SWE-bench vs LiveBench: Which Should You Use in 2026?
SWE-bench is the benchmark to use in 2026 if you need to measure how well an AI model or coding agent fixes real GitHub issues: it ships 2,294 task instances from 12 Python repositories, plus a human-validated 500-task Verified subset where top models like Claude Opus 5 now score around 96%. LiveBench is not covered by the data behind this comparison, so no factual claims about it can be made here. If your goal is evaluating coding agents on real-world bug-fixing, SWE-bench is the benchmark with a documented, actively updated track record.
TL;DR
- SWE-bench evaluates LLMs on real-world GitHub issues: given a codebase and an issue, the model must generate a patch, and success is decided by running unit tests.
- The original SWE-bench test set contains 2,294 instances drawn from 12 Python repositories, including django (850), sympy (386) and scikit-learn (229).
- SWE-bench Verified is a 500-task, human-validated subset created with OpenAI in August 2024; it is the most-cited variant in 2026 leaderboards.
- Other variants include Lite (300 instances, cheaper to run), Multilingual (300 tasks across 42 repos and 9 languages), Multimodal (480 tasks with visual elements) and Bash Only.
- On Verified as of October 2026, Claude Opus 5 leads at 96%, with Claude Mythos 5 at 95.5% and Claude Fable 5 at 95%; Grok 4.6 is reported at 95.6% elsewhere.
- Running SWE-bench locally is resource-intensive: the team recommends an x86_64 machine with at least 120GB free storage, 16GB RAM and 8 CPU cores; cloud options include sb-cli (AWS) and Modal.
- A February 2026 leaderboard update from the SWE-bench team provided results not self-reported by model labs, using the same mini-SWE-agent harness and system prompt for every model.
Head to head
| Dimension | LiveBench | SWE-bench |
|---|---|---|
| Pricing | Not established in this comparison | Free, open source; datasets on Hugging Face 🏆 |
| Task realism | Not established in this comparison | Real GitHub issues from 12 Python repos; patches graded by unit tests 🏆 |
| Eval variants | Not established in this comparison | Full (2,294), Verified (500), Lite (300), Multilingual (300), Multimodal (480), Bash Only 🏆 |
| Grading method | Not established in this comparison | Automated: unit tests run against the generated patch in Docker 🏆 |
| Reproducibility | Not established in this comparison | Docker harness; needs ~120GB storage, 16GB RAM, 8 cores; cloud via sb-cli/Modal 🏆 |
| Freshness of results | Not established in this comparison | Leaderboard updated through 2026 (Feb 2026 full run; Oct 2026 third-party tables) 🏆 |
| Independence of results | Not established in this comparison | Mix of team-run evals and lab self-reports; Bash Only run was not self-reported 🏆 |
The contenders
SWE-bench is an open-source benchmark, introduced in the ICLR 2024 oral paper 'Can Language Models Resolve Real-World GitHub Issues?', that evaluates large language models on real software engineering work. Given a codebase and an actual GitHub issue, the model must generate a patch that resolves the problem, and the patch is graded by running the repository's unit tests — so a model cannot pass by writing plausible-looking code; it has to make the tests go green. The original test set holds 2,294 instances from 12 Python repositories such as Django, SymPy, scikit-learn, matplotlib and pytest. Several curated variants exist: SWE-bench Verified (500 human-validated tasks, produced with OpenAI Preparedness in August 2024), SWE-bench Lite (300 instances for cheaper evaluation), Multilingual (300 tasks from 42 repositories across 9 languages), Multimodal (480 tasks described with visual elements, fully open-sourced in September 2026), and a Bash Only view where every model runs in the same mini-SWE-agent environment. Evaluation is Docker-based for reproducibility, with cloud submission via sb-cli or Modal. The project is active in 2026, releasing ProgramBench in May 2026 and CodeClash in November 2025, and is supported by Open Philanthropy, AWS, Modal, a16z, OpenAI and Anthropic.
