AI Coding Leaderboard.

today's ai coding benchmark standings

how we rank: swe-bench verified, swe-bench pro and terminal-bench

We rank by SWE-bench Verified (500 real, human-validated GitHub issues resolved end-to-end), tiebroken by the harder SWE-bench Pro. A score is only printed once confirmed against an independent evaluation or the maker's primary source — and every row states which kind it is. Where both exist, we print both: one as the ranked score, the other in that row's note. We would rather show you the gap than ask you to trust our pick.

Our independent reference is vals.ai, which runs every model itself through the same minimal bash-only harness (mini-swe-agent), so the models are compared on equal footing. That matters more than it sounds: SWE-bench scores a model and its scaffolding together, and vendors report using their own tuned scaffolds. Against vals.ai's neutral harness, the vendor claims on this board run 2.6 to 11.6 points optimistic.

So rows marked vendor-reported are best-case numbers and are not strictly comparable to the independent ones — where we know the independent figure, we print it in the row's note.

That is a deliberate choice and you should know we made it: on five rows (Claude Sonnet 5, MiniMax M3, Qwen3.7 Max, Kimi K2.6, DeepSeek V4 Pro) an independent score for that exact model exists and disagrees with the maker's, and we still rank on the maker's published figure because it is the number that model is sold and quoted on. On four of those the independent number is lower.

DeepSeek V4 Pro runs the other way: vals.ai measures the 0813 GA build 15.8 points ABOVE DeepSeek's own claim, which the disclose-don't-rebase rule was never written for, so that row is flagged in its note for a human decision on whether to rebase it. We disclose the independent one beside it instead of quietly restating the board on a single evaluator's harness choice. The honest consequence: positions that straddle the two regimes are approximate.

Qwen3.7 Max is the sharpest case — it sits at #16 on Alibaba's 80.4%, and on the neutral harness its 68.8% would put it far down the table. Note that llm-stats, which we previously miscredited as an independent tracker, labels its own SWE-bench Verified table "Verified: 0 / Self-reported: 104"; it aggregates vendor claims. Models still being checked are marked “verifying” and shown without a number rather than estimated.

Prices are per 1M input tokens on the standard API tier and can change — always confirm current pricing with the provider.

our picks: the best ai models for coding right now

compare head-to-head

how the field got here

1. 2021GitHub Copilot preview Autocomplete-in-the-editor goes mainstream.
2. 2023ChatGPT + GPT-4, then Cursor Chat-based coding and the first AI-native editor arrive.
3. Aug 2024SWE-bench Verified launches A human-validated benchmark of real GitHub issues sets an honest bar.
4. Oct 2024Claude 3.5 Sonnet hits ~49% Agents begin resolving real issues, not just snippets.
5. 2025Terminal agents Claude Code and Codex CLI move AI out of the editor into the whole repo.
6. Apr–Jun 2026Open weights close the gap DeepSeek V4, Kimi K2.6 and peers cluster at ~80% Verified — for pennies.
7. 2026Verified saturates in the mid-80s SWE-bench Pro and Terminal-Bench become the real differentiators.
8. Jul 2026Claude Fable 5 returns Restored after a 20-day export-control suspension; retakes SWE-bench Pro at 80.3%.
9. Jun 30 2026Meituan open-sources LongCat-2.0 A 1.6T MoE coder trained entirely on domestic Chinese chips; vendor-reported 59.5% SWE-bench Pro, awaiting independent eval.
10. Jul 25 2026Claude Opus 5 takes #1 vals.ai measures 97.00% ±0.76 on the neutral harness, one day after launch. A statistical tie with GPT-5.6 Sol and Claude Fable 5.

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