Claude Opus 5 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 — Claude Opus 5 posts 97.0% against 95.4% for GPT-5.6 Terra, a 1.6-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?

GPT-5.6 Terra is the cheaper model: $2 per 1M input tokens ($12 output) versus $5 ($25 output) for Claude Opus 5 — 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:

• Claude Opus 5: Independent (vals.ai, observed Jul 25 2026, mini-swe-agent bash-only harness): SWE-bench Verified 97.00% ±0.76, the highest score on the board and 1st of the 75 systems vals.ai has run. Entered ranked Jul 25, 2026 after one day unranked: Anthropic published no SWE-bench Verified number at launch and still has not, so this is vals.ai's own measurement rather than a vendor claim. Read the #1 as a three-way tie, not a win — GPT-5.6 Sol is at 96.20% ±0.86 (a 0.8-point gap, ~0.7 sigma) and Claude Fable 5 at 95.00% ±0.98 (2.0 points, ~1.6 sigma), both inside the combined margin of error. We rank Opus 5 first only because it scored highest. Where the top two genuinely separate is task length, and not in Opus 5's favour: on the 1-to-4-hour tier Sol solves 98% against Opus 5's 90%, while Opus 5 leads on shorter work (98% under 15 minutes and 97% on 15-minute-to-1-hour tasks, vs 97% and 95% for Sol). Released Jul 24, 2026 at $5/$25 per 1M, the same price as Opus 4.8 and half of Fable 5. Anthropic's launch claims stay unreproducible (Frontier-Bench v0.1, CursorBench 3.2 and Zapier AutomationBench are proprietary), so this is the first externally checkable score the model has. Fast mode runs about 2.5x default speed at 2x base price.
• 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 reviewsClaude Opus 5, 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.