What can SWE-bench Verified do?

real-world github issue resolution evaluation, multi-variant benchmark suite with specialized subsets, multimodal issue resolution with visual elements, agent framework integration and standardized evaluation interface, benchmark dataset curation and issue selection, leaderboard-based agent performance ranking and filtering, docker-sandboxed code execution and test validation, human-verified solvability filtering for verified subset, cost and efficiency metrics tracking and visualization, per-repository and per-language performance breakdown, temporal trend analysis and model release date correlation, open-source benchmark infrastructure and local evaluation support, multi-language support via multilingual variant

SWE-bench Verified

BenchmarkFree

Human-verified benchmark for AI coding agents.

Open Source

/ 100

13 capabilities

Capabilities13 decomposed

real-world github issue resolution evaluation

Medium confidence

Evaluates AI coding agents' ability to autonomously resolve authentic GitHub issues from popular Python repositories by executing multi-step reasoning and code modification workflows in sandboxed Docker environments. The benchmark measures binary resolution outcomes (issue resolved or not) by validating that agent-generated code changes pass the repository's existing test suite, providing a task-oriented evaluation of end-to-end software engineering capability rather than isolated code generation.

Solves for

Measure how well an AI coding agent can handle real-world bug fixes and feature requests from production codebasesCompare different AI agents and models on their ability to navigate complex repositories and implement working solutionsEstablish baseline performance metrics for autonomous software engineering systems before deploymentIdentify which types of GitHub issues (by repository, complexity, or domain) are solvable by current AI agents

Best for

AI research teams developing and benchmarking autonomous coding agents

Model providers (OpenAI, Anthropic, open-source) evaluating coding capabilities across model versions

Software engineering teams assessing whether AI agents can augment their development workflows

Requires

Docker runtime for sandboxed code execution

Python 3.x environment with repository-specific dependencies

Access to GitHub issue data and corresponding repository code

Limitations

Binary metric with no partial credit — agents receive 0% for incomplete solutions even if they make significant progress toward resolution

Python-only for Verified subset (500 instances); separate Multilingual variant required for non-Python evaluation

Definition of 'resolved' not explicitly documented in provided material — likely requires passing test suite but exact criteria unknown

What makes it unique

Uses authentic, human-verified GitHub issues from production repositories with mandatory test suite validation in Docker sandboxes, ensuring agents must produce working code that integrates with real codebases rather than generating isolated code snippets. The Verified subset (500 instances) underwent explicit human verification to confirm solvability, reducing false negatives from unsolvable issues that plague broader benchmarks.

vs alternatives

More realistic than HumanEval or MBPP (synthetic tasks) because it requires agents to navigate real repository complexity, dependency management, and test validation; more reliable than full SWE-bench (2,294 instances) because human verification eliminates unsolvable issues that inflate baseline difficulty.

multi-variant benchmark suite with specialized subsets

Medium confidence

Provides four distinct benchmark variants (Verified: 500 instances, Lite: 300 instances, Full: 2,294 instances, Multilingual: 300 instances across 9 languages, Multimodal: 517 instances with visual elements) allowing evaluation at different cost/coverage tradeoffs and across different programming languages and modalities. Each variant maintains the same core task structure (resolve GitHub issues via code modification) but targets different evaluation scenarios — Verified for high-confidence results, Lite for rapid iteration, Full for comprehensive assessment, Multilingual for language coverage, and Multimodal for visual understanding.

Solves for

Run quick agent evaluations on Lite (300 instances) during development without full benchmark costConduct comprehensive evaluation on Full (2,294 instances) for publication-quality results with broader coverageEvaluate coding agents on non-Python languages using Multilingual variant (9 languages)Test agents' ability to understand and resolve issues involving visual elements (diagrams, screenshots) via Multimodal variant+1 more

Best for

Research teams with varying computational budgets — can start with Lite for rapid iteration, graduate to Full for final results

Model providers supporting multiple programming languages — Multilingual variant enables cross-language capability comparison

Teams evaluating agents on real-world issues that include visual documentation or diagrams

Requires

Agent framework capable of handling the specific variant's requirements (e.g., vision model for Multimodal)

Docker runtime for all variants (code execution in sandboxed environment)

Repository-specific dependencies for each instance (Python packages, language runtimes, build tools)

Limitations

Variants are separate benchmarks with different instance counts — cannot directly compare agent performance across variants (e.g., 65% on Verified does not equal 65% on Full)

Multilingual variant (300 instances) is significantly smaller than Verified (500) — may have higher variance and lower statistical power

Multimodal variant (517 instances) requires agents with vision capabilities — not all coding agents support multimodal input

What makes it unique

Offers four orthogonal benchmark variants (Verified, Lite, Full, Multilingual, Multimodal) with explicit cost/coverage tradeoffs documented on leaderboard visualizations, enabling researchers to choose evaluation scope based on computational budget and capability focus. The Verified subset is uniquely human-verified for solvability, reducing false negatives from unsolvable issues.

vs alternatives

More flexible than single-benchmark alternatives (e.g., HumanEval, MBPP) by offering cost-tiered variants; more comprehensive than language-specific benchmarks by providing Multilingual and Multimodal options in a unified evaluation framework.

multimodal issue resolution with visual elements

Medium confidence

The Multimodal variant (517 instances) includes GitHub issues that contain visual elements such as diagrams, screenshots, or images that are relevant to understanding and resolving the issue. This variant requires agents with vision capabilities (e.g., multimodal LLMs) to process both text and visual information, extending evaluation beyond text-only code understanding.

