SWE-Bench Pro 2026 — The Realistic Benchmark for AI Coding Models
GPT-5.3-Codex 56.8% SOTA, GPT-5.5 58.6%, GPT-5.2-Codex 80% on Verified, Opus 4.7 is now the Fast Mode default. What the benchmark is, how to read the scores, how to pick by scenario.
SWE-Bench Pro vs Verified — The Benchmark
SWE-Bench Verified (popular from 2024): ~500 human-verified GitHub issue-fix tasks. SWE-Bench Pro (mainstream from 2026): same approach but harder — longer context, more files touched, closer to real PR workflows. Verified plateaued near 80% (5.2-Codex), Pro still has ~40 points of headroom and is now the canonical benchmark. Terminal-Bench 2.0 measures terminal-agent tasks; OSWorld measures GUI-operating tasks; GDPval measures professional knowledge.
2026 Q1-Q2 Score Evolution
GPT-5.2-Codex (2026-01-14): SWE-Bench Verified 80.0%, Pro 56.4%. GPT-5.3-Codex (2026-02-05): Pro 56.8% (new high), Terminal-Bench 2.0 77.3%, OSWorld 64.7%. GPT-5.5 (2026-Q2): Pro 58.6% retakes the top. Claude Opus 4.7 (2026-Q2): now the Fast Mode default; widely regarded as the strongest at deep refactors. Gemini 3 (2026-03): strong in Google's ecosystem but trails on Pro.
Cross-Model Comparison by Scenario
Pure Pro score: GPT-5.5 > GPT-5.3-Codex > GPT-5.2-Codex > Opus 4.7 (lower on Pro but deepest real-world refactors). By scenario: 1) Next.js/React multi-file edits → Cursor Composer 2.0; 2) Python/Django deep refactors → Claude Opus 4.7 / Claude Code; 3) Rust / terminal agent / long-horizon PRs → GPT-5.3-Codex; 4) Cross-domain agents (code + research + writing) → GPT-5.5.
Unified Access to All Major Models via QCode
QCode.cc provides a single transparent API gateway to all major coding models from inside China — Claude (Opus 4.7 / Sonnet), GPT (5.5 / Codex family), Gemini (3) and more. One subscription, usage-based billing. In Claude Code, Codex CLI, Cursor, Cline, Continue, switching models is just changing base URL and model id — configure once, run everywhere.
FAQ
Is 58.6% on SWE-Bench Pro high? How should I read it?
Pro is significantly harder than Verified; 58.6% is the all-time high. Roughly speaking: out of 5 real GitHub PR tasks, the model completes 3 end-to-end. With human review and retries in production, this represents a major productivity gain.
Why does general-purpose GPT-5.5 beat coding-specialized 5.3-Codex on Pro?
Pro contains a meaningful share of subtasks that require cross-domain reasoning and document comprehension — 5.5's generalist nature helps there. For pure long-horizon coding, terminal agents, and OS-GUI tasks, 5.3-Codex is still the pick (SOTA on Terminal-Bench / OSWorld).
Opus 4.7's Pro score isn't published — how do I evaluate it?
Anthropic has internal numbers but hasn't published full Pro results. Community measurements on long Python refactors and long-context RAG show Opus 4.7 leading. Practical advice: pick by scenario — Python depth → Opus 4.7; pure benchmarks → GPT-5.5/5.3-Codex.
Access All Major Coding Models Through QCode
GPT-5.5 / 5.3-Codex / Opus 4.7 / Gemini 3 through a single QCode gateway. Transparent from inside China, usage-based.
Start Your QCode Plan