OpenAI's model lineup changed twice in September 2026, and the result confuses even regular ChatGPT users. GPT-6.1 Sol is now the default serious-work model, GPT-6 Luna handles fast, high-volume jobs, and the planned GPT-6.1 Astra never shipped at all. If you are choosing between Sol and Luna — in the API, in Codex, or in ChatGPT itself — the decision is simpler than the names suggest: pick Sol when judgment matters, pick Luna when volume matters, and do not pay flagship prices for work either one can already handle.
This guide compares GPT-6.1 Sol and GPT-6 Luna on price, capability, benchmarks, access, and real-world fit, with the honest caveats the launch headlines skipped.
The Short Answer
- GPT-6.1 Sol is OpenAI's near-flagship workhorse. It is built for complex coding, multi-step agents, document work, computer use, and professional research — at one-fifth of GPT-6 Astra's API price.
- GPT-6 Luna is OpenAI's efficiency model. It is built for focused, repeatable, high-volume tasks — classification, extraction, routing, summaries, and bulk transformations — at one-twentieth of Sol's price.
- Both share the same 1.05-million-token context window and 128,000-token maximum output, so the difference is not how much they can read. It is how much judgment each one brings to the task.
- If you only remember one rule: start with Luna for work you can verify cheaply; start with Sol for work where a weak answer costs you time, money, or rework.
What Changed in September 2026
A quick timeline keeps the names straight:
- September 22, 2026: OpenAI released GPT-6 Sol and GPT-6 Luna, cutting API prices roughly in half against their GPT-5.6 predecessors. Sol was positioned for complex coding and agentic workflows; Luna for focused, high-volume tasks.
- September 28, 2026: OpenAI cancelled the planned GPT-6.1 Astra release after internal safety testing found the model did not meet its bar for staying within scope and authorization and for accurately communicating what it had done. The original GPT-6 Astra remains the flagship tier.
- September 29, 2026: At DevDay, OpenAI released GPT-6.1 Sol, an upgrade to GPT-6 Sol that it describes as delivering near-Astra performance at a fifth of Astra's standard API price. The older GPT-6 Sol is not deprecated, but its model page now points users to GPT-6.1 Sol, and new projects should start on 6.1.
- October 7, 2026: OpenAI rolled GPT-6 out to the full ChatGPT user base. GPT-6 Sol powers paid tiers, while GPT-6 Luna serves Free and Go users. GPT-6.1 Sol itself is not yet in the standard ChatGPT chat surface — it lives in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise, and Edu users, plus the API.
That last point is the most common source of confusion. Seeing "GPT-6 Sol" in ChatGPT does not mean you are using GPT-6.1 Sol. For the newest Sol, you need Work, Codex, or the API.
Pricing: A 20x Gap That Decides Most Choices
API pricing, per million tokens, standard tier:
| Model | Input | Cached input | Output |
|---|---|---|---|
| GPT-6.1 Sol | $2.00 | $0.10 | $10.00 |
| GPT-6 Luna | $0.10 | $0.01 | $0.50 |
| GPT-6 Astra (flagship) | $10.00 | $1.00 | $50.00 |
GPT-6.1 Sol costs the same per token as the original GPT-6 Sol on input and output, but its cached input is half the price ($0.10 vs $0.20). Against Astra, Sol is one-fifth the price on input and output, and one-tenth on cached input. Against Luna, Sol is 20 times the price on every component.
To make that concrete, take an illustrative workload of 10,000 tasks using 2,000 uncached input tokens and 500 output tokens each — 20 million input tokens plus 5 million output tokens in total. At list rates, that workload costs about $4.50 on Luna, about $90 on Sol or GPT-6.1 Sol, and about $450 on Astra. These are scenario calculations, not measured usage: real bills also depend on reasoning effort, retries, tool calls, cache writes, and long-context surcharges. Prompts over 272,000 input tokens bill at higher rates, and Batch and Flex tiers run at half the standard rates.
