Explore how Big Pickle excels in the SWE Atlas Codebase QnA benchmark and its implications for developers.
In a recent evaluation using Scale AI's SWE Atlas Codebase QnA benchmark, Big Pickle, a free stealth model hosted on OpenCode Zen, achieved notable results. This assessment took place on August 11, 2026, utilizing the official open-source harness and task data derived from the SWE Atlas ethernet-module/">framework.
With a task resolve rate of 50.8% based on 63 of 124 tasks, Big Pickle has made waves in this competitive landscape. It not only outperformed every other entry in the Mini-SWE-Agent scaffold class but also bested the GPT entries associated with CodeX. Only the Claude models, operating within their Claude Code scaffold, secured higher scores.
Understanding the performance of Big Pickle through specific language and category metrics provides deeper insight into its capabilities.
The resolved rates for various programming languages shed light on Big Pickle's proficiencies. The statistics from the evaluation indicate:
TypeScript: 18 out of 31 tasks resolved successfully, resulting in a 58.1% success rate.
Python: With 16 out of 29 tasks resolved, Python saw a performance rate of 55.2%.
Go: For the Go programming language, the model resolved 19 out of 38 tasks, equating to a 50% success rate.
C: Big Pickle managed to resolve 10 out of 26 tasks, achieving a rate of 38.5%.
Next, let’s consider how Big Pickle performed across various categories of tasks:
In the area of Code Onboarding, Big Pickle attained a success rate of 60.7%, resolving 17 out of 28 tasks.
For Architecture & System Design, it achieved a 52.3% success rate by resolving 23 of 44 tasks.
In Root-Cause Analysis, Big Pickle resolved 17 out of 37 tasks, marking a success rate of 45.9%.
In the realm of Security, it achieved a 45.5% success rate by resolving 5 of 11 tasks.
API & Library Usage / Integration saw a lower success rate of 25%, with only 1 out of 4 tasks resolved.
The methodology adopted in this benchmark evaluation closely aligned with the published protocols by Scale AI. The following steps outline the comprehensive process used during the evaluation:
Initially, the necessary repositories were cloned from GitHub, namely the [SWE-Atlas](https://github.com/scaleapi/SWE-Atlas) and Laude Institute’s Harbor, ensuring the correct versioning under the defined conditions.
Using the harvested code, the environment was set up, which included important preflight checks to confirm the model and judging endpoints were correctly configured, alongside all scripts and configuration files that ensure seamless integration.
The total estimated cost for executing the full QnA run was approximately $70 based on reduced sandbox resources. Fully scaled operations, presumably, would involve expenditures approximately 2 to 3 times that amount. Additional costs accounted for about $25 associated with the Anthropic API employed for judging.
In-depth specifics regarding Big Pickle's task performances can be found in the logs generated during operation. The results, encapsulated in a CSV format, include details such as task ID, category, resolved state, aggregate rubric score, and the breakdown of rubrics passed versus total attempts.
Both the evaluations' results and verification logs provide comprehensive audit trails and quality assurance for transparency and reproducibility.
It should be noted that while these results provide significant insights, they should be interpreted cautiously. Scale AI explicitly requests treating the SWE Atlas benchmarks primarily as a measure of progress rather than a baseline for training objectives.
As the landscape of AI models and development tools continues to evolve, the performance metrics presented by Big Pickle highlight its potential in solving coding queries and how it stacks against its contemporaries.