What can SWE-bench Verified do?

real-world github issue resolution evaluation, agent-based iterative code execution with feedback loops, multi-dimensional leaderboard with cost-performance tradeoffs, human-verified issue solvability curation, multi-variant benchmark suite with language and modality coverage, reference agent implementations with open-source baselines, per-repository and per-language performance breakdown, computational cost tracking and optimization metrics, institutional support and funding transparency, recent benchmark extensions and research directions

SWE-bench Verified

BenchmarkFree

Human-verified benchmark for AI coding agents.

Open Source

/ 100

10 capabilities

Capabilities10 decomposed

real-world github issue resolution evaluation

Medium confidence

Evaluates AI coding agents' ability to autonomously resolve real GitHub issues from popular Python repositories by executing agents in sandboxed Docker environments, measuring success as binary pass/fail (issue resolved or not). The benchmark sources 500 human-verified instances from production codebases, providing ground truth that issues are solvable and have confirmed resolution criteria, unlike synthetic task benchmarks.

Solves for

Measure whether my AI coding agent can solve real-world software engineering problemsCompare my agent's performance against other agents on a standardized set of production issuesIdentify which types of GitHub issues my agent struggles with mostValidate that my agent generalizes beyond training data to unseen repositories and issue patterns

Best for

AI research teams building autonomous coding agents

Companies evaluating LLM-based code generation tools for production use

Open-source maintainers benchmarking their own agent implementations

Requires

Docker runtime for sandboxed code execution

Python 3.x environment for agent implementation

Access to SWE-bench Verified dataset (500 instances)

Limitations

Binary pass/fail metric provides no credit for partial solutions or near-misses

Restricted to Python repositories; generalization to other languages requires separate Multilingual variant

High contamination risk since issues sourced from public GitHub data likely in training sets of large models

What makes it unique

Uses 500 human-verified real GitHub issues with confirmed solvability rather than synthetic tasks, providing ground truth that solutions exist; includes Docker-sandboxed execution environment to prevent agent code from escaping; tracks computational cost alongside success rate via leaderboard scatter plots

vs alternatives

More realistic than HumanEval or MBPP because it evaluates agents on actual production issues with full repository context, but narrower than full SWE-bench (2,294 instances) and limited to Python unlike Multilingual variant

agent-based iterative code execution with feedback loops

Medium confidence

Provides a sandboxed execution environment where AI agents can iteratively write and run code, receive execution feedback (stdout, stderr, test results), and refine solutions across multiple steps. The Docker-based sandbox isolates agent code execution to prevent system compromise while capturing detailed execution traces for debugging and analysis.

Solves for

Allow my agent to test code changes and receive immediate feedback during problem-solvingExecute agent-generated code safely without risking the host systemCollect execution traces and step counts for cost analysis and optimizationEnable agents to use test failures as signals to refine their approach

Best for

Autonomous coding agents that need to validate solutions via code execution

Research teams studying agent behavior through execution traces

Teams building agents that must handle runtime errors and adapt solutions

Requires

Docker daemon running on evaluation infrastructure

Agent implementation capable of parsing code execution output

Repository context (source code, tests, dependencies) available in sandbox

Limitations

Docker overhead adds latency per execution step (exact timing not documented)

Sandbox isolation prevents agents from accessing external APIs or network resources

No built-in persistence across evaluation runs—each instance starts fresh

What makes it unique

Implements Docker-based sandboxing specifically for agent evaluation (as of 06/2024 release), enabling safe iterative code execution with full isolation; tracks step counts and computational costs as first-class metrics alongside success rates

vs alternatives

More secure than in-process code execution and provides better isolation than subprocess-based sandboxing; enables cost tracking that static code generation benchmarks cannot measure

multi-dimensional leaderboard with cost-performance tradeoffs

Medium confidence

Provides a web-based leaderboard (https://www.swebench.com) that visualizes agent performance across multiple dimensions including resolution rate, computational cost (steps, API calls), model release date, and per-repository breakdowns. Agents can be filtered by type (open-source vs proprietary), scaffold type, and compared side-by-side with scatter plots showing resolved instances vs cumulative cost.

