SWE-bench benchmark leaderboard in 2026: best AI for coding

Ever wondered which AI model is best for coding?

Coding benchmarks score models on programming tasks. Think of them as a stress test for bug fixing, repo navigation, and patch quality.

Benchmarks have changed fast. Older tests like HumanEval focus on writing small functions from prompts. Useful, but far from how teams build software in 2026.

SWE-bench is closer to real work. It measures whether a model can resolve real GitHub issues in real codebases, then pass the project tests.

Benchmark data last checked: January 2026

Why should you care?

SWE-bench is not just “coding trivia”. It is a great way to evaluate the best AI for coding.

It is one of the best proxies we have for:

• real repo understanding

• multi-file bug fixing

• test-driven reliability

So if your work involves software, data, automation, or internal tools, these scores are a useful signal when picking an AI model.

Not sure which AI model to pick?

Read our full guide to the best LLMs

Which AI model is best for coding in 2026?

SWE-bench shows something clear: the top tier is tight, but the drop after that is steep.

The top tier stays strong

• Gemini 3 pro and Claude opus 4.5 tie at 74%

• GPT-5.2 is right behind at 72%

At this level, the question is no longer “can it fix GitHub issues?”

It is “how often does it fix them on the first try without you babysitting?”

the middle tier is usable, but you will feel the friction

DeepSeek V3.2-exp scores 60% and Qwen 3 scores 55%.

That is still strong. But it usually means:

• more retries

• more small mistakes

• more time spent steering the model

For many business codebases, this tier can still be “good enough”. But you should expect more review and cleanup.

Llama 4 is the outlier

Llama 4 scores 21%. That is not a small gap. It is a different league.

This does not mean it is useless for all coding. It means it struggles with what SWE-bench measures: repo-level bug fixing with tests as the judge.

Some models were excluded because they are not in the data.

We could not verify Grok 4.1, Ernie 5.0, or Mistral 3 in the dataset used for this leaderboard.

What is the SWE-bench benchmark?

SWE-bench was introduced by Jimenez et al. (2024) as a benchmark based on real GitHub issues from widely used Python repositories.

Each task requires a model to understand the issue, modify the codebase, and produce a patch that passes the project’s test suite.

Unlike older benchmarks, SWE-bench measures repo-level debugging, multi-file changes, and test-driven correctness.

What is the SWE-bench Verified?

The original SWE-bench benchmark had a problem: many tasks were not fully fair.

Some issues were vague, and some unit tests demanded exact warning messages or behaviors never mentioned in the GitHub issue.

To fix this, OpenAI and the benchmark authors released SWE-bench Verified.

It is a smaller, human-audited set of 500 tasks that are clear, solvable, and reliably graded.

This makes the benchmark more reliable for comparing modern coding agents.

What is the HumanEval benchmark?

HumanEval tests how well LLMs can generate correct code based on docstrings.

It was introduced by Chen et al. (2021) as a way to evaluate a model’s coding ability using real-world programming tasks.

The test includes 164 coding problems that consist of:

• Function signatures

• Docstrings

• Code bodies

• Unit tests

The final HumanEval score is the average accuracy of the LLM across all tasks.

This benchmark has largely been replaced by the SWE-bench now.

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