GPT-6 Sol vs Luna vs Astra: Which OpenAI Model Should You Use?

GPT-6 Sol vs Luna vs Astra comes down to how hard the job is and how often you run it. Use GPT-6 Luna for high-volume, simple work such as extraction, tagging and summaries. Use GPT-6 Sol as the default for most serious writing, coding and agent work, and save GPT-6 Astra for the hardest reasoning, planning and review.

The price gap is wide: Sol costs 20 times Luna per token, and Astra five times Sol. Astra arrived as a limited preview on 3 September 2026 (see our GPT-6 Astra guide), and OpenAI added Sol and Luna on 22 September.

Volatile facts below are correct as of 24 September 2026. For what changed from GPT-5.6, read GPT-6 Sol and Luna explained.

How do GPT-6 Sol vs Luna vs Astra compare on price and specs?

All three share a 1,050,000-token context window, a 128,000-token maximum output, and text and image input, so the real differences are price, reasoning ceiling and effort settings. Luna costs $0.10 in and $0.50 out per million tokens, Sol $2 and $10, and Astra $10 and $50, as of 24 September 2026.

Past 272K input tokens, OpenAI bills the whole request, not just the excess, at twice the input rate and 1.5 times the output rate, per its API pricing documentation. Batch and Flex halve Sol and Luna prices; Fast mode doubles them. Artificial Analysis lists Sol’s context at 872K, below OpenAI’s figure.

How much does one job cost on Luna, Sol and Astra?

A typical job of 20,000 input tokens and 5,000 output tokens costs about $0.0045 on Luna, $0.09 on Sol and $0.45 on Astra at standard rates. Run it 10,000 times a month and the bill is roughly $45, $900 or $4,500. At volume, model choice is the biggest cost lever you have.

A 300,000-token document with a 10,000-token answer crosses the 272K line, so Sol charges $1.35 instead of $0.70. The same job costs about $0.07 on Luna and $6.75 on Astra.

Which is smartest on benchmarks: GPT-6 Sol vs Luna vs Astra?

Astra leads every benchmark OpenAI published for the three, but the gaps vary a lot. On the independent Artificial Analysis Intelligence Index, Astra scores 53, Sol 48 and Luna 37 at maximum effort. Sol sits close to Astra on agent tasks, while Astra pulls well ahead on computer use.

Vendor rows come from Vellum’s breakdown of OpenAI’s Sol and Luna benchmarks and a Kingy AI compilation; OpenAI’s page did not print the FrontierCode or Luna AutomationBench scores. The index row is from the Artificial Analysis model leaderboard (v4.3.2). Our GPT-6 Astra benchmarks explainer covers Astra’s headline results.

Does raising reasoning effort close the gap?

Partly. On Artificial Analysis’s effort ladder, Luna at max (37) beats Sol at low (34), and Astra at low (46) beats Sol at xhigh (44). Sol at max (48) passes Astra at low but not at medium (50). Astra gains little near the top: 51 at high, 52 at xhigh and 53 at max.

Effort costs time, though: Artificial Analysis measured about 143 seconds to first token for Sol at max.

GPT-6 Luna vs Sol: is Sol worth 20 times the price?

For bulk work with clear rules, usually not. Luna lands within a few points of Sol on computer use and bug fixing at a twentieth of the per-token price. Sol earns its premium on multi-step automation, harder code and tasks that need judgement, where Luna’s scores fall away sharply.

On OpenAI-reported numbers, Luna scores 58.1% to Sol’s 60.5% on OSWorld offline and 66.6% to 68.8% on DeepSWE bug fixing, but only 20.7% to Sol’s 33.2% on AutomationBench.

The real saving is smaller than the sticker suggests: Artificial Analysis found Luna uses about 51,000 tokens per index task against Sol’s 31,000, so cost per task is $0.07 versus $1.06, a 15-fold gap. It also measured a higher hallucination rate for Luna (77%) than Sol (60%), so build a fact check into any Luna pipeline.

GPT-6 Sol vs Astra: when is Astra worth five times more?

When failure is expensive. Astra leads Sol by five points on the Artificial Analysis index and by 12 points on OpenAI’s offline OSWorld test, so it suits computer-use agents, hard maths, science and security reasoning, and final review. For everyday coding and writing, Sol gets close for a fifth of the price.

