DeepSWE operates on a freemium model, offering access to its core benchmark tasks and evaluation framework. While a free component is available, specific details on paid tiers or advanced features are not publicly disclosed as of late 2026.

How does DeepSWE compare to alternatives?

DeepSWE is positioned as a next-generation benchmark that addresses shortcomings of predecessors like SWE-Bench Pro by offering contamination-free, longer-horizon tasks. Unlike broader evaluation platforms such as Galileo AI or DeepEval, DeepSWE specializes in coding benchmarks. It differs from ProjDevBench by focusing on novel problem-solving rather than end-to-end project development, and from AgentPerf by prioritizing agent capability over hardware performance.

AI Tool

DeepSWE Review

Name: DeepSWE
Availability: OnlineOnly
Author: Stork.AI

DeepSWE is a contamination-free benchmark for evaluating AI coding agents on realistic, long-horizon software engineering tasks, focusing on original and novel scenarios.

shipped Jun 1, 2026aifreemium

Why it matters

1DeepSWE evaluates AI coding agents on 113 software engineering tasks from 91 active open-source repositories.

2Released by Datacurve in May 2026, it addresses perceived flaws in existing AI coding evaluations like SWE-Bench Pro.

3Initial leaderboard results in late May 2026 showed OpenAI's GPT-5.5 leading with a 70% success rate.

4DeepSWE tasks require 5.5 times more code on average and have significantly shorter prompts than previous benchmarks.

Stork’s verdict on DeepSWE

DeepSWE evaluates genuine problem-solving capabilities for coding agents, though some users question its model rankings.

DeepSWE reviewed by Stork AI · stork.ai/en/deepswe

overview

What is DeepSWE?

DeepSWE is an AI coding benchmark tool developed by Datacurve that enables researchers, model providers, and engineering teams to evaluate genuine problem-solving capabilities of agentic AI on novel, unseen scenarios. It presents 113 software engineering tasks drawn from 91 active open-source repositories across five programming languages: TypeScript, Go, Python, JavaScript, and Rust.

features

Key Features of DeepSWE

DeepSWE provides a comprehensive evaluation framework designed to rigorously assess the performance of frontier AI coding agents. Its features are built to ensure a contamination-free and realistic assessment of AI models' coding capabilities on complex, real-world software engineering tasks.

Evaluates genuine problem-solving capabilities of agentic AI on novel, unseen scenarios.
Provides a contamination-free benchmark for reliable evaluation, preventing models from 'cheating' on seen data.
Focuses on realistic, long-horizon software engineering tasks, requiring end-to-end problem resolution.
Assesses agents' ability in repository exploration, multi-file changes, behavioral correctness, and verification.
Offers insights into the behavioral tendencies and performance of AI coding models.
Utilizes 113 tasks derived from 91 active open-source repositories.
Supports evaluation across TypeScript, Go, Python, JavaScript, and Rust programming languages.
Employs a standardized harness called mini-swe-agent for consistent evaluation.

use cases

Who Should Use DeepSWE?

DeepSWE is designed for various stakeholders in the AI and software development ecosystem who require authoritative and reliable evaluation of AI coding agents. Its insights are critical for making informed decisions regarding AI model development, deployment, and strategic integration.

Researchers & Model Providers: For evaluating frontier coding agents, comparing AI models on realistic software engineering tasks, and advancing the state-of-the-art in AI coding.
Engineering Teams & Leaders: To assess agents' ability in repository exploration, multi-file changes, behavioral correctness, and verification, informing deployment decisions for AI-assisted development.
Developers: To score new AI coding agents, reproduce benchmark leaderboards, and gain insights into model performance and behavioral tendencies.
Business Owners & Enterprise Buyers: To understand the true capabilities of AI coding agents for strategic adoption, accelerated development cycles, and identifying suitable solutions for automated code generation or legacy code modernization.

how to use

How to Use DeepSWE

DeepSWE functions as an evaluation framework, primarily accessed through its open-source components on GitHub. Users can leverage the benchmark to test and compare AI coding agents against a standardized set of real-world software engineering tasks.

