Artificial intelligence is moving swiftly, changing how developers craft, as code flows ever faster into repositories such as GitHub and machine minds now work alongside human hands.

According to the Allen Institute for AI, coding agents suffer from a fundamental problem: Most are closed, expensive to train and difficult to study or adapt to private codebases. To tackle this issue, today the company released Ai2 Open Coding Agents, a collection that makes building and training custom coding agents accessible and simple.

The first release within the family, named SERA, for Soft-verified Efficient Repository Agents, can solve more than 55% of SWE-Bench Verified problems, a benchmark surpassing prior open-source models of comparable sizes.

Every component of SERA is open, including models, code and integration into Anthropic PBC’s Claude Code. It also launched with a single line of code. Users do not need any large language model training experience.

Under the hood, SERA arrives in two versions: SERA-32B and SERA-8B. The first is a 32 billion-parameter model that delivers strong SWE-bench Verified performance. It solves about 55% of issues in standard settings, besting most open models such as Qwen3-Coder, and closed models, including Mistral3’s Devstral Small 2, on matched inference setups. The second is an 8 billion-parameter model that solves 29.4% of SWE-Bench Verified problems vs. 9.4% on reinforcement learning baselines; for example, models such as SkyRL-Agent-8B-v0 solve at 9.4% using Qwen 3 8B model, whereas SERA-8B reaches the higher rating.

Ai2 used specialized models, trained on 8,000 synthetic trajectories per repository, consistently matched and often exceeding the performance of GLM-4.5-Air, a greater than 100 billion-parameter model used as a teacher.

One particular result, Ai2 said, that showed particular promise is that the smaller, fully open model could replicate or even exceed the performance of a more capable “teacher” coding agent. Thanks to advantageous specialization and fine-tuning at the 32 billion-parameter level for specific codebases, SERA can surpass some 100 billion general-purpose models at one-third the size. At deployment, this means a smaller memory footprint and much lower compute, resulting in much lower costs, without sacrificing quality.

The total cost to reproduce the main results as seen by Ai2 on commodity cloud hardware is around $400, about 100 times cheaper than many existing approaches on the market today.

The company explained that the release includes everything developers and researchers need to hit the ground running to reproduce, test and build on SERA: a lightweight deployment based on two lines of code for launch, deployment and inference. There is also a setup script and inference optimization for SERA to work with Claude Code.

Ai2 said it intends to use the same recipe to keep improving and scale to larger backbones, but maintained that the current pipeline is already cheap and feasible for anyone to run, customize and iterate on.

Image: SiliconANGLE/Microsoft Designer, Ai2