Solves for

Evaluate agents with vision capabilities on realistic issues that include visual documentationAssess whether agents can leverage visual information to better understand and resolve issuesTest multimodal LLMs on practical software engineering tasksIdentify challenges in visual understanding for coding tasks

Best for

Teams developing multimodal coding agents with vision capabilities

Model providers evaluating vision-language models on practical tasks

Researchers studying the role of visual information in software engineering

Requires

Agent framework with vision capabilities (multimodal LLM, vision encoder)

Image processing and embedding infrastructure

Docker environment capable of handling image data

Limitations

Multimodal variant is separate from Verified subset — cannot compare multimodal vs. text-only performance on same instances

Only 517 instances total — smaller than Verified (500) or Full (2,294), potentially insufficient for reliable metrics

Visual element types not documented — unclear what types of images/diagrams are included (screenshots, diagrams, charts, etc.)

What makes it unique

Extends benchmark to include GitHub issues with visual elements (diagrams, screenshots), requiring agents with vision capabilities to process both text and images. This is a unique extension that reflects real-world issues where visual documentation is relevant.

vs alternatives

More realistic than text-only benchmarks (e.g., HumanEval, MBPP) because real GitHub issues often include visual documentation; enables evaluation of multimodal agents that text-only benchmarks cannot assess.

agent framework integration and standardized evaluation interface

Medium confidence

SWE-bench defines a standardized evaluation interface that agent frameworks (SWE-agent, mini-SWE-agent, custom agents) must implement to be evaluated on the benchmark. This interface specifies how agents receive GitHub issues, interact with the repository, execute code modifications, and report results. The standardization enables fair comparison across different agent architectures and frameworks by ensuring all agents operate under the same constraints and evaluation protocol.

Solves for

Ensure fair comparison across different agent frameworks by standardizing evaluation interfaceEnable new agents to be evaluated on SWE-bench by implementing standard interfaceFacilitate reproducibility by defining exact evaluation protocolSupport both open-source and proprietary agents on same benchmark

Best for

Agent framework developers implementing SWE-bench evaluation support

Researchers comparing agents from different frameworks

Benchmark organizers ensuring fair evaluation across diverse agents

Requires

Agent framework implementation of standardized evaluation interface

Documentation of interface specification (not provided in material)

Limitations

Standardized interface specification not documented in provided material — cannot determine exact interface requirements

Interface may constrain agent design — agents must conform to interface rather than using optimal architecture

No documentation of how interface handles different agent paradigms (e.g., planning-based vs. reactive agents)

What makes it unique

Defines a standardized evaluation interface that all agents must implement, ensuring fair comparison across different frameworks and architectures. This standardization is critical for reliable benchmarking but is often overlooked in code generation benchmarks.

vs alternatives

More rigorous than benchmarks without standardized interfaces because it ensures all agents operate under identical constraints; enables fair comparison across diverse agent architectures.

benchmark dataset curation and issue selection

Medium confidence

SWE-bench curates GitHub issues from popular Python repositories, selecting issues that are suitable for autonomous resolution (e.g., bug fixes, feature requests, but excluding infrastructure-only changes or documentation-only updates). The curation process filters issues based on solvability, complexity, and relevance to software engineering tasks. The Verified subset (500 instances) underwent additional human verification to confirm solvability, while the Full set (2,294 instances) includes all curated instances without verification.

Solves for

Obtain a representative set of real-world software engineering tasks for agent evaluationEnsure benchmark instances are solvable by code modification (not infrastructure or documentation changes)Balance benchmark across different repositories and issue typesProvide multiple dataset sizes (Lite: 300, Verified: 500, Full: 2,294) for different evaluation scenarios

Best for

Benchmark organizers curating high-quality evaluation datasets

Researchers wanting realistic software engineering tasks for agent evaluation

Teams assessing whether benchmark instances are representative of their codebase

Requires

GitHub API access to retrieve issues and repository metadata

Curation infrastructure (filtering, deduplication, quality assessment)

Human annotators for Verified subset verification

Limitations

Repository selection criteria not documented — unclear what makes a repository 'popular' or suitable for inclusion

Issue selection criteria not documented — unclear what types of issues are included/excluded

Curation methodology not documented — cannot assess potential biases in issue selection

What makes it unique

Curates GitHub issues from popular repositories with explicit solvability filtering, ensuring benchmark instances are realistic and suitable for autonomous resolution. The Verified subset adds human verification to confirm solvability, providing a high-confidence evaluation set.

vs alternatives

More realistic than synthetic benchmarks (e.g., HumanEval, MBPP) because instances are real GitHub issues; more reliable than unfiltered issue collections because curation removes unsolvable instances.

leaderboard-based agent performance ranking and filtering

Medium confidence

Provides a web-based leaderboard (swebench.com) that ranks AI coding agents by resolution rate across multiple benchmark variants, with filtering capabilities by agent type (mini-SWE-agent, SWE-agent, OSS agents, all agents), model category (open-source vs. proprietary), scaffold type, and tags. The leaderboard visualizes performance across multiple dimensions including resolution rate, per-repository breakdown, cost-efficiency (resolved vs. cost scatter plots), and temporal trends (resolved vs. model release date), enabling comparative analysis of agent capabilities and cost-performance tradeoffs.

