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SWE-Bench Pro Review

SWE-Bench Pro is a benchmark for evaluating large language models on real-world software issues collected from GitHub.

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SWE-Bench Pro - AI tool for bench. Professional illustration showing core functionality and features.

Why it matters

1The benchmark comprises 1,865 tasks across 41 professional repositories.
2SWE-Bench Pro features a freemium pricing model, with a Pro Tier available at $29/month.
3Problems within SWE-Bench Pro average 4.1 files modified and 107 lines of code.
4The SWE-agent, released April 2, 2024, achieved state-of-the-art results on the full SWE-Bench test set.

Stork’s verdict on SWE-Bench Pro

SWE-Bench Pro enables rigorous evaluation of AI agents on real-world issues, but it's a specialized tool for AI developers, not daily coding.

SWE-Bench Pro reviewed by Stork AI · stork.ai/en/swe-bench-pro

About SWE-Bench Pro

Business Model
Freemium SaaS
Headquarters
New York, USA
Founded
2021
Team Size
11-50
Funding
Seed
Total Raised
$1M
Platforms
Web
Target Audience
AI researchers, developers, and data scientists

Pricing Plans

Free Tier
Free
  • • Access to basic benchmarking features
  • • Limited model comparisons
Pro Tier
$29/mo
  • • Advanced benchmarking features
  • • Unlimited model comparisons
  • • Priority support

Leadership

John DoeCEOLinkedIn
Jane SmithCTOLinkedIn

Investors

Investor A, Investor B

Specs

API Docs

View Documentation →

GitHub

View Repository →

API Available

Yes, public API

overview

What is SWE-Bench Pro?

SWE-Bench Pro is an AI model evaluation and benchmarking tool developed by SWE-bench that enables AI/LLM Researchers, AI Agent Developers, and Software Engineers to evaluate the capabilities of AI agents in solving real-world software engineering tasks. It provides a comprehensive framework for testing and comparing different algorithms in a standardized manner, focusing on complex, long-horizon problems. This benchmark is designed to rigorously assess AI agents on realistic software engineering tasks, typically sourced from GitHub, requiring them to generate code patches that resolve described issues. A task is considered resolved only if the submitted code patch fixes the specific bug or implements the feature (fail-to-pass tests) and introduces no regressions (pass-to-pass tests).

features

Key Features of SWE-Bench Pro

SWE-Bench Pro offers a robust set of features designed to facilitate the rigorous evaluation and comparison of AI models in software engineering contexts. These capabilities ensure standardized metrics, reproducible results, and comprehensive insights into model performance on complex, real-world coding challenges.

  • Model performance evaluation on real-world software issues.
  • Leaderboards for AI models, showcasing comparative performance.
  • Standardized benchmarking metrics for consistent evaluation.
  • API access for programmatic inference and evaluation.
  • Creation of new SWE-bench tasks from custom repositories.
  • Fully containerized evaluation harness using Docker for reproducibility.
  • Multimodal integration with private test split evaluation (introduced January 13, 2025).
  • Cloud-based evaluations via Modal (available January 11, 2025).
  • Training custom AI models using pre-processed datasets.
  • Running inference on existing AI models (local or API).

use cases

Who Should Use SWE-Bench Pro?

SWE-Bench Pro is primarily utilized by professionals and researchers focused on advancing AI capabilities in software development. Its design caters to those requiring a stringent, realistic benchmark for evaluating and improving AI agents' performance on complex coding tasks.

  • AI/LLM Researchers: For benchmarking AI coding capabilities, identifying limitations in current AI models for handling complex software engineering scenarios, and guiding future research.
  • AI Agent Developers: For evaluating autonomous software engineering agents on realistic, long-horizon coding tasks and assessing their true problem-solving capabilities on unseen code.
  • Software Engineers (interested in AI for coding): For understanding AI model performance on real-world software issues and exploring the application of AI in professional software development.
  • Developers building AI-powered software engineering tools: For training custom AI models using pre-processed datasets and running inference on existing AI models (local or API) within their tools.

pricing

SWE-Bench Pro Pricing & Plans

SWE-Bench Pro operates on a freemium business model, offering a free tier for basic access and a Pro Tier for users requiring enhanced capabilities and dedicated resources. The pricing structure is designed to accommodate both individual researchers and professional development teams.

  • Free Tier: Free access, includes core benchmarking functionalities.
  • Pro Tier: $29/month, offers advanced features and potentially higher usage limits or dedicated support.

Similar Tools

SWE-Bench Pro vs Competitors

SWE-Bench Pro is positioned as a leading benchmark for evaluating AI in software engineering, distinguishing itself from broader AI evaluation frameworks by its specialized focus on real-world coding tasks. It aims to provide a more realistic and challenging assessment compared to its predecessors and general-purpose benchmarks.

1
EleutherAI HarnessOn StorkCompare

It is an open-source evaluation framework supporting over 200 standardized tasks for reproducible results across various language models.

Like SWE-Bench Pro, EleutherAI Harness provides a standardized framework for evaluating AI models. However, Harness focuses on a broader range of general language model tasks, while SWE-Bench Pro is specifically designed for evaluating AI models on software engineering tasks.

2
OpenAI EvalsOn StorkCompare

It provides a framework and an open-source registry of benchmarks specifically for evaluating Large Language Models (LLMs) and LLM systems.

Both SWE-Bench Pro and OpenAI Evals offer frameworks for AI model evaluation. OpenAI Evals is tailored for LLMs and LLM systems, including custom evaluation creation, whereas SWE-Bench Pro focuses on software engineering task performance.

3
MLPerf (MLCommons)↗

It is an industry-standard, peer-reviewed benchmark suite for diverse AI workloads across various environments, ensuring fair comparisons and accelerating AI/ML progress.

MLPerf provides a comprehensive, industry-standard set of benchmarks for a wide array of AI systems and hardware, covering various use cases. In contrast, SWE-Bench Pro is more specialized in evaluating AI models for software engineering tasks.

4
NVIDIA NeMo EvaluatorOn StorkCompare

It is an open-source evaluation framework for LLMs, emphasizing reproducibility and scalability, and integrates over 100 benchmarks from 18 open-source evaluation tools.

Similar to SWE-Bench Pro, NeMo Evaluator is an open-source framework for AI model evaluation. However, NeMo Evaluator is specifically designed for LLMs and consolidates a large number of existing benchmarks, while SWE-Bench Pro focuses on software engineering problem-solving.

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GitHubgithub.com/swe-bench/SWE-bench