GPT-6 Astra, Sol or Luna: Which Tier You Need, and Why GPT-6 Sol Beat GPT-6.1 Sol

OpenAI's GPT-6 lineup has four models: Astra at the top, two Sol models in the middle, and Luna at the bottom. We ran all four through the same three script-graded tasks, five times each.

All four passed all 15 calls. None of them got an answer wrong, so accuracy does not decide which one you need on this kind of work. What separates them is price, latency and token count. Three findings:

1. GPT-6 Sol was faster than GPT-6.1 Sol on all 15 calls, at the same list price, and cost about 10% less.
2. Astra cost 5.65× as much as GPT-6 Sol for the same graded results.
3. Luna cost 13× less than GPT-6 Sol, less than its 20× list-price gap, because it reasons more.

Prices

From shared/pricing.ts, per million tokens:

The two Sol models cost the same. The obvious question is whether there is any reason to stay on the older one. On our battery, the answer is the opposite of what you would expect.

Quality: all 60 calls passed

On the JSON task, Astra and both Sol models returned exactly 33 output tokens on every run: the same four-key object, no code fences, no reasoning tokens. Luna returned the same object with 29–39 reasoning tokens before it.

Cost and latency

Cost per battery pass (one call of each task), averaged over five passes; median latency per call:

GPT-6 Sol was the fastest of the four on every task.

GPT-6 Sol vs GPT-6.1 Sol

This is the clearest result in the post. Every individual call, not just the medians:

The ranges do not overlap on any task. The slowest GPT-6 Sol call on each task was faster than the fastest GPT-6.1 Sol call. On the arithmetic task the median gap is more than 2×.

Cost moved the same way. The JSON task cost exactly the same on both ($0.000612 per call, identical tokens). On arithmetic, 6.1 Sol produced slightly more output (134–163 tokens against 127–150). On bullets, 6.1 Sol produced 58–140 tokens against GPT-6 Sol's 35–104, partly because GPT-6 Sol skipped reasoning entirely on three of its five bullets runs. Over 15 calls, 6.1 Sol came to $0.0159 and GPT-6 Sol to $0.01438.

Two caveats. Latency is wall-clock from one machine in one session on 2026-10-19, and host-side latency can change week to week. And this battery is three short tasks; a newer model's improvements may sit in work it does not test. But if you are choosing between the two Sol models today on ordinary structured work, this battery gives no reason to move from GPT-6 Sol to GPT-6.1 Sol, and two measured reasons not to.

Astra: 5.65× the cost for the same results

Astra's list price is 5× Sol's. Measured, it cost 5.65× GPT-6 Sol per battery pass and 5.11× GPT-6.1 Sol. It was also slower than GPT-6 Sol on every task, roughly twice as slow at the median.

Astra used more reasoning than GPT-6 Sol on the bullets task (18–97 reasoning tokens per run against 0–61), and every token costs 5× more. On the arithmetic task the two used similar token counts (Astra 131–147 output tokens, GPT-6 Sol 127–150), so the cost gap there is almost exactly the list-price gap.

The case for Astra is work this battery cannot reach: long agentic runs, hard multi-step problems, anything where a small per-step advantage compounds. Our tasks resolve in one call. If yours do too, Astra is buying nothing we can measure.

Luna: 13× cheaper, not 20×

Luna's list price is one twentieth of Sol's. It came in at 13.1× cheaper than GPT-6 Sol per pass, because it reasons on every task where GPT-6 Sol often does not:

Luna's bullets answers cost 117–172 output tokens; GPT-6 Sol's cost 35–104. The visible text was about the same length. This is the same pattern we saw in August, when GPT-5.6 Luna's measured gap to Sol was smaller than its list gap, that time because Luna wrote more.

13× is still a lot. At a million battery passes Luna costs about $219 and GPT-6 Sol about $2,876. For high-volume extraction, classification or formatting work of this shape, Luna passed everything we gave it.

OpenAI counts reasoning inside completion_tokens, so every cost in this post already includes it. What Promptster displays is what OpenAI bills.

Which tier

If you are moving off a retired model, see the October 23 swap table; gpt-6-luna is Promptster's suggested replacement for gpt-4.1-nano. For how GPT-6 compares with Anthropic's tiers at the same prices, see Claude 5.5 vs GPT-6, tier by tier.

Method

Three script-graded tasks: strict JSON extraction from a messy support ticket (four keys, exact match, code fences fail), a four-step arithmetic word problem with answer 580, and an instruction-adherence task asking for exactly three bullets of 12 words or fewer with no preamble. Five runs per task per model, requested at temperature 0.7 and max_tokens 1,500, run 2026-10-19 through Promptster's own provider handlers. GPT-6 models reject a custom temperature, so the OpenAI handler sends reasoning_effort: medium and max_completion_tokens instead. Costs are from shared/pricing.ts at list rate with no cache or batch discounts. Latency is wall-clock from one machine, not an OpenAI SLA. Five runs is enough for pass/fail against a fixed answer; the latency ranges are a single session.

The wider comparison, including open-weight models, is in Benchmarking open-source vs. closed-source models.

Try it yourself

• Run the three-bullet instruction task — the task where Luna's extra reasoning shows most, no account needed

To run GPT-6 Sol and GPT-6.1 Sol side by side on your own prompts and see whether the latency gap holds, that's Promptster.

All figures from content/blog/data/q4-2026-battery/ (summary plus one raw file per call, including OpenAI's usage object verbatim), run 2026-10-19. Costs at list price from shared/pricing.ts. Per-pass costs are the 15-call total divided by five. Ranges are min–max across five runs; medians over five runs.