GPT-5.6 Sol vs DeepSeek V4 Pro

head-to-head

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 80.6% for DeepSeek V4 Pro, a 15.6-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?

DeepSeek V4 Pro is the cheaper model: $0.435 per 1M input tokens ($0.87 output) versus $5 ($30 output) for GPT-5.6 Sol — roughly 11.5× less on input. Coding workloads are output-heavy — agents write diffs, tests and retries — so weight the output rate more than the input rate when you estimate a monthly bill.

DeepSeek V4 Pro ships open weights. Beyond the per-token discount, that means you can self-host it, fine-tune it, pin a snapshot so the model can't change under you, and keep code on your own infrastructure — none of which is possible with GPT-5.6 Sol, which is API-only. For teams with compliance constraints that difference outweighs any benchmark point.

when to pick each

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 — now the second-highest score on the board. Correction, Jul 25, 2026: this row read "the top score on the board" from Jul 17 until Jul 25, when vals.ai evaluated Claude Opus 5 at 97.00% ±0.76 and took the #1 slot. The 0.8-point gap is ~0.7 sigma and not significant, so the two are a statistical tie, and Sol still leads on the longest tasks (98% vs 90% on the 1-to-4-hour tier). 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.
• DeepSeek V4 Pro: Vendor-reported, as aggregated by llm-stats (June 2026). Corrected Jul 17, 2026: we previously called llm-stats an "independent tracker", but llm-stats labels its own SWE-bench Verified table "Verified: 0 / Self-reported: 104" — every score on it is vendor-claimed, so this is a vendor number, not an independent one. No independent evaluator has run V4 Pro; vals.ai has evaluated the plain DeepSeek V4 (77.4%), which is a different model. Tied with Gemini 3.1 Pro on Verified, ahead on Pro.

Full reviewsGPT-5.6 Sol, decoded

Ranked on our AI Coding Leaderboard, updated 2026-07-27. Scores are confirmed against primary sources; prices are per 1M input tokens and can change.