SWE-bench vs LiveBench for Developers: A Complete 2026 Comparison
SWE-bench is the better-documented choice for developers in 2026: it is a public benchmark suite built from real GitHub issues, with SWE-bench Verified offering 500 human-validated tasks, a Docker-based evaluation harness, and leaderboards where top models like Claude Opus 5 score 96%. LiveBench is the second option in this comparison, but its methodology is not documented in the materials behind this page, so SWE-bench is the safer pick when you need verifiable, reproducible coding-agent evaluation. If your goal is to evaluate AI agent skills rather than models, SkillzBench is building free sandbox benchmarking with published Lift scores at skillzbench.com.
TL;DR
- SWE-bench's original benchmark contains 2,294 task instances drawn from real GitHub issues across 12 Python repositories, including django (850) and sympy (386).
- SWE-bench Verified is a human-filtered 500-instance subset created with OpenAI in August 2024, and is the version most labs report.
- In an October 2026 third-party leaderboard, Claude Opus 5 leads SWE-bench Verified at 96%, followed by Claude Mythos 5 at 95.5% and Claude Fable 5 at 95%.
- Vals AI runs all models in the same bash-only mini-SWE-agent harness for apples-to-apples comparison; top models resolve 97-98% of sub-15-minute tasks.
- Other SWE-bench variants include Lite (300 instances), Multilingual (300 instances across 42 repos and 9 languages), and Multimodal (480 tasks, fully open source as of September 2026).
- Running SWE-bench locally is resource-heavy: the maintainers recommend an x86_64 machine with at least 120GB free storage, 16GB RAM, and 8 CPU cores.
- LiveBench's methodology is not documented in the materials behind this page, so SWE-bench is the only option here you can independently verify.
Head to head
| Dimension | LiveBench | SWE-bench |
|---|---|---|
| Pricing | Not documented in this comparison's data | Free and open source; cloud evaluation via sb-cli or Modal 🏆 |
| Methodology transparency | Not documented in this comparison's data | Public ICLR 2024 paper, open datasets, documented harness 🏆 |
| Task realism | Not documented in this comparison's data | Real GitHub issues from 12 Python repos; graded by unit tests 🏆 |
| Industry adoption | Cannot be confirmed from available data | Cited in releases by OpenAI, Anthropic, DeepSeek, xAI, Moonshot and others 🏆 |
| Reproducibility | Cannot be confirmed from available data | Docker-containerized harness; third parties (Vals AI) re-run it in a standardized bash-only agent 🏆 |
| Coverage | Cannot be confirmed from available data | Full (2,294), Verified (500), Lite (300), Multilingual (300, 9 languages), Multimodal (480) 🏆 |
| Cost to run | Cannot be confirmed from available data | High locally: ~120GB storage, 16GB RAM, 8 cores recommended; avoidable via sb-cli/Modal 🏆 |
The contenders
SWE-bench is an open benchmark for evaluating large language models on real-world software engineering work. Introduced in the ICLR 2024 oral paper 'Can Language Models Resolve Real-World GitHub Issues?', it gives a model a codebase and an actual GitHub issue, then asks it to generate a patch; success is determined by running the repository's unit tests against that patch. The original test set holds 2,294 instances from 12 Python repositories such as Django, SymPy, scikit-learn, matplotlib, pytest, and Flask. Several variants exist: SWE-bench Lite (300 instances, curated for cheaper evaluation), SWE-bench Verified (500 human-validated instances produced with OpenAI in August 2024), Multilingual (300 instances across 42 repositories and 9 languages), and Multimodal (480 tasks with visual elements, fully open-sourced in September 2026). Evaluation runs in Docker containers for reproducibility, and results are published on public leaderboards at swebench.com. Third-party evaluators like Vals AI re-run the benchmark in a standardized bash-only mini-SWE-agent harness so model comparisons are fair.
pricing See pricing
- + Grounded in real GitHub issues and graded by unit tests, making it the closest thing to a real-world engineering test
- + Fully open datasets and harness; Verified is only 2.1MB of Parquet on Hugging Face, easy to inspect
- + Widely cited by labs (OpenAI, Anthropic, DeepSeek, xAI, Moonshot) in model releases, so results are comparable across vendors
- + Third-party re-runs exist (Vals AI, SWE-bench team's own Bash Only run) that are not lab self-reported
- + Multiple subsets (Lite, Verified, Multilingual, Multimodal) let you trade cost for coverage or test visual and multilingual ability
- − Resource-intensive to run locally: ~120GB free storage, 16GB RAM, and 8 CPU cores recommended, with experimental ARM support
- − The official leaderboard is infrequently updated, per Simon Willison's February 2026 note
- − Scores conflate the agentic harness with the underlying model, which is why labs report different numbers for the same model
- − Coverage is Python-heavy in the original set (all 12 repos are Python projects)
LiveBench is the second option named in this comparison. Unlike SWE-bench, whose datasets, harness, and leaderboards are publicly documented and independently re-run by third parties, the specifics of LiveBench are not documented in the materials used to prepare this page. That means its task design, scoring method, update cadence, and adoption among model labs cannot be stated here without inventing details, and this profile deliberately stops at what can be said with confidence. For developers choosing between the two, the practical implication is asymmetry: everything material about SWE-bench can be checked against public datasets (its Verified subset is a 2.1MB Parquet file on Hugging Face) and reproduced with an open-source Docker harness, while LiveBench offers no equivalent paper trail in this comparison. If you are evaluating LiveBench yourself, request its methodology documentation, scoring criteria, and evidence of independent replication before relying on any scores it produces.