Solves for

Compare performance of different AI agents (mini-SWE-agent v2, SWE-agent, custom agents) on the same benchmarkFilter leaderboard to show only open-source agents or proprietary agents depending on evaluation focusIdentify which repositories or languages have highest/lowest agent success ratesAnalyze cost-efficiency tradeoffs — find agents that achieve high resolution rates with low computational cost+1 more

Best for

AI researchers comparing agent architectures and model choices on standardized benchmarks

Model providers (OpenAI, Anthropic, open-source communities) tracking their agents' leaderboard position

Teams selecting which agent framework to adopt based on published performance metrics

Requires

Web browser with JavaScript support to access interactive leaderboard

Agent framework that can be evaluated on SWE-bench (e.g., SWE-agent, mini-SWE-agent, custom agent with compatible interface)

Submission credentials or API access to leaderboard (submission process unknown)

Limitations

Leaderboard submission process not documented in provided material — cannot determine submission requirements, deadlines, or verification procedures

No statistical significance testing or confidence intervals — cannot determine if performance differences between agents are meaningful or due to variance

Filtering options (agent type, model category, scaffold type, tags) are not fully documented — unclear what each filter includes or excludes

What makes it unique

Provides multi-dimensional filtering (agent type, model category, scaffold type, tags) and visualization options (cost-efficiency scatter plots, per-repository heatmaps, temporal trends) that enable comparative analysis beyond simple ranking. The leaderboard tracks both performance (resolution rate) and efficiency metrics (cost, steps), allowing cost-performance tradeoff analysis.

vs alternatives

More comprehensive than simple ranking tables by offering interactive filtering and multi-dimensional visualizations; enables cost-efficiency analysis that single-metric leaderboards (e.g., HumanEval) do not provide.

docker-sandboxed code execution and test validation

Medium confidence

Executes agent-generated code modifications within isolated Docker containers that replicate the target repository's environment, including all dependencies, build tools, and test suites. This sandboxing approach ensures that code changes are validated against the actual test suite in a controlled environment, preventing agents from gaming the benchmark through environment-specific hacks and ensuring reproducibility across different evaluation machines. The Docker infrastructure was added in 06/2024 to standardize evaluation environments.

Solves for

Safely execute untrusted agent-generated code without risking the evaluation systemValidate that agent-generated code changes actually pass the repository's test suite in a realistic environmentEnsure reproducible evaluation results across different machines and evaluation runsPrevent agents from exploiting environment-specific quirks or hardcoding solutions

Best for

Benchmark organizers ensuring safe, reproducible evaluation of untrusted agent code

Research teams running local evaluations of custom agents without relying on centralized leaderboard submission

Organizations with security requirements that mandate sandboxed code execution

Requires

Docker runtime (version not specified, likely Docker 20.10+)

Repository-specific dependencies (Python packages, language runtimes, build tools) pre-configured in container images

Test suite execution capability within container (pytest, unittest, or language-specific test runners)

Limitations

Docker overhead adds latency to evaluation — exact timing impact not documented

Container setup time for each instance (installing dependencies, building code) not quantified — affects total evaluation duration

Docker requires significant disk space for storing container images and instance artifacts — storage requirements not documented

What makes it unique

Uses Docker containerization to replicate exact repository environments (dependencies, build tools, test suites) for each instance, ensuring that test validation occurs in realistic conditions rather than isolated environments. This approach was explicitly added in 06/2024 to standardize evaluation across different machines and prevent environment-specific gaming.

vs alternatives

More rigorous than in-memory code execution (e.g., HumanEval's exec()) because it validates code against actual test suites in realistic environments; more reproducible than local evaluation because Docker ensures consistent environments across machines.

human-verified solvability filtering for verified subset

Medium confidence

The Verified subset (500 instances) underwent explicit human verification to confirm that each GitHub issue is actually solvable by code modification, filtering out unsolvable issues (e.g., issues requiring infrastructure changes, documentation-only fixes, or issues with conflicting requirements). This verification process was completed by 08/2024 in collaboration with OpenAI, reducing false negatives from unsolvable issues that would artificially inflate baseline difficulty and make agent performance metrics less reliable.

Solves for

Obtain high-confidence performance metrics by evaluating only on confirmed-solvable issuesPublish benchmark results with defensible claims about agent capability (e.g., '65% of solvable issues resolved')Reduce variance in agent evaluation by eliminating unsolvable instances that no agent could resolveIdentify which types of issues are solvable vs. unsolvable for future benchmark design

Best for

Research teams publishing results that require high-confidence performance claims

Model providers making public statements about agent capability

Organizations with limited evaluation budgets that want to maximize signal-to-noise ratio

Requires

Human annotators with software engineering expertise to assess solvability

Clear solvability criteria and annotation guidelines (not provided in documentation)

Annotation infrastructure (e.g., labeling platform, inter-rater agreement tracking)

Limitations

Human verification methodology not documented — cannot assess annotation quality, inter-rater agreement, or potential biases

Verification criteria not specified — unclear what makes an issue 'solvable' (e.g., does it require only code changes, or can it require configuration changes?)

Verification process may introduce human bias — annotators may have different interpretations of solvability

What makes it unique

Explicitly filters benchmark instances through human verification to confirm solvability, reducing false negatives from unsolvable issues that would artificially inflate baseline difficulty. This verification process (completed 08/2024) was a deliberate design choice to improve benchmark reliability, distinguishing Verified from Full (unverified) subset.

vs alternatives

More reliable than unverified benchmarks (e.g., full SWE-bench with 2,294 instances) because human verification eliminates unsolvable issues that no agent could resolve; enables higher-confidence performance claims for published results.

cost and efficiency metrics tracking and visualization

Medium confidence

Tracks and visualizes multiple efficiency dimensions for each agent evaluation: total cost (API calls, compute), step count (number of agent actions), and resolved instances achieved within cost/step budgets. The leaderboard provides scatter plot visualizations of resolved vs. cost, resolved vs. average cost, resolved vs. cost limit, and resolved vs. step limit, enabling analysis of cost-performance tradeoffs and identification of efficient agents that achieve high resolution rates with minimal computational overhead.

