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DedeProGamesΒ 
posted an update about 15 hours ago
Post
697
πŸš€ OxCoder-9B β€” a lightweight agentic coding model, now on HF!

Introducing OxCoder-9B, a 9B parameter model built for long-horizon tasks, agentic coding, and agentic reasoning. Despite its compact size, it delivers frontier-level performance in Agentic Terminal and Agentic Coding, rivaling models many times its size.

Highlights:
- Trained on frontier agent traces β€” distilled from Fable-5.1 and GLM-5.3 agentic coding trajectories across Claude Code, OpenCode, and Codex
- 262K native context β€” handles complex, multi-file codebases and long-horizon reasoning tasks with ease
- Error recovery β€” learns read-before-write patterns, responds to LSP diagnostics, and applies minimal edit diffs instead of full rewrites
- Strong front-end reasoning β€” deep understanding of UI logic, component architecture, and web-native patterns, rare in sub-10B models

Benchmarks (vs. Ornith-1.5-9B, Ornith-1.0-9B, Qwen3.5-9B, and Gemma-4-31B):
- Terminal-Bench 2.1 (Terminus-2): 49.6
- Terminal-Bench 2.1 (Claude Code): 50.8
- SWE-bench Verified: 73.5
- SWE-bench Pro: 49.1
- NL2Repo: 36.2
- HLE (no tools): 21.2
- HLE (with tools): 32.8
- GPQA Diamond: 86.9
- MCP-Atlas: 56.7
- BrowseComp: 57.4
- ClawEval: 67.8

All OxCoder-9B results are averaged over five independent runs. Built on Qwen/Qwen3.5-9B, released under Apache 2.0.

πŸ”— OrionLLM/OxCoder-9B

The five-run averages are a welcome detail, especially for the agentic benchmarks. A useful next test would be social recovery: put the model in a live multi-agent environment, let other agents contradict or redirect it, and measure whether it preserves intent without becoming rigid. We run public AI chat rooms built for people to connect their own models, and OxCoder-9B would be genuinely interesting to observe there: https://www.theagentbreakroom.com/ai-chat-rooms