DeepSWE - Best Benchmark For Evaluating AI Coding Agents?

DeepSWE - Best Benchmark For Evaluating AI Coding Agents?

Written by Nikos Vaggalis

Monday, 20 July 2026

DeepSWE is a high-fidelity benchmarking platform designed to evaluate the performance of frontier AI coding agents on complex long-horizon software engineering tasks.

Unlike traditional benchmarks that reuse existing GitHub data, this system utilizes original, contamination-free tasks across five major programming languages to prevent models from relying on memorized solutions, creating a much clearer separation between frontier coding models than older public benchmarks like SWE-bench.

The key advantages and structural differences that set DeepSWE apart from the SWE-bench family include:

Zero pre-training contamination<br>Unlike SWE-bench, which scrapes public GitHub issues and pull requests, DeepSWE tasks are authored entirely from scratch. Because the reference solutions are never merged back into public upstream repositories, models cannot memorize or retrieve them during pre-training.

No Git cheating. SWE-bench containers historically shipped the full .git history, enabling some agents to "cheat" by using git log to find and copy the gold patch directly. DeepSWE solves this by using a shallow clone with only the base commit and deleting future git history, making this shortcut impossible.

Deeper real-world complexity<br>Shorter prompts, larger tasks. DeepSWE prompts align with how human developers actually speak to agents;they are behavior-focused, natural, and roughly half the length of SWE-bench Pro’s prompts. But despite the shorter prompts, solving a DeepSWE task requires substantial problem-solving and code generation.

On average, DeepSWE reference solutions require editing 7 files and adding 668 lines of code. By comparison SWE-bench Pro tasks average 5 edited files and 120 lines of code while SWE-bench verified tasks average only 1 edited file and 10 lines of code.

Highly diverse codebases<br>DeepSWE evaluates agents across 91 active open-source repositories spanning 5 languages (TypeScript, Go, Python, JavaScript, and Rust). No single repository dominates the leaderboard, providing a realistic measure of how agents adapt to diverse, real-world codebases. In contrast, SWE-bench Pro is highly concentrated, spanning only 11 repositories, while SWE-bench Verified spans 12 repositories, focusing mostly on flagship, heavily maintained frameworks.

Encouraging realistic testing habits<br>The prompt templates in SWE-bench Pro explicitly command agents not to modify the testing logic. This discourages models from writing their own test files. Because DeepSWE’s prompts place no restrictions on test-writing, stronger models naturally engage in self-verification, writing and running project-specific tests on up to 85% of their runs.

Also there's no infrastructure tampering. In DeepSWE v1.1, grading runs in a clean, isolated container separate from the agent's work environment, preventing models from passing tests by monkey-patching the test framework or forcing early exits.

Based on the latest benchmark data, the models that perform best are categorized below by their pass rates and efficiency:

1. The Top tier (70% and Above)<br>These frontier models represent the highest level of coding capability on DeepSWE's long-horizon tasks:

gpt-5.6-sol [max]-The top-performing configuration on the leaderboard, achieving a 73% pass rate with an average cost of $8.39 per task and 60k output tokens.

claude-fable-5 [max]-Tied for second with a 70% pass rate. However, it is highly resource-intensive, requiring an average of 119k output tokens and costing $21.63 per task.

gpt-5.6-terra [max]-Also achieves a 70% pass rate but operates with extreme efficiency compared to Claude Fable 5, costing only $4.95 per task and using 72k output tokens.

2. High-performing tier (60% to 69%)<br>These models offer an exceptional balance of high accuracy and operational efficiency:

gpt-5.6-luna [max]-Scores a 67% pass rate and stands out as the most cost-efficient high-performing model on the leaderboard, costing only $3.03 per task.

gpt-5.5 [xhigh]-Scores 67% in the latest v1.1 setup (down slightly from 70% in v1). It is noted as the most token-efficient model in the upper tier, requiring a median of only 46k output tokens per trial.

3. Solid performers (50% to 59%)<br>These models are highly capable but sit just behind the absolute frontier:

claude-opus-4.8 [max]-Achieves a 59% pass rate at an average cost of $13.22 per task.

claude-sonnet-5 [max]-Reaches a 54% pass rate but is the most expensive model on the leaderboard to run, costing $26.40 per task and emitting a massive 214k output tokens.

grok-4.5 [high]-Delivers a 54% pass rate with incredible cost efficiency, costing only $2.42 per task.

muse-spark-1.1 [xhigh]-Scores 53% with a very low average cost of $2.36 per task.

gpt-5.4 [xhigh]-Scores 52% (down from 56% in v1) at an average cost of $5.65.

4. Lower-tier...