GPT-6 Astra vs Sol vs Luna: Is Astra worth 5x the price?

OpenAI's current lineup has three tiers. GPT-6 Astra sits at the top. OpenAI positions it as its most capable model, calling it its most intelligent model for the best results. GPT-6.1 Sol is the mid-tier option, and GPT-6 Luna is the budget tier for high-volume work. Astra costs $10 per million input tokens and $50 per million output tokens. Sol costs one-fifth of that, and Luna costs one-twentieth of Sol.

For Astra's premium, you'd expect a clear capability lead over Sol. In our Ready for Enterprise (RFE) LLM Benchmark, there isn't one. Astra's capability score is 89.1. Sol's is 88.6. Once price and speed are factored in, Sol ranks higher overall: #7 with an RFE score of 57.3, compared with #9 and 56.2 for Astra.

For most enterprise workloads, the 5x price isn't worth it. Test GPT-6.1 Sol first, use GPT-6 Luna for simple high-volume tasks, and pay for Astra only where you can measure the difference. The best model for a given job is usually the cheapest capable model that passes your tests, not the most expensive OpenAI model on the price list.

GPT-6 Astra vs Sol vs Luna at a glance

Source: FullStack RFE LLM Benchmark, data snapshot used for this article. Rankings can change as new models ship and results are added.

Our RFE score combines six public benchmarks covering agentic work, factual accuracy, human preference, web development, graduate-level science, and advanced math, then adds price and speed. The capability score reflects benchmark performance alone. Astra and Sol are within half a point on capability, while Astra costs five times as much, which is why Sol comes out ahead overall. 

Luna’s low price lifts its RFE score to 56.3, slightly above Astra’s, but its capability score of 33.3 falls below FullStack’s capability threshold. Models under the threshold rank after every model that reaches it, which is why Luna sits at #16.

Astra vs Sol: where the benchmarks tie

On two of the hardest tests in our comparison, Astra and Sol score exactly the same. Both reach 58.2% on Terminal-Bench 4.0, which measures multistep agentic work in a command-line environment. Both reach 93.7% on FrontierMath Tiers 1–3, a test of advanced math. On GPQA Diamond, a graduate-level science test, they're within half a point (95.8% versus 95.4%). On Arena Text, a human-preference rating, Sol actually scores higher, at 1484 versus 1477.

OpenAI's own results tell a similar story. OpenAI positions GPT-6.1 Sol as near-Astra intelligence for a fifth of the price. On DeepSWE v1.1, a test of complex software engineering tasks in real codebases, OpenAI reports that Sol matches Astra at roughly one-fifth of the cost. On GDP.pdf, a test of professional work that involves answering questions from complex PDFs, Sol approaches Astra's score at roughly one-fifth of the cost per task. For coding agents and complex coding work, that makes Sol the more efficient choice.

Where GPT-6 Astra still leads

Astra's advantages are real but small, and cluster in a few areas.

Factual reliability

Astra scores 75.6% on SimpleQA Verified, versus 73.9% for Sol. OpenAI reports that across tested reasoning settings, Sol's factual error rate stays within 1.9 percentage points of Astra's, at less than one-fifth the cost per task.

Web development and coding

Astra leads on Arena WebDev, at 1774 versus 1753, and on the SWE-Bench Pro figures we track, at 96.9% versus 96.1%. Both are modest gaps.

Computer use and the hardest research problems

On the offline set of OSWorld 2.0, a test of computer-use workflows, OpenAI reports that Sol comes within 2.1 percentage points of Astra at maximum reasoning effort, at roughly one seventh the cost per task. The widest gap is on Terminal-Bench Science, which tests scientific research workflows. There, Astra achieves the highest score of the models OpenAI tested, at 68.1%, and OpenAI recommends Astra for the most difficult scientific research. That capability comes at a price: at maximum effort, about $23.80 per task for Astra, versus $5.47 for Sol.

Speed

In our measurements, Astra is slightly faster than Sol, at 37 tokens per second versus 29, with a quicker first token (3.03 seconds versus 3.51). Neither is fast compared with models like Claude Sonnet 5.5, which runs at 86 tokens per second in our measurements. But if you're choosing between these two, the premium model doesn't cost you speed.

What the 5x price means in practice

GPT-6 Astra's standard rates are five times Sol's on both input and output. With prompt caching, the gap widens: cached input costs $1.00 per million tokens for Astra versus $0.10 for Sol, a 10x difference. For agents that reuse long system prompts, that matters.

