6.1 Sol vs 6 Astra

01Pricing

Standard price / 1M tokens

6.1 Sol vs 6 Astra on the Standard card, per million tokens.

Input / 1M tokens

Sol $2.00Astra $10.00

Cached input / 1M

Sol $0.10Astra $1.00

Cache write / 1M

Sol $2.50Astra $12.50

Output / 1M tokens

Sol $10.00Astra $50.00

Sources checked 8 October 2026. Above 272K input tokens, the higher rate applies to the whole request.^{OpenAI pricing, Sol model, Astra model}

Above 272K input tokens

Input above 272K

Sol $4.00Astra $20.00

Cached input above 272K

Sol $0.20Astra $2.00

Cache write above 272K

Sol $5.00Astra $25.00

Output above 272K

Sol $15.00Astra $75.00

Batch and Flex are half of Standard. Fast is double, so Fast short-context rates are $4 / $20 for Sol and $20 / $100 for Astra. Regional processing adds 10% where it applies.^{OpenAI pricing, Sol model, Astra model}

On price, 6.1 Sol vs 6 Astra favors Sol whenever you can check the result and the budget matters. Try Astra when a harder task keeps failing, or when a miss costs more than the tokens.

02Performance

6.1 Sol vs 6 Astra on Artificial Analysis, 8 October 2026. A max-effort score is not a low-effort score. Cost is the weighted cost of an index task. Output speed is tokens per second after the first streamed chunk.^{Artificial Analysis}

Effort

Same effort on both. The scale ends at the higher number.

Intelligence Index

Sol 52Astra 53

GDPval-AA v2.1

Sol 1575Astra 1542

Terminal-Bench 4.0

Sol 56.1%Astra 59.1%

AA-LCR v1.1

Sol 83.0%Astra 80.7%

Cost per task (USD)

Sol $0.72Astra $3.26

Output speed (tok/s)

Sol 56Astra 45

Time to first token (s)

Sol 331.97Astra 376.98

Time per task (s)

Sol 684.27Astra 600.75

Every recorded row

Finance & Accounting

Sol 54Astra 55

Strategy & Ops

Sol 57Astra 57

Legal

Sol 58Astra 59

Healthcare & Medical

Sol 51Astra 52

Engineering

Sol 54Astra 55

Economics

Sol 59Astra 60

AA-Briefcase v1.1

Sol 1564Astra 1569

AutomationBench-AA

Sol 64.9%Astra 68.5%

SciCode

Sol 54.2%Astra 56.5%

Humanity's Last Exam

Sol 52.9%Astra 54.7%

GDP.pdf

Sol 31.0%Astra 31.0%

CritPt

Sol 31.7%Astra 31.7%

AA-Omniscience

Sol 42Astra 43

Cost of a finished task

15 graded runs, total

Sol $2.66Astra $14.77

Terminal-Bench Science, cost / task

Sol $5.47Astra $23.80

At these short-context rates, a 6.1 Sol vs 6 Astra call with 40,000 uncached input tokens and 8,000 output tokens comes to $0.16 on Sol and $0.80 on Astra, before cache writes, tools, or retries. The New Stack’s 15 graded runs were correct on both models. Astra finished CI triage sooner (0:24 vs 0:31). Sol finished the incident log (2:20 vs 2:53) and the resolver spec (7:07 vs 10:06). Combined time was 49:54 vs 1:07:07.^{MyClaw, The New Stack, MarkTechPost}

MarkTechPost’s account of OpenAI’s 6.1 Sol vs 6 Astra comparisons: Sol matches Astra on DeepSWE v1.1 at roughly one-fifth of the cost, and sits within 2.1 points on OSWorld 2.0 offline at about one-seventh the cost per task. On Terminal-Bench Science 0.1, Astra leads at 68.1%.^MarkTechPost

03Differences

Where 6.1 Sol vs 6 Astra share a limit, it is listed once.

Official role

Sol. Lower-cost model for complex coding, computer use, and professional work, described as near Astra.^{Sol model}

Astra. Most capable model for the most demanding work: reasoning, coding, computer use, research, and documents.^{Astra model}

API model ID

Sol. gpt-6.1-sol^{Sol model}

Astra. gpt-6-astra^{Astra model}

Reported release

Sol. 29 September 2026.^{MarkTechPost, The New Stack}

Astra. 3 September 2026.^MarkTechPost

Context and max output

Same on both. 1,050,000 context tokens. 128,000 max output.^{Sol model, Astra model}

Input and output

Same on both. Text in and out. Image input only. No audio or video.^{Sol model, Astra model}

Reasoning effort

Sol. low, medium (default), high, xhigh, max. none and minimal are not supported.^{Sol model}

Astra. low, medium, high, xhigh, max.^{Astra model}

Knowledge cutoff

Same on both. 30 April 2026.^{Sol model, Astra model}

Where it is offered

Sol. OpenAI API, ChatGPT Work, and Codex on Plus, Pro, Business, Enterprise, and Edu. Not in ordinary Chat. Enterprise and Edu need an admin to enable it.^{Sol model, ChatSense, MarkTechPost}

Astra. OpenAI API, ChatGPT, and Codex.^{Astra model, MarkTechPost}

Main limits

Sol. Above 272K input tokens, the whole request uses 2× input and cache rates and 1.5× output. Tool calling is on the Responses API.^{Sol model}

Astra. The same 272K rule applies to the whole request. Listed tools are described for the Responses API.^{Astra model}

The official context window is 1,050,000 tokens. Artificial Analysis lists it as 1M. OpenCode lists 1.1M.^{Sol model, Astra model, Artificial Analysis, OpenCode}

04Which to choose

In 6.1 Sol vs 6 Astra, Sol’s newer version number does not put it above Astra.^{Sol model, Astra model}

Budget, or work you can check

Start with GPT-6.1 Sol.