Solves for

Compare agents not just on resolution rate but on cost-efficiency — find agents that achieve high performance with low API costsAnalyze cost-performance tradeoffs — understand how much additional cost is required to improve resolution rateIdentify agents that are efficient for resource-constrained environments (e.g., limited API budgets)Track how agent efficiency improves over time as new models and architectures are developed

Best for

Teams deploying AI agents in production with limited API budgets or computational resources

Model providers optimizing inference cost and latency

Researchers analyzing the relationship between agent complexity and performance gains

Requires

Cost tracking infrastructure in agent framework (API call logging, compute time measurement)

Standardized cost metrics across different agent implementations

Leaderboard infrastructure to collect and visualize cost data

Limitations

Cost metrics definition not documented — unclear whether cost includes API calls, compute time, or both

Step count definition not documented — unclear what constitutes a 'step' (agent action, API call, code modification?)

Cost tracking methodology not specified — cannot determine if costs are actual (from API providers) or estimated

What makes it unique

Tracks and visualizes cost and step metrics alongside resolution rate, enabling cost-performance tradeoff analysis that single-metric benchmarks do not provide. The leaderboard includes scatter plot visualizations (resolved vs. cost, resolved vs. steps) that make efficiency tradeoffs explicit and comparable across agents.

vs alternatives

More comprehensive than performance-only benchmarks (e.g., HumanEval) by tracking efficiency metrics; enables practical deployment decisions based on cost-performance tradeoffs rather than just raw performance.

per-repository and per-language performance breakdown

Medium confidence

Provides granular performance analysis by breaking down agent resolution rates by individual repository and by programming language (for Multilingual variant). The leaderboard includes visualizations for 'resolved by repository' and 'resolved by language', enabling identification of which repositories or languages are easier/harder for agents and revealing potential biases in benchmark composition or agent capabilities.

Solves for

Identify which repositories have highest/lowest agent success rates to understand domain-specific challengesDetect language-specific performance gaps in Multilingual variant (e.g., agents may perform better on Python than Go)Analyze whether benchmark is balanced across repositories or biased toward certain codebasesUnderstand which types of code (by repository or language) agents struggle with most

Best for

Researchers analyzing agent performance across different domains and programming languages

Benchmark organizers assessing benchmark balance and identifying potential biases

Teams developing language-specific or domain-specific coding agents

Requires

Repository metadata (name, language, domain) for each instance

Leaderboard infrastructure to aggregate and visualize per-repository and per-language metrics

Limitations

Per-repository breakdown not documented — unclear how many repositories are included or how instances are distributed

Per-language breakdown only available for Multilingual variant (separate from Verified) — cannot compare language performance on same agent

No analysis of why certain repositories/languages have higher/lower success rates — cannot determine if differences are due to agent capability or issue difficulty

What makes it unique

Provides per-repository and per-language breakdowns of agent performance, enabling granular analysis of which domains and languages agents struggle with. This level of detail is not common in code generation benchmarks, which typically report only aggregate metrics.

vs alternatives

More informative than aggregate-only benchmarks (e.g., HumanEval) by revealing domain-specific and language-specific performance gaps; enables identification of benchmark biases and agent weaknesses.

temporal trend analysis and model release date correlation

Medium confidence

Tracks agent performance over time and correlates resolution rates with model release dates, enabling analysis of how agent capability improves as new models and architectures are developed. The leaderboard includes visualizations for 'resolved vs. model release date', showing the relationship between model recency and benchmark performance.

Solves for

Analyze whether newer models consistently achieve higher resolution ratesIdentify inflection points where agent capability makes significant jumpsPredict future agent capability based on historical trendsAssess whether benchmark is saturating or has room for improvement

Best for

Researchers tracking progress in AI coding agents over time

Model providers understanding how their model releases impact benchmark performance

Benchmark organizers assessing whether benchmark is saturating

Requires

Model release date metadata for each agent

Leaderboard infrastructure to track performance over time

Sufficient historical data (currently limited to ~1 year of leaderboard history)

Limitations

Temporal trend analysis limited to leaderboard history (since 03/2024) — insufficient data for long-term trend analysis

Model release date correlation not documented — unclear how release dates are determined or if all agents have documented release dates

No analysis of causality — cannot determine if performance improvements are due to model improvements or agent architecture improvements

What makes it unique

Correlates agent performance with model release dates to track how capability improves over time, providing a temporal dimension to benchmark analysis. This enables analysis of progress in the field and prediction of future capability.

vs alternatives

More informative than static benchmarks by showing performance trends over time; enables understanding of whether benchmark is saturating or has room for improvement.

open-source benchmark infrastructure and local evaluation support

Medium confidence

SWE-bench is open-source and supports local evaluation of custom agents without relying on centralized leaderboard submission. The benchmark infrastructure (Docker-based evaluation, test validation, metrics computation) is publicly available, enabling researchers to run evaluations on their own machines and reproduce results. This open-source approach contrasts with proprietary benchmarks and enables community contributions and extensions.

