Benchmark · agentic

SWE-bench Pro

19 results 13 models

A harder, contamination-resistant version of SWE-bench from Scale AI. It draws ~1,865 real GitHub issues from held-out enterprise, startup and open-source repositories the models were not trained on, and the fixes typically span several files.

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Example
Given a real issue and the full repository, the agent must produce a patch — often touching multiple files — so the project's hidden test suite passes. Problems come from private and commercial repos specifically to avoid training-data leakage.
Scoring
% of issues resolved, reported as pass@1 (one attempt). Higher is better. Scores run lower than on SWE-bench Verified because the tasks are harder and previously unseen.
Verification
Fully automatic and held-out: the generated patch is applied and the repository's real hidden unit tests run in a sandbox. A task counts only if every target test passes and nothing else regresses.
Why it matters
Built to resist the contamination and saturation that inflate scores on SWE-bench Verified, so it is a cleaner signal of real, generalizing coding-agent skill on code the model has never seen.
Worked example
Task
Repository acme/payments at a fixed base commit. Issue: Money('0.05').allocate([1, 1, 1]) drops a cent — the three parts sum to $0.04, not $0.05. Produce a patch so an allocation always sums back to the original amount.
Solution
--- a/payments/money.py
+++ b/payments/money.py
@@ def allocate(self, ratios):
-        total = sum(ratios)
-        return [self._from_minor(self.minor * r // total) for r in ratios]
+        total = sum(ratios)
+        shares = [self.minor * r // total for r in ratios]
+        remainder = self.minor - sum(shares)
+        for i in range(remainder):
+            shares[i] += 1
+        return [self._from_minor(s) for s in shares]
Walkthrough
Integer division truncates, so the leftover minor units (cents) were silently lost. The fix computes each share, then distributes the remainder one unit at a time to the first parts, guaranteeing the allocated parts always sum to the total. The hidden tests assert sum(m.allocate(ratios)) == m across several amounts and ratios.
0 21.5 43 64.5 86 2025-12-11 2026-04-15 2026-08-19 GPT-5.2 Thinking · 55.6 · 2025-12-11 GPT-5.2 Thinking · 55.6 · 2025-12-11 GPT-5.2 Thinking · 55.6 · 2025-12-11 GPT-5.5 · 58.6 · 2026-04-23 GPT-5.5 · 58.6 · 2026-04-23 Claude Opus 4.7 · 64.3 · 2026-04-23 Qwable-v1 · 80.3 · 2026-06-16 Grok 4.5 · 64.7 · 2026-07-08 Xiaomi-Robotics-U0 · 63.2 · 2026-07-17 Gemini 3.5 Flash-Lite · 54.2 · 2026-07-21 Gemini 3.5 Flash-Lite · 54.2 · 2026-07-21 Gemini 3.5 Flash-Lite · 54.2 · 2026-07-27 OpenForgeClaw · 31.7 · 2026-07-24 OpenForgeClaw · 55.9 · 2026-07-24 KAT-Coder-V2.5 · 65.2 · 2026-07-26 Qwen3.8-Max · 67.7 · 2026-08-03 Argus · 78 · 2026-08-06 Muse Glimmer · 51.2 · 2026-08-10 Ornith-1.5 · 65.1 · 2026-08-19
GPT-5.2 Thinking GPT-5.5 Claude Opus 4.7 Qwable-v1 Grok 4.5 Xiaomi-Robotics-U0 Gemini 3.5 Flash-Lite OpenForgeClaw KAT-Coder-V2.5 Qwen3.8-Max Argus Muse Glimmer Ornith-1.5
Timeline
Date Model Score Source
2026-08-19 Ornith-1.5 65.1% Ornith-1.5 releases 9B, 35B-A3B, and 397B models with self-improving training
2026-08-10 Muse Glimmer 51.2% Meta releases Muse Glimmer, a 30B open-weights agentic model running on one consumer GPU
2026-08-06 Argus 78.0% Argus: A General-Purpose Agentic Runtime for Long-Horizon Reasoning
2026-08-03 Qwen3.8-Max 67.7% Alibaba releases Qwen3.8-Max, a 2.4T-parameter MoE model
2026-07-27 Gemini 3.5 Flash-Lite 54.2% Google releases Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber
2026-07-26 KAT-Coder-V2.5 65.2% KwaiKAT releases KAT-Coder-V2.5, an agentic coding model trained on 100,000+ verifiable environments
2026-07-24 OpenForgeClaw 55.9% OpenForgeRL enables end-to-end training of harness-based agents in any environment
2026-07-24 OpenForgeClaw 31.7% OpenForgeRL enables end-to-end training of harness-based agents in any environment
2026-07-21 Gemini 3.5 Flash-Lite 54.2% Google releases Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber for agentic workloads
2026-07-21 Gemini 3.5 Flash-Lite 54.2% Google releases Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber
2026-07-17 Xiaomi-Robotics-U0 63.2% Xiaomi releases U0, a 38B-parameter model for unified embodied synthesis
2026-07-08 Grok 4.5 64.7% xAI releases Grok 4.5 with low cost and mixed benchmark performance
2026-06-16 Qwable-v1 80.3% Qwable-v1 Released as Distillation of Claude Fable-5
2026-04-23 GPT-5.5 58.6% OpenAI releases GPT-5.5 and GPT-5.5 Pro in API
2026-04-23 GPT-5.5 58.6% OpenAI releases GPT-5.5 with agentic capabilities and doubled API pricing
2026-04-23 Claude Opus 4.7 64.3% OpenAI releases GPT-5.5 with agentic capabilities and doubled API pricing
2025-12-11 GPT-5.2 Thinking 55.6% OpenAI releases GPT-5.2, outperforming Gemini 3 in coding and reasoning benchmarks
2025-12-11 GPT-5.2 Thinking 55.6% OpenAI introduces GPT-5.2 with state-of-the-art professional knowledge work capabilities
2025-12-11 GPT-5.2 Thinking 55.6% OpenAI introduces GPT-5.2 with improved coding, long-context, and reasoning capabilities