1Access the DeepSWE benchmark tasks and evaluation framework, which are available on GitHub.
2Utilize the standardized mini-swe-agent harness to run evaluations of AI coding agents.
3Submit AI agent solutions to the benchmark for scoring against predefined criteria and verifiers.
4Review leaderboard results and performance insights to understand the capabilities of evaluated models.
5Reproduce existing benchmark results or contribute new agent evaluations to the DeepSWE ecosystem.

pricing

DeepSWE Pricing & Plans

DeepSWE operates on a freemium model, providing access to its core benchmark tasks and evaluation framework. Specific details regarding paid tiers or advanced features, such as enhanced analytics or enterprise support, are not publicly disclosed as of late 2026.

Freemium: Core benchmark access and evaluation framework available via open-source components.
Paid Tiers: Specific pricing for advanced features or enterprise solutions are not publicly detailed.

Pros

+Provides a contamination-free benchmark design, preventing models from 'cheating' on seen data.
+Evaluates genuine problem-solving capabilities on novel, unseen, long-horizon software engineering tasks.
+Utilizes a diverse set of 113 tasks from 91 active open-source repositories across five programming languages.
+Offers robust evaluation of repository exploration, multi-file changes, behavioral correctness, and verification.
+Addresses perceived flaws and a 'benchmark trust crisis' in existing AI coding evaluations.
+Includes open-source components (tasks, evaluation framework, mini-swe-agent harness) available on GitHub.

Cons

−Specific pricing for advanced features or enterprise solutions is not publicly detailed as of late 2026.
−Some user discussions indicate skepticism regarding the accuracy of certain model rankings and reported cost calculations.
−An API is not available for programmatic integration, limiting direct automation.
−The benchmark's focus is solely on coding tasks, not broader AI agent evaluation or hardware performance metrics.
−Requires familiarity with GitHub and the mini-swe-agent harness for full utilization and reproduction of results.

Similar Tools

DeepSWE vs Competitors

DeepSWE is positioned as a next-generation benchmark that directly addresses the shortcomings of its predecessors and offers a more rigorous evaluation standard for AI coding agents. It differentiates itself through its focus on contamination-free, long-horizon, and novel software engineering tasks.

Galileo AIOn Stork Compare

Galileo AI provides a unified platform for evaluating, monitoring, and protecting GenAI applications and agents across their entire lifecycle, from development to production.

Galileo AI offers a comprehensive platform for agent evaluation and observability, similar to DeepSWE's goal of evaluating agentic AI. While DeepSWE focuses specifically on coding benchmarks for novel scenarios, Galileo AI provides broader evaluation and monitoring capabilities for various agentic behaviors, including tool orchestration and multi-step actions.

DeepEval (by Confident AI)On Stork Compare

DeepEval is an open-source, pytest-native LLM evaluation framework offering over 50 research-backed metrics for comprehensive agent evaluation across various use cases.

DeepEval is an open-source framework, aligning with DeepSWE's freemium model, and provides a programmatic way to evaluate AI agents, including their reasoning and action layers. DeepSWE specifically targets coding benchmarks for novel scenarios, whereas DeepEval offers a broader set of metrics for different AI agent behaviors, integrating directly into CI/CD workflows.

ProjDevBenchOn Stork Compare

ProjDevBench is an end-to-end benchmark designed to evaluate AI coding agents on their ability to develop complete, runnable software projects from high-level requirements.

ProjDevBench is a direct benchmark for evaluating AI coding agents on end-to-end project development, which closely mirrors DeepSWE's focus on evaluating problem-solving capabilities on novel coding scenarios. Unlike DeepSWE, which is described as a 'robust AI coding benchmark,' ProjDevBench is presented as a specific benchmark dataset and methodology for project-level evaluation.

Artificial Analysis AgentPerfOn Stork Compare

Artificial Analysis AgentPerf provides the industry's first multi-vendor open benchmarks for profiling real-world AI agent coding tasks, focusing on hardware performance under agentic workloads.

AgentPerf is a benchmark specifically for AI agent coding tasks, similar to DeepSWE. However, AgentPerf primarily measures hardware performance and concurrent agent support under real-world coding trajectories, using private test sets to prevent optimization, which aligns with 'novel scenarios.' DeepSWE focuses more broadly on the agent's problem-solving capabilities rather than the underlying hardware performance.

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