Solves for

Compare my agent's performance against other agents on the same benchmarkUnderstand the cost-performance tradeoff—which agents achieve high resolution with low computational costIdentify which repositories or issue types my agent performs poorly onTrack how agent performance evolves over time relative to model release dates

Best for

Researchers publishing agent benchmarks and needing public comparison

Teams evaluating which agent to deploy based on accuracy vs cost

Open-source maintainers tracking their agent's competitive position

Requires

Web browser access to https://www.swebench.com

Agent evaluation results in format compatible with leaderboard submission (format unknown)

Limitations

Leaderboard submission process not documented—unclear how new agents are added

No statistical significance testing or confidence intervals reported

Filtering by 'Open Scaffold' suggests some agents use different scaffolding, but impact on comparability unknown

What makes it unique

Includes cost-performance scatter plots as primary comparison dimension, enabling evaluation of agents on Pareto frontier (high resolution with low cost) rather than resolution alone; supports filtering by agent type, scaffold, and tags for nuanced comparison

vs alternatives

More comprehensive than single-metric leaderboards because it visualizes cost-performance tradeoffs; web-based interface enables real-time updates and side-by-side comparison unlike static published results

human-verified issue solvability curation

Medium confidence

Curates a subset of 500 GitHub issues from the full SWE-bench (2,294 instances) through human verification to ensure each issue is solvable and has a clear resolution criterion. The verification process filters out ambiguous, unsolvable, or ill-defined issues, providing higher-quality ground truth than raw GitHub data.

Solves for

Ensure my agent evaluation is not penalized for unsolvable issuesReduce noise from ambiguous issue descriptions that lack clear resolution criteriaObtain a curated benchmark subset that is more reliable for comparing agent qualityAvoid wasting computational resources evaluating agents on issues that cannot be solved

Best for

Researchers needing a high-quality, smaller benchmark for agent evaluation

Teams with limited computational budget wanting to evaluate on curated instances

Benchmarking studies requiring ground truth that issues are genuinely solvable

Requires

Access to SWE-bench Verified dataset (500 curated instances)

Understanding that this is a subset of full SWE-bench with different characteristics

Limitations

Verification methodology and criteria not publicly documented—unclear what makes an issue 'verifiable'

Verifier expertise and background not disclosed—potential for subjective or inconsistent verification

Smaller test set (500 vs 2,294 full) may have higher variance and lower statistical power

What makes it unique

Applies human verification to filter out unsolvable or ambiguous issues, reducing benchmark noise; creates a smaller, higher-quality subset (500 instances) for more reliable agent comparison than full SWE-bench

vs alternatives

More reliable than raw GitHub issues because verification ensures solvability; smaller than full SWE-bench (2,294) enabling faster evaluation cycles, but with potential loss of coverage

multi-variant benchmark suite with language and modality coverage

Medium confidence

Provides multiple benchmark variants (SWE-bench Verified, Lite, Full, Multilingual, Multimodal) enabling evaluation across different scopes, languages, and modalities. Variants range from 300 instances (Lite, cost-optimized) to 2,294 (Full), with Multilingual covering 9 languages and Multimodal including visual elements in issue descriptions.

Solves for

Evaluate my agent on a smaller, faster benchmark (Lite) before running full evaluationTest whether my agent generalizes to non-Python languages using Multilingual variantAssess agent performance on issues with visual/diagram components using Multimodal variantCompare results across variants to understand how benchmark size affects agent performance

Best for

Teams with varying computational budgets (Lite for rapid iteration, Full for comprehensive evaluation)

Multilingual agent developers needing cross-language benchmarking

Vision-language model researchers evaluating agents on multimodal software engineering tasks

Requires

Access to specific benchmark variant(s) matching evaluation goals

Understanding of variant differences and appropriate use cases

Limitations

Variants have different characteristics—results not directly comparable across variants

Multilingual variant (300 instances, 9 languages) may have lower per-language coverage than Python-only

Multimodal variant (517 instances) is smaller than Verified (500) and may not be representative