On Agents’ Last Exam the gap is three points, and in its GPT-6 Sol and Luna announcement, OpenAI says Sol “makes about half as many mistakes as its predecessor, approaching Astra-level reliability at much lower cost.” Moving work up? Read how to prompt GPT-6 Astra first.

Which ChatGPT plan has GPT-6 Astra, Sol and Luna?

Plus is the cheapest plan with all three, but only in ChatGPT Work and Codex, not Chat. Free and Go users get Luna in the desktop app only. Pro, Business and Enterprise add GPT-6 Pro, an Astra-powered model, in Chat. Enterprise admins must switch the new models on before members can pick them.

The Plus Work lineup comes from TechRadar’s explainer on ChatGPT Chat vs Work, and GPT-6 Pro limits from OpenAI’s Help Center article on GPT-6 Pro. Plan prices come from third-party trackers, so confirm them at checkout. For a walkthrough, see how to access GPT-6 Astra in ChatGPT.

Why can’t I see GPT-6 Sol in ChatGPT Chat?

Because OpenAI released Sol and Luna for ChatGPT Work and Codex only. Chat, meant for quick everyday questions, still runs GPT-5.6 Sol on Plus and GPT-5.6 Luna on Free and Go. Use the switch at the top of the screen to move to Work, which is built for longer, multi-step tasks and finished deliverables.

Read more: ChatGPT Pricing 2026

Enterprise admins must enable each model first, and Work itself needs Enterprise Key Management on Enterprise and Edu workspaces.

Which GPT-6 model should I use for each task?

Match the model to the cost of a wrong answer. Pick Luna for repeatable jobs with clear rules and an easy check, Sol for work that needs judgement or repository context, and Astra for hard, multi-step problems where one failure costs more than the tokens. The table below is our read of the published results.

How should you route work across Luna, Sol and Astra?

Run Sol as the default, push bulk subtasks down to Luna, and send plans and final reviews up to Astra. Because all three share one API and context window, a router can switch models per step. Start with the cheapest model that passes your acceptance check, and escalate only when it fails.

A user in the OpenAI Developer Community launch thread described a similar chain: Sol at high effort orchestrating, Luna as the worker, Astra re-working failures. Keep a per-model effort map, since Astra has no “none” setting.

Training teams can apply this too; see our GPT-6 Sol guide for learning and development teams.

How do Sol, Luna and Astra differ on safety?

In OpenAI-reported tests, Luna and Astra followed injected instructions 0% of the time, while Sol did so 11.3% of the time, down from 51.9% for its predecessor. That makes Sol the model to sandbox most carefully when an agent reads untrusted web pages, emails or files.

Other OpenAI-reported figures are mixed: Sol worked around explicit denials 64.4% of the time against Luna’s 42.4%, but its coding-deception rate (1.3%) was lower than Luna’s (2.8%). OpenAI says these tests deliberately probe challenging situations rather than typical use.

What are people saying about the GPT-6 lineup?

Reactions praise the price and criticise the naming. Reviewers welcome Sol and Luna as cheap, capable workhorses, but many users find the Astra, Sol, Luna and 5.6-versus-6 labels confusing, the Chat versus Work split hard to follow, and the extra cost of maximum effort hard to justify on some tasks.

In a MacRumors Forums thread on the launch, one commenter asked: “Is there anyone in the valley that can name things?” TechRadar’s Graham Barlow put it bluntly: “If that sentence makes absolutely no sense to you, I don’t blame you.”

There is praise too. A MacRumors commenter wrote that “GPT-6 Sol on High is much more efficient than Astra was on Light”. Yet Astra at low (46) still outscores Sol at high (43) on Artificial Analysis’s index, so the gain is cost, not score.

Critics note the ceiling barely moved: per Artificial Analysis, Sol gained one index point over GPT-5.6 Sol and Luna none. On diminishing returns, one Developer Community user said Sol at max was “making no headway in the problems and outputting worse for the increased cost.”

Conclusion

Default to Sol, drop to Luna wherever you can verify output cheaply, and reserve Astra for calls where one mistake costs more than the tokens. Price your three biggest workloads with the 20K-in, 5K-out example above, then check which cross 272K.

Test your real prompts on Luna at high effort and Sol at medium before paying for Astra. If Anthropic’s new model is also on your shortlist, our GPT-6 Sol vs Claude Opus 5.5 comparison covers that decision.