AI ToolDead Man Walking

DeepSWE Review

DeepSWE is a robust AI coding benchmark designed to evaluate genuine problem-solving capabilities of agentic AI on novel, unseen scenarios.

shipped Jun 1, 2026aifreemium

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1Evaluates AI coding agents on 113 original, handcrafted tasks.

2Achieves a false positive rate of 0.3% and false negative rate of 1.1% in verification.

3OpenAI's GPT-5.5 led the initial leaderboard with a 70% success rate.

4Tasks are sourced from 91 active open-source repositories across five languages.

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Stork Quadrant

Dead Man Walking· 0/100

An LLM can do most of what this tool's UI promises. No moat, no agent presence.

“This is a benchmark tool, which means its core product is a curated set of problems and a scoring harness. LLMs can generate novel coding problems, and the open-source community already produces competing benchmarks freely. There is no proprietary data, no network effect, no regulatory gate. This will be commoditized fast.”
— Claude Sonnet 4.6, scored 2026-06-01

Defensibility · 0/100

Physical-world coupling
Regulatory moat
Network liquidity
Proprietary refreshing data
High-trust catastrophic workflows
Multi-party coordination
Brand / community / taste

An LLM alone could replace

Generate coding problems or test cases for evaluating AI agents
Assess whether an AI solution is correct by reviewing code output
Produce benchmark-style prompts to probe edge cases in software engineering tasks
Summarize or compare AI model performance on coding tasks

Agent-Readiness · 0/100

Verified MCP
Listed on agent surfaces
Usage-based pricing
Headless agent auth
Public OpenAPI
Active changelog
llms.txt

How to defend

The only real move is to own a continuously refreshing problem set sourced from real production codebases under license — problems that can't be scraped or replicated — and sell access to that corpus to model labs who need eval data they can trust hasn't leaked into training sets.

Ship an MCP server and list it on Stork — biggest single point gain (+25).
Get listed in the Anthropic MCP registry, Cursor, or Claude Desktop (+20).
Add a usage-based or per-call tier; per-seat-only pricing dies when agents replace seats (+15).
Expose API-key auth with a self-serve sandbox tier; remove sales-call gates (+15).
Publish an OpenAPI spec at /openapi.json or /.well-known/openapi (+10).

How this score is computed →See the full quadrant How to defend

DeepSWE at a Glance

Pricing

freemium

Key Features

Evaluates AI coding agents on 113 original, handcrafted tasks. · Achieves a false positive rate of 0.3% and false negative rate of 1.1% in verification. · OpenAI's GPT-5.5 led the initial leaderboard with a 70% success rate.

Alternatives

SWE-bench, Snorkel Agentic Coding benchmark, ProjDevBench

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Markdown

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overview

What is DeepSWE?

DeepSWE is an AI coding benchmark tool developed by Datacurve that enables researchers, model providers, and engineering teams to evaluate the genuine problem-solving capabilities of agentic AI. It focuses on novel, unseen scenarios and long-horizon software engineering tasks to provide contamination-free assessments. DeepSWE functions as a benchmark for measuring the ability of AI coding agents to handle realistic software development challenges. It assesses an AI's capacity for contextual understanding, logical reasoning, and adherence to best practices in code generation. The benchmark was officially released by Datacurve around May 2026, generating discussion due to its critique of existing benchmarks and its novel evaluation approach. It was developed to overcome perceived critical flaws in existing evaluations, such as data contamination, unrealistic prompts, and unreliable grading systems.

quick facts

Quick Facts

Attribute	Value
Developer	Datacurve
Business Model	Freemium
Pricing	Freemium
Platforms	Web
API Available	No
Founded	May 2026

features

Key Features of DeepSWE

DeepSWE incorporates several key features designed to provide a comprehensive and reliable evaluation of AI coding agents on complex software engineering tasks.

1Evaluates genuine problem-solving capabilities of agentic AI on novel, unseen scenarios.
2Provides a contamination-free benchmark with 113 original, handcrafted tasks.
3Assesses AI coding agents on realistic, long-horizon software engineering tasks.
4Evaluates agents' ability in repository exploration and multi-file changes.
5Measures behavioral correctness and verification of generated code.
6Scores new AI coding agents and reproduces the benchmark leaderboard.
7Offers insights into behavioral tendencies and performance of AI coding models.
8Tasks are sourced from 91 active open-source repositories across five languages (TypeScript, Go, Python, JavaScript, Rust).

use cases

Who Should Use DeepSWE?

