The practical promise of a coding model is not just whether it can generate code, but whether it can keep working through a real repository task without requiring a dense, much larger model. Flint targets that gap: its reported results combine competitive agentic coding scores with a comparatively small active parameter count, making local execution a central part of its proposition.
Key Capabilities
- On release evaluations, it scored 89.7% on SWE-bench Pro v2 and 74.2% on SWE-bench Verified, suggesting it can handle repository-level fixes as well as code generation.
- Its 39.8B-parameter mixture-of-experts architecture activates roughly 2.5B parameters per token. This reduces the amount of model computation per token, although the full checkpoint still has substantial storage and hardware requirements.
- Reasoning and multi-turn tool use are built for agent workflows: the model can inspect code, call tools, and iterate rather than only respond to a single prompt.
- A 262,144-token documented context configuration can accommodate large codebase excerpts and task history, but a large context window does not guarantee equal accuracy across its full length.
Who It Suits
Great fit if you want to run a coding agent locally, keep source code on your own hardware, and are willing to evaluate the model on your repositories and serving stack. Look elsewhere if you need a lightweight laptop-only setup with minimal configuration, guaranteed correctness, or consistently strong results on long tasks: the BF16 checkpoint is large, deployment depends on compatible inference runtimes, and generated code and tool actions still need review.