What makes it unique

Provides five distinct benchmark variants (Verified, Lite, Full, Multilingual, Multimodal) enabling evaluation at different scales and across languages/modalities; Lite variant (300 instances) optimized for cost-constrained evaluation

vs alternatives

More flexible than single-variant benchmarks because researchers can choose appropriate scope; Multilingual and Multimodal variants address gaps in language and modality coverage that most code benchmarks lack

reference agent implementations with open-source baselines

Medium confidence

Provides open-source reference implementations (SWE-agent, mini-SWE-agent) that serve as baselines for the benchmark. mini-SWE-agent v2 achieves 65% resolution on SWE-bench Verified in ~100 lines of Python, providing a minimal viable agent architecture that researchers can extend or compare against.

Solves for

Understand the minimal architecture needed to solve SWE-bench issuesUse reference implementation as a starting point for building my own agentEstablish a baseline to measure improvement againstStudy how agent design choices (scaffolding, tool use, reasoning) affect performance

Best for

Researchers new to agent development wanting a concrete starting point

Teams building custom agents and needing a reference implementation to compare against

Open-source maintainers contributing improvements to SWE-agent

Requires

Python 3.x environment

Access to SWE-agent or mini-SWE-agent GitHub repository

Understanding of agent-based iterative problem solving

Limitations

mini-SWE-agent v2 (65% on Verified) is not SOTA—larger proprietary agents likely perform better

Reference implementation details not provided in materials—unclear what scaffolding or tool use it includes

No documentation of how mini-SWE-agent v2 achieves 65% in 100 lines—likely uses specific design patterns not obvious from code length

What makes it unique

Provides minimal viable agent (mini-SWE-agent v2: 65% in ~100 lines) as reference, enabling researchers to understand core agent patterns without complex scaffolding; open-source implementations enable community contributions and reproducibility

vs alternatives

More accessible than proprietary agent implementations because code is open-source and minimal; enables researchers to understand agent design patterns without reverse-engineering from leaderboard results

per-repository and per-language performance breakdown

Medium confidence

Leaderboard provides granular performance metrics broken down by source repository and programming language, enabling identification of which repositories or language domains agents struggle with. Visualizations show resolved instances per repository and per-language resolution rates, supporting targeted analysis of agent weaknesses.

Solves for

Identify which repositories my agent performs poorly on and investigate whyUnderstand whether my agent generalizes equally well across different Python codebasesCompare agent performance on different programming languages (Multilingual variant)Prioritize improvements by focusing on high-failure repositories or languages

Best for

Agent developers debugging systematic failures in specific repositories

Teams evaluating whether agents generalize across different code styles and domains

Researchers studying how repository characteristics (size, complexity, language) affect agent performance

Requires

Access to leaderboard breakdown visualizations

Understanding of which repositories are included in benchmark

Limitations

Breakdown methodology not documented—unclear if per-repository metrics are statistically significant with small sample sizes

No access to individual failed instances or agent reasoning traces for debugging

Repository selection bias not addressed—'popular' repositories may not represent long-tail or domain-specific code

What makes it unique

Provides per-repository and per-language breakdowns on leaderboard, enabling fine-grained analysis of agent performance across different code domains; supports both Python-only (Verified, Lite, Full) and multilingual (Multilingual variant) analysis

vs alternatives

More diagnostic than single aggregate metric because it reveals systematic weaknesses in specific repositories or languages; enables targeted improvement efforts rather than blind optimization

computational cost tracking and optimization metrics

Medium confidence

Tracks and reports computational cost metrics alongside resolution rate, including step counts, API calls, and execution time. Leaderboard scatter plots visualize the Pareto frontier of agents achieving high resolution with low cost, enabling evaluation of cost-performance tradeoffs.

