Benchmarks That Matter
Not all benchmarks are equal. Some are saturated, some are contaminated, and some don’t reflect real-world coding. Here’s what matters.
The Essential Three
Section titled “The Essential Three”These benchmarks best predict real-world coding performance:
SWE-bench (and variants)
Section titled “SWE-bench (and variants)”What it tests: Resolving real GitHub issues from popular open-source projects.
| Variant | Difficulty | What It Measures |
|---|---|---|
| SWE-bench Verified | Medium | 500 human-validated tasks, ~1 hour each |
| SWE-bench Pro | Hard | Complex multi-file changes, commercial codebases |
Why it matters: Unlike synthetic puzzles, these are actual bugs that humans filed and fixed. Models must understand existing code, locate the problem, and generate correct patches.
Current scores (snapshot: Jan 2026):
- Claude Opus 4.5: 80.9%
- GPT-5.2: 80.0%
- DeepSeek V3.2: 73.1%
Caveat: SWE-bench Verified is becoming saturated. SWE-bench Pro (where top models score <45%) is the new bar.
| Link | swebench.com |
LiveCodeBench
Section titled “LiveCodeBench”What it tests: Competitive programming problems from LeetCode, AtCoder, CodeForces.
Why it matters: Continuously updated with new problems released after model training cutoffs. This makes it contamination-resistant — models can’t memorize solutions.
Key features:
- Time-segmented evaluation (only test on post-training problems)
- Multi-capability: generation, self-repair, test prediction
- Updated monthly
Current scores (snapshot: Jan 2026):
- Kimi K2 Thinking: 83.1%
- Gemini 3 Pro: 79.7%
- Grok 4: 79.0%
| Link | livecodebench.github.io |
Aider Polyglot
Section titled “Aider Polyglot”What it tests: Code editing across 6 languages (C++, Go, Java, JavaScript, Python, Rust).
Why it matters: Tests editing existing code, not just generating from scratch. Measures if models can follow instructions and make targeted changes without breaking things.
Current scores (snapshot: Jan 2026):
- Claude Opus 4.5: 89.4%
- GPT-5 (high): 88.0%
- Gemini 3 Pro: 82.2%
| Link | aider.chat/docs/leaderboards |
Supplementary Benchmarks
Section titled “Supplementary Benchmarks”These provide additional signal but shouldn’t be used alone:
HumanEval / MBPP
Section titled “HumanEval / MBPP”What it tests: Simple function-level code generation (e.g., “write a function that reverses a string”).
Limitation: Too easy — top models score 95%+. Saturated and likely contaminated.
Use for: Sanity checks, not model selection.
Tencent AutoCodeBench
Section titled “Tencent AutoCodeBench”What it tests: 3,920 problems across 20 programming languages, auto-generated to avoid contamination.
Why interesting:
- Multilingual coverage beyond Python
- Three difficulty tiers (easy/medium/hard)
- Claude Opus 4 leads at 52.4%
Caveat: Newer benchmark, less adoption.
| Link | github.com/Tencent-Hunyuan/AutoCodeBenchmark |
SimpleBench
Section titled “SimpleBench”What it tests: Common-sense reasoning that humans find easy but LLMs struggle with.
Why interesting: Exposes reasoning failures that coding benchmarks miss. Tests spatial, social, and adversarial reasoning.
Current scores (snapshot: Jan 2026):
- Humans: ~85%
- Gemini 3 Pro: 76.4%
- Other top LLMs: ~62%
Use for: Understanding model limitations, not coding selection.
| Link | simple-bench.com |
Behavioral Benchmarks
Section titled “Behavioral Benchmarks”These test how models behave, not just what they can do:
SnitchBench
Section titled “SnitchBench”What it tests: Whether models report wrongdoing when given evidence of corporate malfeasance.
Why interesting: Tests model alignment and tendency to “take initiative” on ethical issues. Relevant for autonomous agents with real-world permissions.
Finding: Grok leads, followed by Claude models. Both are significantly more likely to report issues to authorities than other models.
| Link | snitchbench.com |
Red Flags: Benchmarks to Ignore
Section titled “Red Flags: Benchmarks to Ignore”| Benchmark | Problem |
|---|---|
| HumanEval alone | Saturated (95%+ scores), likely contaminated |
| Self-reported evals | Vendor benchmarks often cherry-pick favorable tests |
| Single-number scores | ”Best at coding” claims without methodology |
How to Use Benchmarks
Section titled “How to Use Benchmarks”- Check multiple benchmarks — No single test captures everything
- Prefer contamination-resistant — LiveCodeBench, SWE-bench Pro
- Match to your use case — Agentic work? SWE-bench. Editing? Aider Polyglot.
- Verify recency — Benchmarks from 6+ months ago may be outdated
Aggregators
Section titled “Aggregators”| Site | What It Tracks |
|---|---|
| Artificial Analysis | Speed, price, quality combined |
| LLM Stats | Multiple benchmark leaderboards |
| Chatbot Arena | Human preference (blind voting) |
Data snapshot: January 2026. Reviewed: February 2026.