GPT-5.6 Sol vs GPT-5.6 Terra

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 95.4% for GPT-5.6 Terra, a 0.8-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?

GPT-5.6 Terra is the cheaper model: $2 per 1M input tokens ($12 output) versus $5 ($30 output) for GPT-5.6 Sol — roughly 2.5× 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.

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.
• GPT-5.6 Terra: Independent (vals.ai, re-evaluated, mini-swe-agent bash-only harness): SWE-bench Verified 95.4% ±0.94, 5th of 86 systems. CORRECTION, 2026-08-20: this row previously carried 75.2% ±1.93 (32nd of 75), the figure vals.ai published on Jul 22 2026 and which we added Jul 27, 2026. A routine sweep found vals.ai had re-run the model and revised the score up by 20.2 points; the old figure and the reading we built on it, that Terra was the weakest of the three GPT-5.6 tiers, are both withdrawn. A jump that large on an unchanged model points at the first run rather than the model, so read 95.4% as the corrected measurement and not as an improvement. Terra now sits second among the GPT-5.6 tiers, behind Sol at 96.2% and ahead of Luna at 93.0%. Treat it as tied with GLM-5.3 (also 95.4% ±0.94), Grok 4.6 (95.6%) and Claude Fable 5 (95.0%): pooled SEM across those rows is about 1.3 points and every gap is smaller than that. Cheapest of the 95%-plus cluster on measured cost per test at $0.40, against $1.15 for Sol and $2.05 for Fable 5. Repriced Aug 1, 2026: OpenAI cut Terra 20% on Jul 30, 2026, from $2.50/$15 to $2/$12 per 1M (vendor-announced, reported by BleepingComputer and VentureBeat). Cached input $0.25 at launch, not re-confirmed after the cut. Context ~1.05M, max output 128k. Released Jul 9, 2026.

Full reviewsGPT-5.6 Sol, decoded

Ranked on our AI Coding Leaderboard, updated 2026-08-20. Scores are confirmed against primary sources; prices are per 1M input tokens and can change.