GPT-6 Sol vs Astra: Price, Scores and Which Effort to Pick

GPT-6 Sol vs Astra comes down to one ratio and one caveat. The ratio: OpenAI lists GPT-6 Sol at exactly one fifth of GPT-6 Astra's price on every token line, $2 against $10 per million input tokens and $10 against $50 per million output tokens^[1]. The caveat: many answers to this question, including Google's AI Overview, quietly compare GPT-6.1 Sol with Astra, which is a different and newer model^[2].

This article compares GPT-6 Sol (gpt-6-sol, released September 22, 2026) with GPT-6 Astra (gpt-6-astra, released September 3, 2026)^[3]. It covers the price list, the scores OpenAI and Artificial Analysis have published, how the reasoning effort levels line up, and where 6.1 Sol changes the answer.

TL;DR

• Price: GPT-6 Sol costs one fifth of GPT-6 Astra per input, cached input, cache write and output token, on both the standard and long-context tiers^[1].
• Quality at max effort: Astra leads. Artificial Analysis scores Astra 53 and Sol 48 on its Intelligence Index, and 59% against 44% on Terminal-Bench 4.0^[4].
• Quality per dollar: Sol can win. In OpenAI's AutomationBench run, Sol at xhigh scored 33.2% against Astra at low with 30.3%, while Astra cost 3.9 times as much per task^[5].
• Effort levels: Sol accepts none through max; Astra accepts low through max and does not support none^[6].
• 6.1 Sol is the closer rival: GPT-6.1 Sol scores 52 to Astra's 53 on the same index, at a cost per task of $0.72 against $3.26^[4].

GPT-6 Sol vs Astra on OpenAI's price list

Source: OpenAI API pricing, Standard tier, retrieved October 7, 2026^[1].

Batch and Flex processing are half the Standard rates for both models, and Fast mode is double^[1]. The one-to-five ratio holds in every tier. Only Astra has an Ultrafast price row^[1].

A worked example, illustrative only: a month of 100 million uncached input tokens and 20 million output tokens on Standard, all prompts under 272K. On GPT-6 Sol that is 100 × $2 + 20 × $10 = $400. On GPT-6 Astra it is 100 × $10 + 20 × $50 = $2,000. Reasoning tokens bill as output, so the effort level you choose moves the output side of that sum more than anything else.

Per-token price is not cost per task, though. A stronger model can finish in fewer tokens. On Artificial Analysis's benchmark suite at max effort, Sol used about 31,000 output tokens per task and Astra about 27,000, so the per-task gap shrinks to roughly three times: $1.04 for Sol against $3.26 for Astra^[4].

What the published scores say

Two sources have published direct comparisons. They use different harnesses, so do not mix their numbers.

Artificial Analysis, both models at max effort:

Source: Artificial Analysis, retrieved October 7, 2026^[4].

Astra wins most rows, and wins clearly on agentic coding. Sol edges it on SciCode and long-context reasoning, and generates output faster.

OpenAI's AutomationBench, from the GPT-6 Sol launch post:

Source: OpenAI^[5]. AutomationBench tests business workflows across 47 tools.

OpenAI also reports that GPT-6 Sol makes about half as many factual mistakes as GPT-5.6 Sol on its internal evaluation, "approaching Astra-level reliability", and that Astra "remains the world's best model for computer use"^[5]. These are vendor results on OpenAI's own setup.

Matching effort levels between Sol and Astra

The two models do not expose the same effort range. GPT-6 Sol supports none, low, medium (the default), high, xhigh and max. GPT-6 Astra supports low, medium, high, xhigh and max, and does not support none^[6]. That makes cross-effort comparisons the realistic ones, and Artificial Analysis has published two:

Source: Artificial Analysis, retrieved October 7, 2026^[4].

The second row is the interesting one. Sol at max and Astra at low land within two points of each other on the index, and Artificial Analysis's cost to run the full index was almost identical: $1,536 against $1,537^[4]. At that budget the two are a wash. Below it, Sol at high or lower is far cheaper per task than any Astra setting. Above it, Astra at medium and up scores higher than anything Sol reaches.

Latency is not a simple story either. In the high-versus-medium pairing, Astra returned its first token in 5.47 seconds against Sol's 22.47, while Sol produced output faster once it started, 99 tokens per second against 50^[4].

GPT-6 Sol vs Astra is not GPT-6.1 Sol vs Astra

GPT-6.1 Sol, released September 29, 2026, is OpenAI's upgrade to GPT-6 Sol^[3]. OpenAI's launch post says it "nearly matches GPT-6 Astra's intelligence on agentic coding, computer use, and professional work at one-fifth of Astra's standard input and output token prices"^[2]. That is the claim most "Sol is almost Astra" answers repeat, and it belongs to 6.1, not to GPT-6 Sol.

Sources: Artificial Analysis^[4], OpenAI pricing and GPT-6 guide^[1][6].

OpenAI's own numbers point the same way: on DeepSWE v1.1, 6.1 Sol matched Astra at roughly a fifth of the cost and beat GPT-6 Sol's best score by 6.4 points. Astra still had the top score on Terminal-Bench Science, 68.1%, and OpenAI says it "should be used for the most difficult scientific research tasks"^[2].