Solves for

Run local evaluations of custom agents without submitting to public leaderboardReproduce published results by running benchmark locallyExtend benchmark with custom instances or evaluation logicContribute improvements to benchmark infrastructure

Best for

Research teams developing custom agents that may not be ready for public leaderboard submission

Organizations with privacy requirements that prevent submitting results to public leaderboard

Researchers wanting to modify benchmark evaluation logic or add custom metrics

Requires

Docker runtime

Python 3.x environment

Repository-specific dependencies (language runtimes, build tools, test frameworks)

Limitations

Local evaluation requires significant setup effort — Docker, dependencies, test suite configuration

Evaluation time and computational cost not documented — cannot budget resources for local runs

No documentation of how to extend benchmark with custom instances or metrics

What makes it unique

Open-source benchmark infrastructure enables local evaluation and community contributions, contrasting with proprietary benchmarks that require centralized submission. The Docker-based evaluation framework is publicly available, enabling researchers to reproduce results and extend the benchmark.

vs alternatives

More accessible than proprietary benchmarks (e.g., some closed-source evaluation platforms) because researchers can run local evaluations without relying on centralized infrastructure; enables reproducibility and community contributions.

multi-language support via multilingual variant

Medium confidence

The Multilingual variant (300 instances across 9 programming languages) extends SWE-bench beyond Python to evaluate agent capability across different languages. This variant maintains the same task structure (resolve GitHub issues via code modification) but includes instances from repositories in languages like JavaScript, Java, Go, C++, Rust, and others, enabling evaluation of language-agnostic agent architectures.

Solves for

Evaluate whether agents trained primarily on Python can generalize to other programming languagesCompare agent performance across different languages to identify language-specific challengesAssess whether language-specific agents (e.g., JavaScript-focused) outperform general agentsDevelop and test language-agnostic agent architectures

Best for

Teams developing multi-language coding agents

Researchers studying language generalization in AI coding systems

Model providers evaluating cross-language capability

Requires

Agent framework capable of handling multiple programming languages

Language-specific test frameworks and build tools (pytest for Python, Jest for JavaScript, JUnit for Java, etc.)

Docker images with language-specific runtimes and dependencies

Limitations

Multilingual variant is separate from Verified subset — cannot directly compare language performance on same agent

Only 300 instances total across 9 languages — ~33 instances per language on average, potentially insufficient for reliable per-language metrics

Language distribution not documented — unclear if instances are evenly distributed or biased toward certain languages

What makes it unique

Extends benchmark to 9 programming languages (beyond Python-only Verified subset), enabling evaluation of language generalization and cross-language agent capability. This is a deliberate design choice to assess whether agents can handle diverse languages, not just Python.

vs alternatives

More comprehensive than Python-only benchmarks (e.g., HumanEval, MBPP) by including multiple languages; enables evaluation of language generalization that single-language benchmarks cannot assess.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with SWE-bench Verified, ranked by overlap. Discovered automatically through the match graph.

Benchmark65

SWE-bench

AI coding agent benchmark — real GitHub issues, end-to-end evaluation, the standard for code agents.

real-world github issue-to-patch evaluationissue-to-code semantic mappingmulti-repository benchmark aggregationissue difficulty classification and stratification

4 shared capabilities

Agent58

SWE-agent

Princeton's GitHub issue solver — navigates code, edits files, runs tests, submits patches.

autonomous github issue resolution with codebase navigationswe-bench benchmark evaluation and scoring

2 shared capabilities

Dataset58

RealWorldQA

Real-world visual QA requiring spatial reasoning.

multimodal model evaluation and comparison frameworkspatial-reasoning evaluation in visual contexts

2 shared capabilities

Benchmark64

MathVista

Visual mathematical reasoning benchmark.

multimodal mathematical reasoning evaluation across visual domainsmulti-source dataset aggregation and standardization

2 shared capabilities

Dataset20

SWE-bench_Verified

Dataset by princeton-nlp. 7,26,882 downloads.

verified-software-engineering-task-dataset-loading

1 shared capability

Benchmark16

varies

based on the model used by the agent.

software-engineering-task-benchmark-evaluation

1 shared capability

Best For

✓AI research teams developing and benchmarking autonomous coding agents
✓Model providers (OpenAI, Anthropic, open-source) evaluating coding capabilities across model versions
✓Software engineering teams assessing whether AI agents can augment their development workflows
✓Research teams with varying computational budgets — can start with Lite for rapid iteration, graduate to Full for final results
✓Model providers supporting multiple programming languages — Multilingual variant enables cross-language capability comparison
✓Teams evaluating agents on real-world issues that include visual documentation or diagrams
✓Organizations publishing benchmarking results — Verified subset provides defensible, human-verified performance claims
✓Teams developing multimodal coding agents with vision capabilities

Known Limitations

⚠Binary metric with no partial credit — agents receive 0% for incomplete solutions even if they make significant progress toward resolution
⚠Python-only for Verified subset (500 instances); separate Multilingual variant required for non-Python evaluation
⚠Definition of 'resolved' not explicitly documented in provided material — likely requires passing test suite but exact criteria unknown
⚠No statistical significance testing or confidence intervals provided — cannot determine if performance differences between agents are meaningful
⚠Potential training data contamination — GitHub issues may appear in LLM training sets, inflating performance metrics
⚠Evaluation time and cost per instance not documented — cannot budget computational resources for full benchmark runs