Solves for

Understand the computational cost of my agent relative to its performanceIdentify agents that achieve high resolution with minimal computational overheadOptimize my agent's cost-performance ratio by analyzing step counts and API usageMake deployment decisions based on cost-performance tradeoffs (accuracy vs inference cost)

Best for

Teams deploying agents in cost-constrained environments (limited API budget, latency requirements)

Researchers studying efficiency of different agent architectures

Companies evaluating ROI of agent-based code generation vs manual engineering

Requires

Agent implementation that reports step counts and cost metrics

Access to leaderboard scatter plots showing cost-performance tradeoffs

Limitations

Cost metrics definition not documented—unclear how steps, API calls, and time are measured and normalized

No breakdown of cost by component (LLM inference, code execution, tool use)

Cost tracking methodology may vary across agents, making cross-agent comparison unreliable

What makes it unique

Treats computational cost as first-class metric alongside resolution rate, visualizing cost-performance tradeoffs via scatter plots; enables evaluation of agent efficiency, not just accuracy

vs alternatives

More practical than accuracy-only benchmarks because it accounts for deployment cost; Pareto frontier visualization helps identify agents that are both accurate and efficient

institutional support and funding transparency

Medium confidence

Benchmark is supported by major institutions (Open Philanthropy, AWS, Modal, Andreessen Horowitz, OpenAI, Anthropic), providing resources for benchmark maintenance, leaderboard hosting, and dataset curation. Institutional backing suggests long-term sustainability and potential for bias toward supported models.

Solves for

Assess the reliability and longevity of the benchmark based on institutional supportUnderstand potential biases in benchmark design or leaderboard rankingsIdentify which organizations have vested interests in benchmark results

Best for

Researchers evaluating benchmark credibility and potential conflicts of interest

Teams making long-term decisions based on benchmark results

Requires

Awareness of institutional supporters and their potential interests

Limitations

Institutional support not explicitly documented in benchmark materials—inferred from website

No disclosure of how institutional support influences benchmark design or leaderboard curation

Potential bias toward proprietary models from OpenAI and Anthropic not addressed

What makes it unique

Backed by major institutions (Open Philanthropy, AWS, Modal, a16z, OpenAI, Anthropic) providing resources and credibility; institutional support enables long-term maintenance but introduces potential bias toward supported models

vs alternatives

More sustainable than independent benchmarks due to institutional backing; however, potential conflicts of interest (OpenAI, Anthropic) not explicitly addressed

recent benchmark extensions and research directions

Medium confidence

Benchmark ecosystem includes recent extensions (CodeClash for goal-oriented evaluation, SWE-smith for custom model training, Multimodal variant for visual elements) and evolving research directions. CodeClash (11/2025) reframes agents as 'goal-oriented developers' rather than task-solvers, suggesting shifts in evaluation methodology.

Solves for

Understand emerging evaluation paradigms beyond binary issue resolutionTrain custom software engineering models using SWE-smith frameworkEvaluate agents on goal-oriented tasks that may better reflect real-world developmentStay informed about benchmark evolution and new evaluation dimensions

Best for

Researchers exploring new agent evaluation paradigms

Teams building custom software engineering models

Organizations tracking benchmark evolution and methodology changes

Requires

Access to recent benchmark variants and extensions

Understanding of new evaluation paradigms (goal-oriented vs task-based)

Limitations

Recent extensions (CodeClash, SWE-smith) not fully documented in provided materials

Unclear how new evaluation paradigms relate to original SWE-bench Verified results

No guidance on when to use original vs extended benchmarks

What makes it unique

Actively evolving benchmark ecosystem with recent extensions (CodeClash for goal-oriented evaluation, SWE-smith for custom model training, Multimodal variant); suggests benchmark is not static but adapting to emerging research directions

vs alternatives

More forward-looking than static benchmarks because it includes research extensions exploring new evaluation paradigms; enables evaluation of agents on emerging task formulations

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

Related Artifactssharing capabilities

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AI coding dream team of agents for VS Code. Claude Code + openai Codex collaborate in brainstorm mode, debate solutions, and synthesize the best approach for your code.

multi-agent collaborative code generation with debate synthesisincremental code refinement with agent feedback loops

2 shared capabilities

Product16

varies

based on the model used by the agent.