pricing 0
- + Tasks are real GitHub issues, making it the closest thing to a real-world software engineering test
- + Grading is objective: unit tests decide whether a patch resolves the issue
- + Multiple subsets let you trade cost against coverage (Lite at 300 vs full at 2,294 instances)
- + Bash Only / mini-SWE-agent setup removes harness advantage so models are compared fairly
- + Free and open source, with datasets loadable from Hugging Face (Verified is a 2.1MB Parquet file)
- + Actively maintained with new evals (ProgramBench, CodeClash, Multimodal v2) through 2026
- − Local evaluation is resource-intensive: 120GB storage, 16GB RAM and 8 CPU cores recommended
- − Dual evaluation of harness and model means labs report results with different methodologies, complicating comparisons
- − The official leaderboard is infrequently updated, per the February 2026 review
- − Many leaderboard rows are lab self-reports, which third parties compile with caveats
- − Originally Python-only (12 Python repositories), though Multilingual extends to 9 languages
LiveBench appears in this comparison as the second benchmark option named in the title. The material available for this page did not include verifiable details about LiveBench itself — its task design, scoring method, update cadence, or how its results are produced and published — so no specific factual claims about it can be made here. Readers evaluating LiveBench should consult its official site and documentation directly, and apply the same checks this page applies to SWE-bench: are tasks fresh and contamination-resistant, is grading objective or judgment-based, are results independently run or self-reported, and how expensive is the evaluation to reproduce. Those four questions, applied consistently, are what separate a benchmark you can trust from one you cannot, regardless of which benchmark you pick.
pricing See pricing
Verdict
Winner: SWE-bench
SWE-bench is the only one of the two with documented, verifiable details in this comparison: multiple task subsets (Verified, Lite, Multilingual, Multimodal, Bash Only), a public leaderboard refreshed with independent runs, and wide adoption by model labs. LiveBench could not be evaluated on any factual dimension here.
| Option | Overall | Breakdown |
|---|---|---|
| SWE-bench | 8 | : 9: 9: 9: 8: 7: 8 |
| LiveBench | 0 | : 0: 0: 0: 0: 0: 0 |
What SWE-bench actually measures — and why that matters for your team
Choosing the right SWE-bench subset in 2026
Reading SWE-bench scores without being misled
From benchmarking models to benchmarking skills
Which should you pick?
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FAQ
What is the difference between SWE-bench and SWE-bench Verified?
SWE-bench is the original benchmark of 2,294 real GitHub issues from 12 Python repositories. SWE-bench Verified, released in August 2024 through a collaboration with OpenAI's Preparedness team, is a subset of 500 problems that real software engineers confirmed are solvable. Because every task is human-validated, Verified removes noisy or impossible instances from the original set, which is why it has become the headline number most labs and leaderboards report in 2026.
How much does it cost to run SWE-bench evaluations?
SWE-bench itself is free and open source — the datasets are downloadable from Hugging Face (Verified is a 2.1MB Parquet file) and the harness is on GitHub. The real cost is compute: local evaluation is resource-intensive, with the team recommending an x86_64 machine with at least 120GB of free storage, 16GB of RAM and 8 CPU cores. If you can't provision that, cloud-based options exist: sb-cli runs evaluations on AWS, and Modal supports running the full evaluation in the cloud.
Which models score highest on SWE-bench Verified in 2026?
Third-party tables from October 2026 put Claude Opus 5 (Anthropic) first at 96%, followed by Claude Mythos 5 at 95.5% and Claude Fable 5 at 95%; another leaderboard reports Grok 4.6 at 95.6%. Among open-weight models, DeepSeek V4 Pro sits at 80.6% under an MIT license and Kimi K2.6 at 80.2%. Treat self-reported vendor numbers with caution — the February 2026 team-run leaderboard, which used the same mini-SWE-agent harness and system prompt for every model, is the more trustworthy cross-model comparison.
Is SWE-bench only useful for Python code?
The original and Verified subsets draw from 12 Python repositories, so scores on those reflect Python bug-fixing. However, SWE-bench Multilingual extends evaluation to 300 tasks from 42 repositories across 9 programming languages, and SWE-bench Multimodal (480 tasks, fully open-sourced in September 2026) adds issues described with visual elements. If your codebase isn't Python, look at Multilingual results rather than Verified alone.
How can I evaluate the AI skills I attach to my coding agent, not just the model itself?
Benchmarks like SWE-bench measure the model; the skills (SKILL.md instruction packages) you attach to agents like Claude Code, Codex, Cursor or Gemini CLI are a separate quality question — and published research found roughly 26% of published skills carry at least one vulnerability. SkillzBench is being built to address exactly this: a free directory that crawls and dedupes skills from GitHub, pins versions to commit SHAs, runs security scans and license checks, and benchmarks each skill with and without it to publish a Lift score in percentage points, plus a free MCP server to load only the skills you choose. It's pre-launch — the waitlist is open now.