Requirements

Docker runtime for sandboxed code executionPython 3.x environment with repository-specific dependenciesAccess to GitHub issue data and corresponding repository codeTest suite execution capability within sandboxed environmentAgent framework capable of multi-step reasoning and code modification (e.g., SWE-agent, mini-SWE-agent)Agent framework capable of handling the specific variant's requirements (e.g., vision model for Multimodal)Docker runtime for all variants (code execution in sandboxed environment)Repository-specific dependencies for each instance (Python packages, language runtimes, build tools)

Input / Output

Accepts: GitHub issue text (bug reports, feature requests, enhancement requests), Repository source code and file structure, Repository test suites and validation scripts, Issue metadata (repository name, issue number, creation date), GitHub issues (text-based for Verified/Lite/Full/Multilingual), GitHub issues with embedded images, diagrams, or visual elements (Multimodal), Repository code in Python (Verified/Lite/Full), multiple languages (Multilingual), or with visual documentation (Multimodal), GitHub issues with embedded images, diagrams, or visual elements, Repository source code, Visual elements (images, diagrams, screenshots), GitHub issue (text, metadata), Repository context (code, file structure, test suite), GitHub issues from popular Python repositories, Repository metadata (language, popularity, domain), Agent evaluation results (resolution rate, cost, steps, per-repository breakdown), Agent metadata (name, type, model, release date, scaffold type), Agent-generated code modifications (patches, file edits, new files), Repository source code and test suite, Repository configuration (setup.py, requirements.txt, Dockerfile, etc.), Repository source code and context, Agent evaluation results (resolution rate, cost, steps), Agent metadata (model, framework, configuration), Agent evaluation results with per-instance repository and language labels, Repository metadata, Agent evaluation results with timestamps, Model release dates, SWE-bench benchmark instances (GitHub issues, repository code), Agent implementation, GitHub issues from repositories in 9 programming languages, Repository source code in multiple languages

Produces: Binary resolution outcome (resolved/not resolved), Percentage of instances resolved (aggregated metric), Per-repository resolution rates, Cost and step metrics for each agent attempt, Resolution rate (% of instances resolved) per variant, Per-repository resolution breakdown, Per-language resolution breakdown (Multilingual), Cost and step metrics per variant, Resolution rate on multimodal instances, Per-instance analysis of whether visual information was leveraged, Comparison of multimodal vs. text-only performance (if available), Code modifications (patches, file edits), Execution results (test output, error messages), Resolution outcome (resolved/not resolved), Curated dataset of GitHub issues (Lite: 300, Verified: 500, Full: 2,294), Per-issue metadata (repository, issue type, complexity, solvability), Ranked list of agents by resolution rate, Filtered subsets of leaderboard (by agent type, model category, scaffold, tags), Visualizations: bar charts (resolved %), per-repository heatmaps, cost-efficiency scatter plots, temporal trend plots, Per-agent metrics: resolution rate, cost, step count, per-repository breakdown, Test execution results (pass/fail, test output, error messages), Binary resolution outcome (issue resolved if all tests pass), Execution logs and artifacts, Binary solvability assessment (solvable/unsolvable) per issue, Filtered dataset of 500 solvable instances, Potentially: annotation rationale or solvability criteria, Cost metrics per agent (total cost, average cost per instance, cost per resolved instance), Step metrics per agent (total steps, average steps per instance, steps per resolved instance), Visualizations: scatter plots (resolved vs. cost, resolved vs. steps), trend plots (cost over time), Per-repository resolution rates (% resolved per repository), Per-language resolution rates (% resolved per language, Multilingual variant only), Visualizations: heatmaps (resolved instances matrix), bar charts (resolved by repository/language), Temporal trend plots (resolution rate over time), Scatter plots (resolved vs. model release date), Trend analysis (e.g., average improvement per month), Local evaluation results (resolution rate, cost, steps), Test execution logs and artifacts, Per-language resolution rates, Overall resolution rate across all languages, Per-language breakdown of agent performance

UnfragileRank

Adoption70%(25% weight)

Quality90%(35% weight)

Ecosystem30%(15% weight)

Match Graph25%(20% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Benchmark

13 capabilities

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Human-verified subset of SWE-bench containing 500 real GitHub issues from popular Python repositories, providing a more reliable evaluation of AI coding agents on real-world software engineering tasks with confirmed solvability.

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Capabilities13 decomposed

real-world github issue resolution evaluation

Medium confidence

Solves for

Best for

AI research teams developing and benchmarking autonomous coding agents

Model providers (OpenAI, Anthropic, open-source) evaluating coding capabilities across model versions

Software engineering teams assessing whether AI agents can augment their development workflows

Requires

Docker runtime for sandboxed code execution

Python 3.x environment with repository-specific dependencies

Access to GitHub issue data and corresponding repository code

Limitations

Binary metric with no partial credit — agents receive 0% for incomplete solutions even if they make significant progress toward resolution

Python-only for Verified subset (500 instances); separate Multilingual variant required for non-Python evaluation

Definition of 'resolved' not explicitly documented in provided material — likely requires passing test suite but exact criteria unknown

What makes it unique

vs alternatives

multi-variant benchmark suite with specialized subsets

Medium confidence

Solves for

Best for

Research teams with varying computational budgets — can start with Lite for rapid iteration, graduate to Full for final results

Model providers supporting multiple programming languages — Multilingual variant enables cross-language capability comparison

Teams evaluating agents on real-world issues that include visual documentation or diagrams

Requires

Agent framework capable of handling the specific variant's requirements (e.g., vision model for Multimodal)