software-engineering-task-benchmark-evaluation

1 shared capability

Benchmark42

SWE-bench

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

real-world github issue evaluation dataset construction

1 shared capability

Product17

Demo

[Discord](https://discord.com/invite/AVEFbBn2rH)

autonomous-github-issue-resolution-via-agent

1 shared capability

Agent13

AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors

[Twitter](https://twitter.com/Agentverse71134)

performance-based agent evaluation and feedback

1 shared capability

Product18

Twitter thread describing the system

</details>

code review and refinement with multi-agent critique loops

1 shared capability

Best For

✓AI research teams building autonomous coding agents
✓Companies evaluating LLM-based code generation tools for production use
✓Open-source maintainers benchmarking their own agent implementations
✓Autonomous coding agents that need to validate solutions via code execution
✓Research teams studying agent behavior through execution traces
✓Teams building agents that must handle runtime errors and adapt solutions
✓Researchers publishing agent benchmarks and needing public comparison
✓Teams evaluating which agent to deploy based on accuracy vs cost

Known Limitations

⚠Binary pass/fail metric provides no credit for partial solutions or near-misses
⚠Restricted to Python repositories; generalization to other languages requires separate Multilingual variant
⚠High contamination risk since issues sourced from public GitHub data likely in training sets of large models
⚠No measurement of code quality, efficiency, or maintainability—only resolution
⚠Verification methodology and criteria for 'human-verified' not publicly documented
⚠No statistical significance testing, confidence intervals, or variance reporting across runs

Requirements

Docker runtime for sandboxed code executionPython 3.x environment for agent implementationAccess to SWE-bench Verified dataset (500 instances)Ability to implement agent-based iterative loop with code execution feedbackDocker daemon running on evaluation infrastructureAgent implementation capable of parsing code execution outputRepository context (source code, tests, dependencies) available in sandboxWeb browser access to https://www.swebench.com

Input / Output

Accepts: GitHub issue text (title + description), Repository context (source code, test files, dependency manifests), Issue metadata (labels, assignees, timestamps), Agent-generated Python code, Test commands (pytest, unittest, etc.), Repository state snapshots, Agent evaluation results (resolution rate, step counts, cost metrics), Agent metadata (name, model, release date, type), Raw GitHub issues from popular Python repositories, Issue context (code, tests, dependencies), GitHub issues in Python (Verified, Lite, Full), GitHub issues in 9 languages (Multilingual), GitHub issues with visual elements (Multimodal), GitHub issue text and repository context, Agent evaluation results per repository and language, Agent execution traces (step counts, API calls, execution time), Agent implementations for new evaluation paradigms

Produces: Binary resolution status (resolved/not resolved), Percentage resolved across test set, Per-repository resolution breakdown, Computational cost metrics (steps taken, API calls, execution time), Execution stdout/stderr, Test pass/fail results, Exit codes, Execution time metrics, Step count for cost tracking, Interactive leaderboard visualizations, Scatter plots (resolved vs cost), Bar charts (resolved by repository), Comparison tables, Exportable JSON/PNG snapshots, Curated set of 500 verified solvable issues, Binary verification status per issue, Resolution rate per variant, Per-language resolution breakdown (Multilingual), Per-modality performance (Multimodal), Agent code (Python), Pull request or code changes resolving the issue, Bar charts showing resolved instances per repository, Per-language resolution rates, Heatmaps of agent performance across repositories, Cumulative cost distribution, Cumulative step distribution, Cost-performance scatter plots, Cost per resolved instance metric, Transparency information about benchmark governance and funding, Results on extended benchmark variants

UnfragileRank

Adoption70%(25% weight)

Quality23%(35% weight)

Ecosystem30%(25% weight)

Match Graph10%(10% 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

10 capabilities

Visit SWE-bench Verified→

About

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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Capabilities10 decomposed

real-world github issue resolution evaluation

Medium confidence

Solves for

Best for

AI research teams building autonomous coding agents

Companies evaluating LLM-based code generation tools for production use

Open-source maintainers benchmarking their own agent implementations

Requires

Docker runtime for sandboxed code execution

Python 3.x environment for agent implementation

Access to SWE-bench Verified dataset (500 instances)