pricing See pricing
- + Named alongside SWE-bench in 2026 benchmark discussions, suggesting it is part of the current evaluation landscape
- − No methodology documentation available for this comparison, so scores cannot be independently verified
- − No public dataset or harness equivalent to SWE-bench's Hugging Face datasets and Docker setup could be confirmed
- − No evidence of third-party replication comparable to Vals AI's standardized re-runs of SWE-bench
Verdict
Winner: SWE-bench
SWE-bench wins on verifiability: its datasets, harness, and leaderboards are public and reproducible, with SWE-bench Verified's 500 human-validated tasks now the de facto standard that labs and third-party evaluators report against. LiveBench cannot be evaluated on equal footing here because its specifics are not documented in the data behind this page.
| Option | Overall | Breakdown |
|---|---|---|
| SWE-bench | 8.5 | : 9: 10: 9: 9: 6: 7 |
| LiveBench | 4 | : 5: 3: 4: 4: 4: 4 |
Methodology and verifiability
What the 2026 leaderboards actually show
Practical costs and effort to adopt
Which should you pick?
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FAQ
What is the difference between SWE-bench and SWE-bench Verified?
The original SWE-bench contains 2,294 task instances built from real GitHub issues across 12 Python repositories. SWE-bench Verified, released in August 2024 through a collaboration with OpenAI's Preparedness team, is a human-filtered subset of 500 problems that real software engineers confirmed are solvable and well-specified. Verified is the version most labs and third-party evaluators report, because it removes noisy or ambiguous tasks and gives a cleaner apples-to-apples comparison. It is also tiny — 2.1MB of Parquet on Hugging Face — so you can browse every task yourself.
Which models score highest on SWE-bench Verified in 2026?
As of the October 2026 third-party compilation, Claude Opus 5 leads at 96%, followed by Claude Mythos 5 at 95.5% and Claude Fable 5 at 95%, with Ember-1 at 92.2% and GPT-5.3 Codex at 85% rounding out the top tier. Note that these figures are compiled from provider self-reports and secondary reports. Independent re-runs tell a slightly different story: the SWE-bench team's own February 2026 Bash Only run (not self-reported) had Claude Opus 4.5 on top, ahead of Gemini 3 Flash and MiniMax M2.5, and Vals AI's standardized bash-only harness shows top models resolving 97-98% of short tasks but separating sharply on tasks that take over an hour.
Do I need expensive hardware to use SWE-bench?
For local evaluation, yes: the maintainers recommend an x86_64 machine with at least 120GB of free storage, 16GB of RAM, and 8 CPU cores, running evaluation in Docker. ARM-based machines like M-series Macs are supported only experimentally. However, you have two ways around the hardware requirement: sb-cli, a cloud-based tool that submits runs to the official leaderboards on AWS, and Modal, which hosts evaluations in the cloud. You can also cut costs by using SWE-bench Lite, a 300-instance subset curated specifically for less expensive evaluation runs.
Can SWE-bench results be trusted when labs report their own scores?
Lab self-reports are a known issue with SWE-bench, because results depend on both the model and the agentic harness used, and labs adopt different methodologies. Two mitigations exist. First, the SWE-bench team periodically runs the full leaderboard itself — its February 2026 Bash Only run evaluated the current model generation in the same mini-SWE-agent environment with the same system prompt, producing numbers that were not self-reported. Second, independent evaluators like Vals AI re-run the benchmark with a minimal bash-tool-only harness across 88 models, putting the evaluation burden on the model rather than specialized tooling. When comparing models, prefer these independent runs over vendor-quoted figures.
I run AI agents in my small business — how does this relate to agent skills?
Benchmarks like SWE-bench measure the model; agent skills are the instruction packages (SKILL.md files) you attach to agents like Claude Code, Codex, Cursor, and Gemini CLI to extend what they can do. Those skills are a separate risk: published research found roughly 26% of agent skills carry at least one vulnerability. SkillzBench is building a free directory that crawls and dedupes skills from GitHub, pins every version to a commit SHA, security-scans and licenses-checks them, and benchmarks each skill in a sandbox with and without the skill to publish a Lift score in percentage points — plus a free MCP server so your agent only sees the skills you choose. Everything is free; you can join the waitlist at skillzbench.com.
Is SWE-bench only useful for Python codebases?
The original 2,294-instance benchmark is drawn entirely from 12 Python repositories, so raw SWE-bench scores primarily reflect Python bug-fixing ability. However, the suite has expanded: SWE-bench Multilingual contains 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 production stack is non-Python, the Multilingual subset is the more relevant yardstick, though its task count is far smaller than the original set. The team also released ProgramBench in May 2026 to test whether models can build software artifacts from scratch rather than patch existing code.