For example, consider a task that uses 20,000 input tokens and 4,000 output tokens, at standard rates without caching:

Illustrative estimate at standard list prices. Real costs depend on how many tokens each model uses, reasoning effort, caching, and retries. A model that needs more tokens or more retries to finish a task can cost more than this estimate suggests.

Independent measurements show a similar ratio between Astra and Sol. Running the Artificial Analysis Intelligence Index, a composite of 10 benchmarks, costs $0.72 per task with GPT-6.1 Sol versus $3.26 with Astra. Sol scored 52 on that index at max effort, one point below Astra's 53.

Context window, reasoning effort, and access

GPT-6.1 Sol has a 1,050,000-token context window and up to 128,000 output tokens. Prompts with more than 272,000 input tokens are billed at higher rates for the full request: twice the input and cache rates, and 1.5 times the output rate. A larger context window doesn’t guarantee better answers, so test with the prompt sizes you’ll actually send.

GPT-6.1 Sol supports five reasoning settings: low, medium, high, xhigh, and max. Medium effort is the default, and the model doesn’t support turning reasoning off. Higher settings, such as xhigh effort or max effort, generally use more tokens, so set the reasoning effort explicitly when you compare models.

OpenAI lists API pricing for all three models. At launch, GPT-6.1 Sol was also available in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise, and Edu users, though not yet in regular ChatGPT conversations. Tool availability and limits vary by plan, so check the details for your enterprise plan before you decide.

Where GPT-6 Luna fits

GPT-6 Luna follows GPT-5.6 Luna, the budget tier of OpenAI's earlier GPT-5.6 family, which also included Sol and Terra. 

OpenAI describes GPT-6 Luna as built for fast and efficient everyday work at scale. It costs $0.10 per million input tokens, $0.50 per million output tokens, and $0.01 per million cached input tokens, one twentieth of Sol's standard rates. It's also the fastest of the three in our measurements, at 53 tokens per second with a 2.42-second first token.

Cheap models come with trade-offs, and Luna's are clear in our data. Its capability score is 33.3, below our benchmark's capability threshold, and it scores 16.4% on Terminal-Bench 4.0 and 41.4% on SimpleQA Verified. In one OpenAI test of whether models tell users when their search tool is broken, Luna failed to disclose the problem in 28.7% of cases, compared with 2.1% for Sol and 1.5% for Astra.

That makes Luna a poor fit for agentic coding or complex work, but a reasonable option for simple, well-defined tasks such as classification, extraction, routing, and producing structured summaries, especially when each output is checked or low-stakes. 

^{For how OpenAI's earlier Terra, Luna, and Sol models performed against Anthropic on real engineering tasks, see our comparison of OpenAI's Terra, Luna, and Sol vs. Anthropic.}

When GPT-6 Astra is worth the premium

Pay for Astra when the small capability gap has a measurable business cost:

• The hardest reasoning and research problems. Astra has the clearest lead on Terminal-Bench Science, and OpenAI recommends it for the most difficult scientific research.
• High-stakes factual work. If a small reduction in factual errors prevents costly mistakes, Astra's edge on SimpleQA Verified may justify the price.
• Low-volume, high-value tasks. When a workflow runs a few hundred times a month, and each result is worth far more than the model cost, the 5x premium adds little to the total.
• Cases where Sol fails your tests. If Sol misses on your own evaluation set and Astra passes, the premium is buying something real.

When GPT-6.1 Sol is the better choice

For most enterprise workloads, Sol delivers nearly the same results at a lower cost:

• Agentic coding and coding agents, where the two models tie on Terminal-Bench 4.0
• Professional work and business workflows
• Advanced math and quantitative analysis, where they tie on FrontierMath
• Any high-volume workflow, where the 5x price multiplies across every request

How to decide

Start with Sol, and make Astra prove its value:

1. Build an evaluation set from your own workloads, including the hardest cases.
2. Run Astra and Sol with the same prompts, tools, and reasoning settings. Add Luna for any simple, high-volume tasks.
3. Compare accepted results, error rates, and cost per completed task.
4. Route each task to the cheapest model that passes. Send only the tasks where Astra measurably wins to Astra.

This kind of routing keeps the premium model where it earns its price and keeps everything else on a lower cost tier.

How FullStack can help

FullStack builds OpenAI-powered systems for clients and runs five of its own in production. Our OpenAI implementation services help enterprises test model tiers on their real workloads, set up routing between them, and control what they spend on AI.

^{Compare these models in the RFE benchmark.}