On the Standard card, 6.1 Sol vs 6 Astra is one-fifth on input and output, and one-tenth on cached input. Artificial Analysis also shows a much lower cost per index task at the same effort setting. The New Stack’s three graded tasks tied on accuracy, with Sol at $2.66 against Astra’s $14.77.

Move a task to Astra when Sol misses the acceptance check, or when a miss is expensive enough that the higher rate could pay for itself.

^{OpenAI pricing, Artificial Analysis, The New Stack}

Coding

For coding in 6.1 Sol vs 6 Astra, start with Sol on fixes, reviews, and agent loops you can test.

On Artificial Analysis, Terminal-Bench 4.0 at max effort is 56.1% for Sol and 59.1% for Astra. OpenAI, as reported by MarkTechPost, has Sol matching Astra on DeepSWE v1.1 at about one-fifth of the token price.

Try Astra on work that has already failed under a checked Sol setup, especially if the failure looks like the harder engineering or computer-use cases in the vendor reports.

^{Artificial Analysis, MarkTechPost, Hugging Face}

Hard reasoning, science, and computer use

Do not treat the one-point index gap as a reason to default to Astra.

At max effort the Artificial Analysis index is 53 for Astra and 52 for Sol. The same snapshot has Sol ahead on GDPval-AA v2.1 (1575 vs 1542) and on AA-LCR v1.1 (83.0% vs 80.7%). MarkTechPost’s relay of OpenAI’s numbers still gives Astra the lead on Terminal-Bench Science 0.1 at 68.1%, and puts Sol within 2.1 points on OSWorld 2.0 offline.

Use Astra first when the job is the sort of science or computer-use task where those reported gaps showed up, then keep Sol if Astra does not reduce failures.

^{Artificial Analysis, MarkTechPost, Astra model}

Speed

There is no standing speed winner.

At max effort, Artificial Analysis reports faster output from Sol (56 vs 45 tokens per second) but a longer time per index task (684.27 vs 600.75 seconds). Time to first token at that setting is several minutes for both. The New Stack saw Astra finish the short CI task sooner, and Sol finish the two longer tasks sooner.

Time a finished, accepted result on your own task. A faster first token, a faster stream, and a faster completed job are different measurements.

^{Artificial Analysis, The New Stack, MyClaw}

Repeated, high-volume work

Price the accepted result, not a single call.

Cached input is $0.10 per million on Sol and $1.00 on Astra, which matters when an agent rereads the same prefix. Batch and Flex are half of Standard, and Fast is double, on both models. Retries and longer reasoning can erase a list price advantage.

Keep Astra for the slice of the queue where Sol’s rejection rate makes the cheaper call the more expensive outcome.

^{OpenAI pricing, MyClaw, Hugging Face}

05Test your workflow

Run 6.1 Sol vs 6 Astra on one of your own tasks. Keep the input, tools, and acceptance check the same for both models.

Pick a task you actually run, including one that has failed before.
Write down the API model ID, reasoning effort, and processing mode.
Compare whether the result passed, the time to an accepted result, the spend, and the edits you still made.
Keep the cheaper model where it passes. Escalate the task type that keeps failing.

Blank record sheet
Task	Model and settings	Met the bar	Total time	Actual spend	Rework

06FAQ

Can one replace the other?

Not as a blanket swap. In 6.1 Sol vs 6 Astra, Sol is the practical first call for work you can verify. Astra is still the model OpenAI describes as its most capable for the most demanding work, and the reported science and computer-use gaps have not disappeared. Replace one with the other only after the same task passes your check.^{Sol model, Astra model, MarkTechPost}

How to test

Why do benchmarks and reports disagree?

They score 6.1 Sol vs 6 Astra on different tasks. Artificial Analysis is a multi-test index at a named effort level, and at max effort Astra leads by one point. The New Stack ran three tasks five times at max effort and graded every run correct. A one-point index lead and a tie on three tasks can both be true.^{Artificial Analysis, The New Stack}

Performance

Is Sol just Astra on weaker hardware?

No. Published comparisons show a different list price and a small gap on Artificial Analysis at the same effort setting. They do not describe Sol as Astra on weaker hardware.^{OpenAI pricing, Artificial Analysis}

Differences

Does the same context window mean the same long-document accuracy?

No. Both official model pages list 1,050,000 tokens of context and 128,000 tokens of maximum output. That is a capacity limit, not a promise that both models use a long prompt equally well. Artificial Analysis displays the window as 1M, and OpenCode displays 1.1M.^{Sol model, Astra model, Artificial Analysis, OpenCode}