head-to-head
| Metric | GPT-5.6 Sol | GPT-5.6 Luna |
|---|---|---|
| SWE-bench Verified | 96.2% | 93.0% |
| SWE-bench Pro | — | — |
| Terminal-Bench | 88.8% | — |
| Input $ / 1M | $5 | — |
| Output $ / 1M | $30 | — |
| Context | — | — |
| Open weights | No | No |
| Access | API · Codex (public since Jul 9 2026) | API |
| Maker | OpenAI | OpenAI |
what do the benchmarks actually say?
On SWE-bench Verified — real, human-validated GitHub issues resolved end-to-end — GPT-5.6 Sol posts 96.2% against 93.0% for GPT-5.6 Luna, a 3.2-point gap. Verified is the closest public proxy for "can it fix a real bug in a real repo without help", which is why it anchors our ranking.
A few points either way is real but not decisive: within that band, the agent scaffolding around the model — how it retrieves files, runs tests, and retries — often matters as much as the base model. Treat the gap as a lean, not a verdict.
which is cheaper to run?
Public per-token pricing isn't confirmed for both models, so we don't print a cost comparison yet.
when to pick each
The highest independently measured coding score of any model, and it holds up on the long tasks: 98% on the 1-to-4-hour tier where most models fall apart.
The cost outlier of the leaders: within striking distance of the top scores while costing a fraction per solved task, which makes it the obvious candidate for high-volume agent runs.
how were these scores verified?
We only print a number once it's confirmed against a primary source or an independent evaluation, and each row on our leaderboard records which kind it is:
- GPT-5.6 Sol: Independent (vals.ai, Jul 14 2026, mini-swe-agent bash-only harness): SWE-bench Verified 96.20% ±0.86 — the top score on the board. Verified Jul 17, 2026; it had been unranked since Jun 26 because OpenAI published no SWE-bench number of its own, and it still has not. Read the #1 with care: the 1.2-point lead over Claude Fable 5 (95.00% ±0.98) is inside the combined margin of error (~0.9 sigma, not significant), so the two are a statistical tie and we rank Sol first only because it scored higher. Where it does separate is task length — 98% on 1-4 hour tasks vs 93% for Fable 5. OpenAI's own Terminal-Bench 2.1 claim is 88.8% (Sol) / 91.9% (Sol Ultra). No SWE-bench Pro score published. Pricing $5/$30 per 1M.
- GPT-5.6 Luna: Independent (vals.ai, eval listed Jul 17 2026, mini-swe-agent bash-only harness): SWE-bench Verified 93.00% ±1.14. Added Jul 21, 2026 — this row was missing from the board even though vals.ai had already evaluated Luna, and our Kimi K3 note referenced its 93.0% score without ever listing it; adding it moves every row below it down one rank. Treat 3rd and 4th as a tie: Kimi K3's 93.40% ±1.11 is 0.4 points higher, well inside the combined margin of error (~0.25 sigma), so the ordering between them is not significant. Like the rest of the GPT-5.6 family, OpenAI has published no SWE-bench Verified figure of its own, so we rank on the independent number per our standing rule. The striking number is cost: vals.ai measured $0.21 per test against $1.15 for GPT-5.6 Sol, $1.92 for Claude Opus 4.8 and $2.05 for Claude Fable 5, at 201s median latency. Per-token list pricing not confirmed against OpenAI's own pricing page, so inPrice/outPrice stay blank rather than estimated.
Full reviewsGPT-5.6 Sol, decoded
Ranked on our AI Coding Leaderboard, updated 2026-07-21. Scores are confirmed against primary sources; prices are per 1M input tokens and can change.
- OpenAIvals.ai — SWE-bench Verified (independent) — Independent (vals.ai, Jul 14 2026, mini-swe-agent bash-only harness): SWE-bench Verified 96.20% ±0.86 — the top score on the board. Verified Jul 17, 2026; it had been unranked since Jun 26 because OpenAI published no SWE-bench number of its own, and it still has not. Read the #1 with care: the 1.2-point lead over Claude Fable 5 (95.00% ±0.98) is inside the combined margin of error (~0.9 sigma, not significant), so the two are a statistical tie and we rank Sol first only because it scored higher. Where it does separate is task length — 98% on 1-4 hour tasks vs 93% for Fable 5. OpenAI's own Terminal-Bench 2.1 claim is 88.8% (Sol) / 91.9% (Sol Ultra). No SWE-bench Pro score published. Pricing $5/$30 per 1M.
- OpenAIvals.ai — SWE-bench Verified (independent) — Independent (vals.ai, eval listed Jul 17 2026, mini-swe-agent bash-only harness): SWE-bench Verified 93.00% ±1.14. Added Jul 21, 2026 — this row was missing from the board even though vals.ai had already evaluated Luna, and our Kimi K3 note referenced its 93.0% score without ever listing it; adding it moves every row below it down one rank. Treat 3rd and 4th as a tie: Kimi K3's 93.40% ±1.11 is 0.4 points higher, well inside the combined margin of error (~0.25 sigma), so the ordering between them is not significant. Like the rest of the GPT-5.6 family, OpenAI has published no SWE-bench Verified figure of its own, so we rank on the independent number per our standing rule. The striking number is cost: vals.ai measured $0.21 per test against $1.15 for GPT-5.6 Sol, $1.92 for Claude Opus 4.8 and $2.05 for Claude Fable 5, at 201s median latency. Per-token list pricing not confirmed against OpenAI's own pricing page, so inPrice/outPrice stay blank rather than estimated.
- BenchmarkSWE-bench — the real-GitHub-issue benchmark