DeepSWE is designed for a range of professionals and organizations involved in the development and evaluation of AI coding technologies, providing specific benefits for each target persona.

1**Researchers:** For evaluating frontier coding agents on original, long-horizon software engineering tasks and comparing AI coding agents on tasks closer to real software engineering work than short coding puzzles.
2**Model Providers:** To score new AI coding agents, reproduce the benchmark leaderboard, and provide insights into the behavioral tendencies and performance of AI coding models.
3**Engineering Teams & Developers:** For helping teams assess agents' ability in repository exploration, multi-file changes, behavioral correctness, and verification, leading to improved code quality and reliability.
4**Business Owners & Enterprise Buyers:** To identify more capable AI agents, indirectly contributing to faster software development by enabling automation of complex coding tasks and intelligent suggestions.

pricing

DeepSWE Pricing & Plans

DeepSWE operates on a freemium model, allowing users to access core benchmarking functionalities. Specific details regarding paid tiers or usage-based costs are not publicly detailed beyond the freemium designation.

1Freemium: Access to core benchmarking functionalities.

competitors

DeepSWE vs Competitors

DeepSWE positions itself as a superior alternative to existing AI coding benchmarks by addressing critical flaws such as data contamination and unreliable grading systems, offering distinct advantages in evaluation methodology.

SWE-benchOn Stork Compare

SWE-bench evaluates AI agents on their ability to resolve real-world software engineering issues sourced from GitHub, focusing on data contamination resistance and realistic problem-solving.

Similar to DeepSWE, SWE-bench focuses on evaluating agentic AI's problem-solving in coding. Its emphasis on real-world GitHub issues provides a large, diverse dataset, while DeepSWE emphasizes 'novel, unseen scenarios.' SWE-bench is a public benchmark, often used by researchers and companies to report model performance.

Snorkel Agentic Coding benchmark↗

This benchmark assesses AI agents on multi-step coding tasks in fully sandboxed environments, evaluating long-horizon planning, error recovery, and diverse software engineering capabilities.

Like DeepSWE, Snorkel's benchmark targets agentic AI and problem-solving in coding. It distinguishes itself by focusing on multi-step tasks and robust error recovery within sandboxed environments, aligning with DeepSWE's 'genuine problem-solving capabilities' on complex scenarios.

ProjDevBenchOn Stork Compare

ProjDevBench evaluates AI coding agents on their ability to perform end-to-end project development, from system architecture design to iterative solution refinement.

While DeepSWE focuses on novel, unseen scenarios for problem-solving, ProjDevBench extends the scope to full project development, requiring agents to plan, implement, and integrate components at a higher level of abstraction. Both aim to assess deep coding capabilities beyond simple function generation.

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Frequently Asked Questions

+What is DeepSWE?

+Is DeepSWE free?

DeepSWE operates on a freemium model, providing access to core benchmarking functionalities without an upfront cost. Specific details on paid tiers or usage-based pricing are not publicly disclosed.

+What are the main features of DeepSWE?

DeepSWE's main features include evaluating genuine problem-solving on novel, unseen scenarios, providing a contamination-free benchmark with 113 original tasks, assessing agents on realistic long-horizon software engineering tasks, and measuring abilities in repository exploration, multi-file changes, and behavioral correctness. It also scores new AI agents and offers insights into their performance.

+Who should use DeepSWE?

DeepSWE is intended for researchers, model providers, engineering teams, and developers who need to rigorously evaluate AI coding agents. It helps assess agent performance on complex, real-world software engineering tasks and provides insights into their problem-solving capabilities.

+How does DeepSWE compare to alternatives?

DeepSWE differentiates itself from benchmarks like SWE-bench by offering 113 original, handcrafted, contamination-free tasks from 91 active open-source repositories. Compared to Snorkel Agentic Coding, DeepSWE focuses on novel scenarios and behavioral correctness, while ProjDevBench extends evaluation to full end-to-end project development.

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