GPT-5.6 Sol vs DeepSeek V4 Flash

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 88.8% for DeepSeek V4 Flash, a 7.4-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 Flash is the cheaper model: $0.14 per 1M input tokens ($0.28 output) versus $5 ($30 output) for GPT-5.6 Sol — roughly 35.7× 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 Flash 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 Flash: Independent (vals.ai, Aug 5 2026, mini-swe-agent bash-only harness): SWE-bench Verified 88.8%, for the DeepSeek-V4-Flash-0731 build, matching the checkpoint DeepSeek own change log describes. Ranks above Claude Opus 4.8 (88.6%) and below GPT-5.6 Luna (93.0%). Vendor-reported agent numbers for the same build (DeepSeek change log, Jul 31 2026): Terminal-Bench 2.1 82.7, Cybergym 76.7, Toolathlon verified 70.3, DeepSWE 54.4, NL2Repo 54.2, Agent Last Exam 25.2, produced with DeepSeek own unreleased "DeepSeek Harness minimal mode" and not independently reproduced. The V4-Flash-Preview model card (pre-0731 weights) separately listed SWE-bench Verified 79.0 and Terminal-Bench 2.0 56.9; those are not carried over. Same architecture and size as the preview (284B total / 13B active, FP4+FP8), re-post-trained only. Not to be confused with DeepSeek V4 Pro (ranked, 80.6%) or the plain DeepSeek V4 (77.4%). See /p/deepseek-v4-flash-claims-82-7-terminal-bench/.

Full reviewsGPT-5.6 Sol, decodedDeepSeek V4 Flash, decoded

Ranked on our AI Coding Leaderboard, updated 2026-08-06. Scores are confirmed against primary sources; prices are per 1M input tokens and can change.