# DeepSWE - Best Benchmark For Evaluating AI Coding Agents? > Source: > Published: 2026-07-20 16:44:34+00:00 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 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 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 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 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) 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%) 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%) 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 and older frontier models Lighter or older generation models struggle significantly with DeepSWE's complexity. For example: gemini-3.5-flash [medium] scores 37% kimi-k2.7-code scores 31% claude-sonnet-4.6 [high] scores 30% and gemini-3.1-pro [high] lands at just 12% The data suggests that while top-tier models score closely, DeepSWE exposes critical differences in cost efficiency, token usage, and the ability to handle long-horizon engineering work. The gist is that the model that presented the best balance of high pass rate and cost efficiency was gpt-5.6-terra [max] for those seeking frontier-level performance without exorbitant costs. It achieves the exact same pass rate as the highly hyped claude-fable-5 [max] (70%) but does so at a fraction of the cost, saving nearly 77% compared to Fable 5's steep $21.63 per task. Note that when we look at the benchmark data, a "task" and its duration represent a substantial, real-world engineering effort rather than a simple code-completion prompt. Tasks for instance are considered: Add stepped slices for arrays and strings Add JSON Schema refs and dependency keywords Add dependency-aware async initialization to the container The Harness However, because real-world software engineering requires an agent to interact with a terminal, edit files, and run tests, a model cannot be tested in a vacuum; it must operate within some form of "agent scaffolding" or "harness". So to isolate the core reasoning and coding capabilities of the LLM itself (rather than the engineering tricks of a custom vendor wrapper) the primary DeepSWE leaderboard runs all models on a model-agnostic harness called mini-swe-agent. Under this standardized harness, every model gets the exact same bash tool and system prompt, with no custom vendor editing primitives (such as Claude’s str_replace_based_edit_tool or GPT's apply_patch). This allows for a clean comparison of model intelligence across identical conditions. As such, DeepSWE also pilot tests models running in their native, commercial agent environments. The benchmark framework (run via an evaluation tool called Pier) is designed to drive native agent products directly, including: Claude Code (driving Claude Opus or Fable) Codex CLI (driving GPT models) Gemini CLI Evaluating these native setups is highly realistic, as developers actually interact with these models through these sophisticated, model-native products in their day-to-day work. The benchmark reveals that the agent harness often matters just as much as the underlying model. Ultimately, the framework's emphasis on real-world complexity which requires agents to navigate large repositories and implement significant code changes based on concise natural language prompts, highlights a shift toward more realistic and reliable standards for measuring the true problem-solving capabilities of AI coding agents. 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