No. Version numbers belong to different tiers here. Evaluate the named model and its settings against your task rather than sorting models by the number in their names.
Is Sol always five times cheaper?
Standard uncached input and output unit prices are one-fifth of Astra’s. The final bill also depends on tokens consumed, cached input, tools, processing tier and retries.
Are these ChatGPT or Codex subscription prices?
No. This article calculates API token costs. It does not calculate subscription allowances or the number of tasks included in a ChatGPT or Codex plan.
Which should I choose first?
Use Sol as the first candidate when volume and cost matter and you have clear acceptance checks. Compare Astra where additional quality could reduce costly review or failed attempts. Keep the choice tied to measured accepted outcomes.