The takeaway is not that Luna is "cheap" and Sol is "expensive." It is that the price gap is so large that model choice is a routing decision, not a preference. Every task you can safely send to Luna saves 95% against Sol. Every task you send to Sol that Luna could have handled is money spent on judgment you did not need.
Capability: What Each Model Is Actually For
GPT-6.1 Sol: judgment, coding, and agents
OpenAI positions GPT-6.1 Sol for agentic coding, complex documents, business workflows, computer use, scientific tasks, and factual accuracy. In practice, that means:
- Writing and debugging multi-file code, with terminal interaction and longer reasoning chains.
- Running multi-step agents that plan, use tools, check their own work, and recover from errors.
- Synthesizing long documents, reports, and research where nuance and cross-references matter.
- Computer-use tasks — operating interfaces and completing end-to-end workflows.
It also carries a practical API change worth knowing before you migrate: GPT-6.1 Sol drops the none reasoning-effort setting, so low is now the floor, and tool calling works through the Responses API, while Chat Completions runs without tools. Its API model ID is gpt-6.1-sol.
GPT-6 Luna: speed, volume, and verifiable work
OpenAI describes Luna as its most efficient model for focused, high-volume tasks. That is the right mental model: Luna shines where the task is bounded, repeatable, and easy to check.
- Classifying feedback, tickets, or documents into fixed labels.
- Extracting structured fields from messy text, logs, or HTML.
- Routing requests, cleaning data, and reformatting content.
- Producing short summaries in a fixed template.
- Acting as a subagent on a narrowly scoped step inside a larger pipeline.
Luna is not the model for ambiguous briefs, open-ended research, or decisions where a plausible-sounding wrong answer would slip past review. For those, the cost of being wrong dwarfs the saving on tokens.
Benchmarks: Vendor Claims vs Independent Tests
Benchmarks should inform the choice, not make it. OpenAI's own figures and independent tests point in the same direction: GPT-6.1 Sol is close to Astra, and far ahead of Luna, on hard work — while Luna remains strong value on bounded work.
OpenAI's reported results for GPT-6.1 Sol include matching Astra on DeepSWE v1.1 software-engineering tasks at roughly a fifth of the per-task cost, coming within about two points of Astra on OSWorld computer-use benchmarks at roughly a seventh of the cost, and cutting the share of answers with a factual error from 11.4% on GPT-6 Sol to 7.7% on GPT-6.1 Sol at low reasoning effort. Astra still leads on the hardest scientific tasks — OpenAI itself says Astra "should be used for the most difficult scientific research tasks."
Independent testing broadly supports the pitch. Artificial Analysis scores GPT-6.1 Sol at 52 on its Intelligence Index, one point behind Astra at 53 and four points ahead of the original GPT-6 Sol at 48, with an estimated cost of $0.72 per index task against Astra's $3.26. GPT-6 Luna scores 37 on the same index — well behind Sol, but at a fraction of the cost per task. Independent DeepSWE runs have also put Sol close to Claude Opus 5.5 on coding, though Opus 5.5 still leads the overall index at 58.
Two honest caveats: first, benchmark scores are measured at maximum reasoning effort, which is slower and more expensive than the default. Second, higher scores do not guarantee better results on your workload. If you are choosing between models for production, run both on your own evaluation set and compare cost per accepted result — not price per million tokens.
Access: Where You Can Actually Use Each Model
| Surface | GPT-6.1 Sol | GPT-6 Luna |
|---|---|---|
| OpenAI API | Yes, as gpt-6.1-sol | Yes, as gpt-6-luna |
| ChatGPT Work / Codex | Yes, Plus/Pro/Business/Enterprise/Edu | Yes, paid plans |
| Standard ChatGPT chat | Not yet | Paid plans get GPT-6 Sol; Free/Go get Luna (desktop app) |
In ChatGPT, paid users get GPT-6 Sol in the main chat experience, while Free and Go users get GPT-6 Luna. An Ultrafast service tier for GPT-6.1 Sol arrived in the Responses API on October 8, 2026, for developers who need lower latency between output tokens. Availability details change often, so check OpenAI's model pages before building a workflow around a specific surface.
