GPT-6.1 Sol vs GPT-6 Astra vs GPT-6 Luna: Which Should You Choose?
Treating these models as simple “high, medium, and low” tiers leads to poor decisions. A better summary is: Astra prioritizes capability at the frontier, GPT-6.1 Sol balances complex-task intelligence and cost, and Luna prioritizes efficiency at scale.
Most teams do not need to choose only one. They need routing rules that use all three where each makes economic sense.
The short answer
| Requirement | Starting choice | Why |
| The hardest end-to-end work where failure is costly | GPT-6 Astra | OpenAI’s most capable model | | Complex coding, research, documents, and tool workflows | GPT-6.1 Sol | Near-Astra positioning at a much lower price | | Classification, extraction, batch processing, and high volume | GPT-6 Luna | Designed for focused, cost-sensitive workloads | | You do not yet know which one you need | GPT-6.1 Sol | A useful middle baseline for both quality and cost |
What the three models share
All three are reasoning models with a 1,050,000-token context window, a maximum input of 922,000 tokens, and a maximum output of 128,000 tokens. They accept text and images and return text. They support the Responses, Chat Completions, and Batch endpoints.
They also support structured outputs, function calling, file search, image input, web search, prompt caching, Code Interpreter, hosted shell, computer use, and MCP. Similar specifications do not imply identical results. Complex-task completion rate, latency, and total cost remain the important differences.
GPT-6 Astra: preserve headroom for the hardest work
OpenAI positions Astra as its most capable model for complex reasoning, coding, computer use, research, and document creation.
It is a strong candidate when:
- One wrong decision could cause expensive rework or material risk.
- A task spans many steps, tools, and sources.
- The request is incomplete and requires sustained clarification, planning, and correction.
- Evaluation shows that Sol is not reliable enough for a particular hard workload.
The tradeoff is substantial. With Standard short-context processing, Astra costs $10 per million input tokens and $50 per million output tokens—five times GPT-6.1 Sol’s rates. It should not become the unconditional default merely because it is the flagship model.
GPT-6.1 Sol: the default candidate for complex work
OpenAI describes GPT-6.1 Sol as delivering near-Astra performance at a lower cost for complex coding, computer use, and professional work.
Its Standard short-context price is $2 per million input tokens and $10 per million output tokens. For many production teams, that makes it the best first model to evaluate: test its quality, then determine whether a workload should move up to Astra or down to Luna.
Representative tasks include cross-file code changes, long-document review, research synthesis, multi-tool content workflows, complex spreadsheet analysis, and knowledge work that requires planning and verification.
GPT-6 Luna: put scale and cost first
Luna is OpenAI’s most efficient model for focused, high-volume tasks. At Standard short-context rates, it costs $0.10 per million input tokens and $0.50 per million output tokens—one twentieth of GPT-6.1 Sol’s corresponding rates.
It is well suited to tasks with clear boundaries and easy verification:
- Intent classification and content tagging.
- Extraction into a fixed schema.
- Large-scale summarization, rewriting, and format conversion.
- Routine customer-support flows.
- First-pass screening before escalating uncertain cases.
Luna also supports none reasoning effort, which GPT-6.1 Sol and Astra do not. That can matter for latency-sensitive work that does not need deeper reasoning.
Price comparison
The following rates were listed by OpenAI on October 16, 2026. They are Standard short-context prices in U.S. dollars per million tokens.
| Model | Input | Cached input | Output | | GPT-6 Astra | $10.00 | $1.00 | $50.00 | | GPT-6.1 Sol | $2.00 | $0.10 | $10.00 | | GPT-6 Luna | $0.10 | $0.01 | $0.50 |
This is not the same as cost per task. Higher reasoning effort, long output, tool use, and input above 272K tokens can all change the bill. The stronger business metric is total cost per successful task, including review and rework.
A practical routing policy
Start with rules like these, then adjust them using your own evaluation data:
- Send clear, low-risk, automatically verifiable requests to Luna.
- Escalate requests with multiple steps, long documents, cross-file changes, or tool coordination to GPT-6.1 Sol.
- Escalate again when Sol fails validation or when the value and risk of the task justify Astra.
- Evaluate
low,medium, andhighseparately; do not confound model choice with reasoning effort. - Track task success, P95 latency, tokens, tool calls, and human rework time.
How creative teams can use the same framework
Creative work can also be tiered. Luna can classify assets, create tags, normalize formats, and propose headline candidates. GPT-6.1 Sol can work with long story bibles, check continuity, shape scripts, and draft shot plans. Astra can be evaluated for high-value adaptations or difficult structural diagnosis.
If a team develops story ideas in Elser AI or turns source material into characters, locations, shots, and edits in ElserStudio, model selection should still follow the production step: use throughput for routine organization, reliability for structural planning, and human judgment for consequential creative decisions.
The bottom line
These models are complementary rather than simple replacements. The most common sensible pattern is: Luna for simple work at scale, GPT-6.1 Sol for the complex majority, and Astra for the hardest minority.
If you can evaluate only one model first, GPT-6.1 Sol is an informative starting point. It tests serious capability without placing every experiment at flagship pricing.