For a full family overview, including Luna and the missing GPT-6 Terra, see GPT-6 Sol vs Terra vs Luna.

API details that change the choice

• Tools in Chat Completions. On OpenAI's API, GPT-6 Sol supports function calling in Chat Completions only with effort none, and Astra needs the Responses API for tool calling^[6].
• Sampling parameters. Astra does not support custom temperature or top_p values, or logprobs^[3].
• Same limits. Both have a 1,050,000-token context window, 922,000 maximum input tokens and 128,000 maximum output tokens^[7].

On reAPI, both are served through the same Chat Completions endpoint with model ids gpt-6-sol and gpt-6-astra^[8]. Sol accepts all six effort values there; Astra returns 400 for none. On Sol, temperature, top_p, frequency_penalty and presence_penalty return 400, and requests with tools returned tool_calls at medium effort as well as none when measured on September 24, 2026. GPT-6.1 Sol is not on reAPI's model list as of October 7, 2026. Current per-token prices are on the GPT-6 Sol and GPT-6 Astra pages, and GPT-6 Sol pricing covers Sol's cost lines in detail.

FAQ

Is GPT-6 Sol better than Astra?

No, not at equal effort. At max effort Astra scores 53 to Sol's 48 on Artificial Analysis's Intelligence Index and leads on agentic coding^[4]. Sol is better value: it lists at a fifth of Astra's per-token price, and in OpenAI's AutomationBench run Sol at xhigh outscored Astra at low at about a quarter of the cost per task^[5].

GPT-6 Astra medium vs Sol high

Astra at medium scored 50 against Sol at high with 42 on Artificial Analysis's index, and 49% against 26% on Terminal-Bench 4.0. It also cost $1.54 per task against $0.37^[4]. If the quality gap matters for your task, Astra medium is the better choice. If it doesn't, Sol high costs about a quarter as much per task.

GPT-6 Astra low vs Sol high

Astra at low scored 46 and Sol at high scored 42 on Artificial Analysis's index, at $0.82 and $0.37 per task respectively^[4]. Sol at max reached 48 for $1.04 per task, a higher score than Astra low for about a quarter more per task.

GPT-6 Astra vs Sol cost

GPT-6 Astra costs five times as much per token: $10 against $2 per million input tokens and $50 against $10 per million output tokens on OpenAI's Standard tier^[1]. Per task the gap is smaller, about three times at max effort on Artificial Analysis's suite, because Astra used fewer output tokens^[4].

GPT-6 Sol vs 6.1 Sol

GPT-6.1 Sol is the upgraded Sol, released September 29, 2026, at the same $2 input and $10 output with cheaper cached input ($0.10 against $0.20)^[1][3]. It scores 52 against GPT-6 Sol's 48 on Artificial Analysis's index^[4]. It does not support effort none, and it needs the Responses API for tool calling^[6].

GPT-6 Sol: which effort?

Start at the default, medium, and move up only when results fall short. OpenAI's GPT-6 guide maps low to routine extraction and small edits, medium to work that needs judgment, high to difficult debugging and careful review, and suggests testing extra high or max only when high falls short^[9]. If you find yourself at max on Sol, compare the cost per task with Astra at low; Artificial Analysis found them nearly equal^[4].

Choosing between GPT-6 Sol and Astra

Use GPT-6 Sol by default for high-volume coding, analysis and agent work, where its one-fifth price and faster output let you run more attempts. Move a workload to Astra when Sol's max setting still misses, or when the cost of a wrong answer is higher than the token bill, as with hard agentic coding and difficult research. Before settling the GPT-6 Sol vs Astra question, also test GPT-6.1 Sol where your platform offers it, because it closes most of the gap at a similar price to Sol.

References

1. OpenAI. API pricing. Retrieved October 2026 from developers.openai.com/api/docs/pricing
2. OpenAI. Introducing GPT-6.1 Sol. Retrieved October 2026 from openai.com/index/introducing-gpt-6-1-sol
3. OpenAI. API changelog. Retrieved October 2026 from developers.openai.com/api/docs/changelog
4. Artificial Analysis. GPT-6 Sol vs GPT-6 Astra model comparisons (max vs max, high vs medium, max vs low) and GPT-6.1 Sol vs GPT-6 Astra. Retrieved October 2026 from artificialanalysis.ai/models/comparisons/gpt-6-sol-vs-gpt-6-astra
5. OpenAI. Introducing GPT-6 Sol and Luna. Retrieved October 2026 from openai.com/index/introducing-gpt-6-sol-and-luna
6. OpenAI. Using GPT-6 (API guide). Retrieved October 2026 from developers.openai.com/api/docs/guides/latest-model
7. OpenAI. GPT-6 Sol and GPT-6 Astra model pages. Retrieved October 2026 from developers.openai.com/api/docs/models/gpt-6-sol and developers.openai.com/api/docs/models/gpt-6-astra
8. reAPI. GPT-6 Sol and GPT-6 Astra API docs. Retrieved October 2026 from reapi.ai/docs/gpt-6-sol and reapi.ai/docs/gpt-6-astra
9. OpenAI. A model guide for the GPT-6 family. Retrieved October 2026 from openai.com/index/practical-guide-building-gpt-6