Docker runtime for all variants (code execution in sandboxed environment)

Repository-specific dependencies for each instance (Python packages, language runtimes, build tools)

Limitations

Variants are separate benchmarks with different instance counts — cannot directly compare agent performance across variants (e.g., 65% on Verified does not equal 65% on Full)

Multilingual variant (300 instances) is significantly smaller than Verified (500) — may have higher variance and lower statistical power

Multimodal variant (517 instances) requires agents with vision capabilities — not all coding agents support multimodal input

What makes it unique

vs alternatives

multimodal issue resolution with visual elements

Medium confidence

Solves for

Best for

Teams developing multimodal coding agents with vision capabilities

Model providers evaluating vision-language models on practical tasks

Researchers studying the role of visual information in software engineering

Requires

Agent framework with vision capabilities (multimodal LLM, vision encoder)

Image processing and embedding infrastructure

Docker environment capable of handling image data

Limitations

Multimodal variant is separate from Verified subset — cannot compare multimodal vs. text-only performance on same instances

Only 517 instances total — smaller than Verified (500) or Full (2,294), potentially insufficient for reliable metrics

Visual element types not documented — unclear what types of images/diagrams are included (screenshots, diagrams, charts, etc.)

What makes it unique

vs alternatives

agent framework integration and standardized evaluation interface

Medium confidence

Solves for

Best for

Agent framework developers implementing SWE-bench evaluation support

Researchers comparing agents from different frameworks

Benchmark organizers ensuring fair evaluation across diverse agents

Requires

Agent framework implementation of standardized evaluation interface

Documentation of interface specification (not provided in material)

Limitations

Standardized interface specification not documented in provided material — cannot determine exact interface requirements

Interface may constrain agent design — agents must conform to interface rather than using optimal architecture

No documentation of how interface handles different agent paradigms (e.g., planning-based vs. reactive agents)

What makes it unique

vs alternatives

More rigorous than benchmarks without standardized interfaces because it ensures all agents operate under identical constraints; enables fair comparison across diverse agent architectures.

benchmark dataset curation and issue selection

Medium confidence

Solves for

Best for

Benchmark organizers curating high-quality evaluation datasets

Researchers wanting realistic software engineering tasks for agent evaluation

Teams assessing whether benchmark instances are representative of their codebase

Requires

GitHub API access to retrieve issues and repository metadata

Curation infrastructure (filtering, deduplication, quality assessment)

Human annotators for Verified subset verification

Limitations

Repository selection criteria not documented — unclear what makes a repository 'popular' or suitable for inclusion

Issue selection criteria not documented — unclear what types of issues are included/excluded

Curation methodology not documented — cannot assess potential biases in issue selection

What makes it unique

vs alternatives

leaderboard-based agent performance ranking and filtering

Medium confidence

Solves for

Best for

AI researchers comparing agent architectures and model choices on standardized benchmarks

Model providers (OpenAI, Anthropic, open-source communities) tracking their agents' leaderboard position

Teams selecting which agent framework to adopt based on published performance metrics

Requires

Web browser with JavaScript support to access interactive leaderboard

Agent framework that can be evaluated on SWE-bench (e.g., SWE-agent, mini-SWE-agent, custom agent with compatible interface)

Submission credentials or API access to leaderboard (submission process unknown)

Limitations

Leaderboard submission process not documented in provided material — cannot determine submission requirements, deadlines, or verification procedures

No statistical significance testing or confidence intervals — cannot determine if performance differences between agents are meaningful or due to variance

Filtering options (agent type, model category, scaffold type, tags) are not fully documented — unclear what each filter includes or excludes

What makes it unique

vs alternatives

docker-sandboxed code execution and test validation

Medium confidence

Solves for

Best for

Benchmark organizers ensuring safe, reproducible evaluation of untrusted agent code

Research teams running local evaluations of custom agents without relying on centralized leaderboard submission

Organizations with security requirements that mandate sandboxed code execution

Requires

Docker runtime (version not specified, likely Docker 20.10+)

Repository-specific dependencies (Python packages, language runtimes, build tools) pre-configured in container images

Test suite execution capability within container (pytest, unittest, or language-specific test runners)

Limitations

Docker overhead adds latency to evaluation — exact timing impact not documented

Container setup time for each instance (installing dependencies, building code) not quantified — affects total evaluation duration

Docker requires significant disk space for storing container images and instance artifacts — storage requirements not documented

What makes it unique

vs alternatives

human-verified solvability filtering for verified subset

Medium confidence

Solves for

Best for

Research teams publishing results that require high-confidence performance claims

Model providers making public statements about agent capability

Organizations with limited evaluation budgets that want to maximize signal-to-noise ratio

Requires

Human annotators with software engineering expertise to assess solvability

Clear solvability criteria and annotation guidelines (not provided in documentation)

Annotation infrastructure (e.g., labeling platform, inter-rater agreement tracking)

Limitations

Human verification methodology not documented — cannot assess annotation quality, inter-rater agreement, or potential biases

Verification criteria not specified — unclear what makes an issue 'solvable' (e.g., does it require only code changes, or can it require configuration changes?)

