As AI coding agents become increasingly capable, the need for rigorous, real-world evaluation has never been more critical. Today, we’re sharing details about the Snorkel Agentic Coding benchmark—a comprehensive evaluation suite designed to test whether agents can handle the full complexity of software engineering work.
We’ve been listening to our customers as they describe the challenges they face pushing the frontier of coding capabilities, and we’ve applied what we’ve learned from those conversations to developing a benchmark that delivers meaningful feedback about the strengths and weaknesses of even the most advanced models. The Snorkel Agentic Coding benchmark comprises 100 multi-step coding tasks, distributed across four difficulty tiers; a much larger dataset is available to customers upon request. A quick sampling of scores among the top performers on our leaderboard: Claude Opus 4.5: 58%, Gemini 3 Pro: 51.6%, and GPT 5.2: 49.4%. The breakdown below shows the success rate falls as expected with increasing task difficulty.
These tasks span the breadth of capabilities needed for real-world software engineering: from command-line operations and tool use to building, debugging, and refactoring complex codebases. The benchmark focuses on the key areas where coding assistants need to grow. Tasks range from typical software engineering challenges to advanced ML and data analytics work, build and dependency management, and more.
Each task evaluates not just whether an agent can write code, but whether it can plan across long horizons, track multiple subtasks, evaluate and execute its own solutions, and recover from errors or incorrect previous steps. Not only does this greater complexity increase task difficulty, it also yields insights into model behavior. One particularly interesting observation is that models have idiosyncratic tendencies regarding the number of turns needed to complete a task. Perhaps even more interesting is the observation that the number of steps taken does not strictly correlate with task difficulty or successful completion rate.
What sets this benchmark apart is the breadth of programming languages, command-line tools, data and configuration file formats that are tested, as listed here. Snorkel Agentic Coding also includes tasks that require coding in two or more languages to be completed successfully.
| Programming Languages | Command/ Tool Syntax | Config Files | Data Formats |
|
Python |
awk jq sed pandoc yq sh OpenSSL |
CMake CSS Terraform Makefile nginx Dockerfile Ansible |
YAML HTML JSON XML EJS Mustache protobuf |
Drawing on insights from our contributions to the Terminal-Bench project, we evaluate agents in fully sandboxed execution environments that provide dynamic feedback and context over long-horizon objectives. This isn’t about single-turn bug fixing or code completion—it’s about end-to-end software engineering.
Every task is paired with a human-validated reference solution, comprehensive unit tests, and scoring rubrics that assess both final outputs and the agent’s trajectory. Our experts confirm that each challenge is solvable in its environment and verify the reliability of all dependencies. This level of validation ensures that when an agent fails, it’s a meaningful signal about capability gaps, not environment issues.
We’ve built this benchmark to challenge even the most advanced frontier models while remaining useful across the cost-performance spectrum. The four difficulty tiers deliver meaningful feedback whether you’re pursuing Pareto-optimal results (-flash, -fast, -mini) or pushing the boundaries of frontier-level capabilities.
This calibration matters. A benchmark that only the best models can solve provides limited signal. One that’s too easy fails to differentiate capabilities. Our approach ensures that teams working with different models can extract actionable insights about where their agents excel and where they need improvement. For example, the breakdown below shows how some frontier models performed on tasks at each difficulty level.
Models are evaluated using the Pass@5 metric through the Harbor evaluation harness. Each task has a specific timeout limit, with an absolute maximum of 30 minutes for both agent and verifier. This methodology balances thoroughness with practicality—giving agents sufficient time to demonstrate their capabilities while maintaining realistic constraints for consistent, reproducible evaluation.
Looking at final accuracy alone hides the most important story: how agents fail when asked to solve agentic coding tasks. One of the key opportunities in creating the Snorkel Agentic Coding benchmark is in applying a systematic analysis framework to activity traces, yielding deeper insights into how and where agents struggle. Our analysis reveals specific aspects of agent behavior that can be used to shape the priorities for future hill-climbing. For example, Gemini 3 Pro runs into filesystem errors 19.3% of the time, and runtime errors 28.9% of the time, often because it moves on before checking whether earlier commands actually worked.
These failures highlight a broader limitation in how today’s models handle multi-step execution, error signals, and recovery in real-world coding tasks. On frontier-difficulty tasks, the dominant failure mode is not incorrect logic or syntax, but breakdowns in tool understanding and recovery. Even strong models struggle to debug unfamiliar tools under real execution feedback, especially when fixes require rethinking earlier assumptions rather than patching a single command.
In future posts, we will publish assessments of more models against the catalog of error types we’ve observed, and provide an in-depth analysis of the relationship between errors, error recovery, and task failures.
As AI’s jagged frontier makes it harder to predict where models will excel and where they will struggle, environment-based dynamic evaluation of their true capabilities becomes essential. The Snorkel Agentic Coding benchmark provides a window into how well these systems handle the messy, multi-faceted reality of software engineering—not just in isolated coding tasks, but across the full spectrum of activities that define the discipline.
At Snorkel, we use the insights gained from Agentic Coding and our other benchmarks to tailor custom datasets that augment and refine frontier models’ capabilities. We’re excited to see how this benchmark helps teams build more capable, reliable coding agents that can genuinely augment human developers in their work.
If your organization needs specialized, expert-verified, top quality data, come talk to us!
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