Limitations

Binary pass/fail metric provides no credit for partial solutions or near-misses

Restricted to Python repositories; generalization to other languages requires separate Multilingual variant

High contamination risk since issues sourced from public GitHub data likely in training sets of large models

What makes it unique

vs alternatives

agent-based iterative code execution with feedback loops

Medium confidence

Solves for

Best for

Autonomous coding agents that need to validate solutions via code execution

Research teams studying agent behavior through execution traces

Teams building agents that must handle runtime errors and adapt solutions

Requires

Docker daemon running on evaluation infrastructure

Agent implementation capable of parsing code execution output

Repository context (source code, tests, dependencies) available in sandbox

Limitations

Docker overhead adds latency per execution step (exact timing not documented)

Sandbox isolation prevents agents from accessing external APIs or network resources

No built-in persistence across evaluation runs—each instance starts fresh

What makes it unique

vs alternatives

More secure than in-process code execution and provides better isolation than subprocess-based sandboxing; enables cost tracking that static code generation benchmarks cannot measure

multi-dimensional leaderboard with cost-performance tradeoffs

Medium confidence

Solves for

Best for

Researchers publishing agent benchmarks and needing public comparison

Teams evaluating which agent to deploy based on accuracy vs cost

Open-source maintainers tracking their agent's competitive position

Requires

Web browser access to https://www.swebench.com

Agent evaluation results in format compatible with leaderboard submission (format unknown)

Limitations

Leaderboard submission process not documented—unclear how new agents are added

No statistical significance testing or confidence intervals reported

Filtering by 'Open Scaffold' suggests some agents use different scaffolding, but impact on comparability unknown

What makes it unique

vs alternatives

human-verified issue solvability curation

Medium confidence

Solves for

Best for

Researchers needing a high-quality, smaller benchmark for agent evaluation

Teams with limited computational budget wanting to evaluate on curated instances

Benchmarking studies requiring ground truth that issues are genuinely solvable

Requires

Access to SWE-bench Verified dataset (500 curated instances)

Understanding that this is a subset of full SWE-bench with different characteristics

Limitations

Verification methodology and criteria not publicly documented—unclear what makes an issue 'verifiable'

Verifier expertise and background not disclosed—potential for subjective or inconsistent verification

Smaller test set (500 vs 2,294 full) may have higher variance and lower statistical power

What makes it unique

vs alternatives

More reliable than raw GitHub issues because verification ensures solvability; smaller than full SWE-bench (2,294) enabling faster evaluation cycles, but with potential loss of coverage

multi-variant benchmark suite with language and modality coverage

Medium confidence

Solves for

Best for

Teams with varying computational budgets (Lite for rapid iteration, Full for comprehensive evaluation)

Multilingual agent developers needing cross-language benchmarking

Vision-language model researchers evaluating agents on multimodal software engineering tasks

Requires

Access to specific benchmark variant(s) matching evaluation goals

Understanding of variant differences and appropriate use cases

Limitations

Variants have different characteristics—results not directly comparable across variants

Multilingual variant (300 instances, 9 languages) may have lower per-language coverage than Python-only

Multimodal variant (517 instances) is smaller than Verified (500) and may not be representative

What makes it unique

vs alternatives

reference agent implementations with open-source baselines

Medium confidence

Solves for

Best for

Researchers new to agent development wanting a concrete starting point

Teams building custom agents and needing a reference implementation to compare against

Open-source maintainers contributing improvements to SWE-agent

Requires

Python 3.x environment

Access to SWE-agent or mini-SWE-agent GitHub repository

Understanding of agent-based iterative problem solving

Limitations

mini-SWE-agent v2 (65% on Verified) is not SOTA—larger proprietary agents likely perform better

Reference implementation details not provided in materials—unclear what scaffolding or tool use it includes

No documentation of how mini-SWE-agent v2 achieves 65% in 100 lines—likely uses specific design patterns not obvious from code length

What makes it unique

vs alternatives

per-repository and per-language performance breakdown

Medium confidence

Solves for

Best for

Agent developers debugging systematic failures in specific repositories

Teams evaluating whether agents generalize across different code styles and domains