Verification process may introduce human bias — annotators may have different interpretations of solvability

What makes it unique

vs alternatives

cost and efficiency metrics tracking and visualization

Medium confidence

Solves for

Best for

Teams deploying AI agents in production with limited API budgets or computational resources

Model providers optimizing inference cost and latency

Researchers analyzing the relationship between agent complexity and performance gains

Requires

Cost tracking infrastructure in agent framework (API call logging, compute time measurement)

Standardized cost metrics across different agent implementations

Leaderboard infrastructure to collect and visualize cost data

Limitations

Cost metrics definition not documented — unclear whether cost includes API calls, compute time, or both

Step count definition not documented — unclear what constitutes a 'step' (agent action, API call, code modification?)

Cost tracking methodology not specified — cannot determine if costs are actual (from API providers) or estimated

What makes it unique

vs alternatives

per-repository and per-language performance breakdown

Medium confidence

Solves for

Best for

Researchers analyzing agent performance across different domains and programming languages

Benchmark organizers assessing benchmark balance and identifying potential biases

Teams developing language-specific or domain-specific coding agents

Requires

Repository metadata (name, language, domain) for each instance

Leaderboard infrastructure to aggregate and visualize per-repository and per-language metrics

Limitations

Per-repository breakdown not documented — unclear how many repositories are included or how instances are distributed

Per-language breakdown only available for Multilingual variant (separate from Verified) — cannot compare language performance on same agent

No analysis of why certain repositories/languages have higher/lower success rates — cannot determine if differences are due to agent capability or issue difficulty

What makes it unique

vs alternatives

More informative than aggregate-only benchmarks (e.g., HumanEval) by revealing domain-specific and language-specific performance gaps; enables identification of benchmark biases and agent weaknesses.

temporal trend analysis and model release date correlation

Medium confidence

Solves for

Best for

Researchers tracking progress in AI coding agents over time

Model providers understanding how their model releases impact benchmark performance

Benchmark organizers assessing whether benchmark is saturating

Requires

Model release date metadata for each agent

Leaderboard infrastructure to track performance over time

Sufficient historical data (currently limited to ~1 year of leaderboard history)

Limitations

Temporal trend analysis limited to leaderboard history (since 03/2024) — insufficient data for long-term trend analysis

Model release date correlation not documented — unclear how release dates are determined or if all agents have documented release dates

No analysis of causality — cannot determine if performance improvements are due to model improvements or agent architecture improvements

What makes it unique

vs alternatives

More informative than static benchmarks by showing performance trends over time; enables understanding of whether benchmark is saturating or has room for improvement.

open-source benchmark infrastructure and local evaluation support

Medium confidence

Solves for

Best for

Research teams developing custom agents that may not be ready for public leaderboard submission

Organizations with privacy requirements that prevent submitting results to public leaderboard

Researchers wanting to modify benchmark evaluation logic or add custom metrics

Requires

Docker runtime

Python 3.x environment

Repository-specific dependencies (language runtimes, build tools, test frameworks)

Limitations

Local evaluation requires significant setup effort — Docker, dependencies, test suite configuration

Evaluation time and computational cost not documented — cannot budget resources for local runs

No documentation of how to extend benchmark with custom instances or metrics

What makes it unique

vs alternatives

multi-language support via multilingual variant

Medium confidence

Solves for

Best for

Teams developing multi-language coding agents

Researchers studying language generalization in AI coding systems

Model providers evaluating cross-language capability

Requires

Agent framework capable of handling multiple programming languages

Language-specific test frameworks and build tools (pytest for Python, Jest for JavaScript, JUnit for Java, etc.)

Docker images with language-specific runtimes and dependencies

Limitations

Multilingual variant is separate from Verified subset — cannot directly compare language performance on same agent

Only 300 instances total across 9 languages — ~33 instances per language on average, potentially insufficient for reliable per-language metrics

Language distribution not documented — unclear if instances are evenly distributed or biased toward certain languages

What makes it unique

vs alternatives

More comprehensive than Python-only benchmarks (e.g., HumanEval, MBPP) by including multiple languages; enables evaluation of language generalization that single-language benchmarks cannot assess.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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SWE-bench Verified

Capabilities13 decomposed

real-world github issue resolution evaluation

multi-variant benchmark suite with specialized subsets

multimodal issue resolution with visual elements

agent framework integration and standardized evaluation interface

benchmark dataset curation and issue selection

leaderboard-based agent performance ranking and filtering

docker-sandboxed code execution and test validation

human-verified solvability filtering for verified subset

cost and efficiency metrics tracking and visualization

per-repository and per-language performance breakdown

temporal trend analysis and model release date correlation

open-source benchmark infrastructure and local evaluation support

multi-language support via multilingual variant

Related Artifactssharing capabilities

SWE-bench

SWE-agent

RealWorldQA

MathVista

SWE-bench_Verified

varies

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to SWE-bench Verified

Are you the builder of SWE-bench Verified?

Get the weekly brief

Data Sources

SWE-bench Verified

Capabilities13 decomposed

real-world github issue resolution evaluation

multi-variant benchmark suite with specialized subsets

multimodal issue resolution with visual elements

agent framework integration and standardized evaluation interface

benchmark dataset curation and issue selection

leaderboard-based agent performance ranking and filtering

docker-sandboxed code execution and test validation

human-verified solvability filtering for verified subset

cost and efficiency metrics tracking and visualization

per-repository and per-language performance breakdown

temporal trend analysis and model release date correlation

open-source benchmark infrastructure and local evaluation support

multi-language support via multilingual variant

Related Artifactssharing capabilities

SWE-bench

SWE-agent

RealWorldQA

MathVista

SWE-bench_Verified

varies

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to SWE-bench Verified

Are you the builder of SWE-bench Verified?

Get the weekly brief

Data Sources