Pros and Cons
GPT-6.1 Sol
Pros
- Near-Astra capability on coding, documents, and agents at one-fifth of Astra's API price.
- Half-price cached input against the original GPT-6 Sol; strong value on repeated, cache-friendly workloads.
- Improved factual accuracy and alignment scores against its predecessor, per OpenAI's safety card.
- Large 1.05M-token context window with 128K maximum output.
Cons
- Still 20x the price of Luna — overkill for simple, repeatable tasks.
- Slower than Luna on reasoning-heavy prompts; maximum-effort runs can be slow to start.
- Not yet in standard ChatGPT chat; requires Work, Codex, or the API.
- API migration quirks: no
noneeffort level, and tool calling only via the Responses API.
GPT-6 Luna
Pros
- Extremely cheap: $0.10 input / $0.50 output per million tokens, with $0.01 cached input.
- Fast and well suited to bulk, parallel, high-volume pipelines.
- Available to Free and Go users in the ChatGPT desktop app.
- Same large context window and output limit as Sol.
Cons
- Much weaker on hard reasoning, ambiguous coding, and open-ended research (Intelligence Index 37 vs Sol's 52).
- Needs an escalation path: hard cases must route up to Sol or Astra, or quality slips.
- Savings disappear if low-quality output creates review, retry, or rework.
Who Should Use Which?
- Solo developers and small teams: Default to GPT-6.1 Sol for coding, debugging, and agents. Route bulk chores — log triage, test-data cleanup, label extraction — to Luna.
- Content and operations teams: Use Luna for classification, extraction, first-pass summaries, and template-driven drafts. Use Sol for research synthesis, strategy documents, and anything that will be published or sent to a client without heavy review.
- Product teams building AI features: Use Luna for high-volume, low-risk steps inside a pipeline, with Sol as the escalation tier for edge cases and failures. Measure cost per accepted result, not cost per token.
- Students and casual users: On ChatGPT, you will mostly get the model your plan includes — Sol on paid plans, Luna on Free/Go. If your work needs deeper reasoning, the API or Codex route to GPT-6.1 Sol is the upgrade path.
- Anyone on GPT-6 Sol today: Migrate to GPT-6.1 Sol. Same input/output price, cheaper cached input, better benchmarks, and OpenAI is steering new users there.
Verdict
GPT-6.1 Sol vs GPT-6 Luna is not a fight for a single winner. It is a routing question. Sol is the model you reach for when the work needs judgment; Luna is the model you reach for when the work needs throughput. The smart setup uses both: Luna for the bulk, Sol for the hard tail, and Astra only for the rare tasks where its final capability advantage changes the outcome. If you are still defaulting everything to the most expensive model — or running hard work on the cheapest one — you are paying for the wrong thing either way.
Frequently Asked Questions
Is GPT-6.1 Sol better than GPT-6 Luna?
For hard work, yes — coding, agents, complex documents, and research. For simple, repeatable, high-volume work, Luna is the better choice because it is 20x cheaper and fast enough.
Is GPT-6.1 Sol the same as GPT-6 Sol?
No. GPT-6.1 Sol is the September 29, 2026 upgrade to GPT-6 Sol. It keeps the same $2/$10 input/output pricing, halves cached input to $0.10, improves benchmark scores, and drops the none reasoning-effort setting.
Why was GPT-6.1 Astra cancelled?
OpenAI cancelled the planned October 2026 release on September 28, 2026, after internal safety testing found the model fell short on staying within scope and authorization and on accurately reporting what it had done. The original GPT-6 Astra remains the flagship.
Can free ChatGPT users use GPT-6.1 Sol?
Not in the standard chat surface. Free and Go users get GPT-6 Luna in the ChatGPT desktop app. GPT-6.1 Sol requires a paid plan plus Work, Codex, or the API.
Related reading: see our guide to ChatGPT's GPT-6 intelligent UI, Claude Haiku 5.5 for Anthropic's competing cheap-and-fast option, and Gemini Agent, Google's universal AI agent for work.
Sources: Unite.AI; DigitalMatters; Lindy; LLMFly; AICatchup.
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