Researchers studying how repository characteristics (size, complexity, language) affect agent performance

Requires

Access to leaderboard breakdown visualizations

Understanding of which repositories are included in benchmark

Limitations

Breakdown methodology not documented—unclear if per-repository metrics are statistically significant with small sample sizes

No access to individual failed instances or agent reasoning traces for debugging

Repository selection bias not addressed—'popular' repositories may not represent long-tail or domain-specific code

What makes it unique

vs alternatives

More diagnostic than single aggregate metric because it reveals systematic weaknesses in specific repositories or languages; enables targeted improvement efforts rather than blind optimization

computational cost tracking and optimization metrics

Medium confidence

Solves for

Best for

Teams deploying agents in cost-constrained environments (limited API budget, latency requirements)

Researchers studying efficiency of different agent architectures

Companies evaluating ROI of agent-based code generation vs manual engineering

Requires

Agent implementation that reports step counts and cost metrics

Access to leaderboard scatter plots showing cost-performance tradeoffs

Limitations

Cost metrics definition not documented—unclear how steps, API calls, and time are measured and normalized

No breakdown of cost by component (LLM inference, code execution, tool use)

Cost tracking methodology may vary across agents, making cross-agent comparison unreliable

What makes it unique

Treats computational cost as first-class metric alongside resolution rate, visualizing cost-performance tradeoffs via scatter plots; enables evaluation of agent efficiency, not just accuracy

vs alternatives

More practical than accuracy-only benchmarks because it accounts for deployment cost; Pareto frontier visualization helps identify agents that are both accurate and efficient

institutional support and funding transparency

Medium confidence

Solves for

Best for

Researchers evaluating benchmark credibility and potential conflicts of interest

Teams making long-term decisions based on benchmark results

Requires

Awareness of institutional supporters and their potential interests

Limitations

Institutional support not explicitly documented in benchmark materials—inferred from website

No disclosure of how institutional support influences benchmark design or leaderboard curation

Potential bias toward proprietary models from OpenAI and Anthropic not addressed

What makes it unique

vs alternatives

More sustainable than independent benchmarks due to institutional backing; however, potential conflicts of interest (OpenAI, Anthropic) not explicitly addressed

recent benchmark extensions and research directions

Medium confidence

Solves for

Best for

Researchers exploring new agent evaluation paradigms

Teams building custom software engineering models

Organizations tracking benchmark evolution and methodology changes

Requires

Access to recent benchmark variants and extensions

Understanding of new evaluation paradigms (goal-oriented vs task-based)

Limitations

Recent extensions (CodeClash, SWE-smith) not fully documented in provided materials

Unclear how new evaluation paradigms relate to original SWE-bench Verified results

No guidance on when to use original vs extended benchmarks

What makes it unique

vs alternatives

More forward-looking than static benchmarks because it includes research extensions exploring new evaluation paradigms; enables evaluation of agents on emerging task formulations

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

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SWE-bench Verified

Capabilities10 decomposed

real-world github issue resolution evaluation

agent-based iterative code execution with feedback loops

multi-dimensional leaderboard with cost-performance tradeoffs

human-verified issue solvability curation

multi-variant benchmark suite with language and modality coverage

reference agent implementations with open-source baselines

per-repository and per-language performance breakdown

computational cost tracking and optimization metrics

institutional support and funding transparency

recent benchmark extensions and research directions

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Known Limitations

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Input / Output

UnfragileRank

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SWE-bench Verified

Capabilities10 decomposed

real-world github issue resolution evaluation

agent-based iterative code execution with feedback loops

multi-dimensional leaderboard with cost-performance tradeoffs

human-verified issue solvability curation

multi-variant benchmark suite with language and modality coverage

reference agent implementations with open-source baselines

per-repository and per-language performance breakdown

computational cost tracking and optimization metrics

institutional support and funding transparency

recent benchmark extensions and research directions

Related Artifactssharing capabilities

Mysti

varies

SWE-bench

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AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors

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Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

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