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Introducing OmniCoder-9B
We trained a 9B coding agent on 425K real agentic trajectories from Claude Opus 4.6, GPT-5.4, GPT-5.3-Codex, and Gemini 3.1 Pro across Claude Code, OpenCode, Codex, and Droid scaffolding.
Results:
- GPQA Diamond: 83.8 pass@1 (166/198), 86.4 pass@3 — above GPT-OSS-120B (80.1), Qwen3.5-9B (81.7), and Claude Haiku 4.5 (73)
- AIME 2025: 90 pass@5 (27/30)
- Terminal-Bench 2.0: 28.1 (25/89) — +8.1 points over base model
The key insight: We trained on what frontier agents actually do, real tool calls, real error recovery, real edit diffs. The model learns read-before-write patterns, responds to LSP diagnostic, and applies minimal diffs instead of full rewrites.
Base: Qwen3.5-9B. LoRA SFT, 4x H200, Axolotl, 99.35% packing efficiency.
Weights:
Tesslate huggingface.co/Tesslate/OmniCoder-9B
GGUF: huggingface.co/Tesslate/OmniCoder-9B-GGUF
Apache 2.0. Run it locally.
We trained a 9B coding agent on 425K real agentic trajectories from Claude Opus 4.6, GPT-5.4, GPT-5.3-Codex, and Gemini 3.1 Pro across Claude Code, OpenCode, Codex, and Droid scaffolding.
Results:
- GPQA Diamond: 83.8 pass@1 (166/198), 86.4 pass@3 — above GPT-OSS-120B (80.1), Qwen3.5-9B (81.7), and Claude Haiku 4.5 (73)
- AIME 2025: 90 pass@5 (27/30)
- Terminal-Bench 2.0: 28.1 (25/89) — +8.1 points over base model
The key insight: We trained on what frontier agents actually do, real tool calls, real error recovery, real edit diffs. The model learns read-before-write patterns, responds to LSP diagnostic, and applies minimal diffs instead of full rewrites.
Base: Qwen3.5-9B. LoRA SFT, 4x H200, Axolotl, 99.35% packing efficiency.
Weights:
GGUF: huggingface.co/Tesslate/OmniCoder-9B-GGUF
Apache 2.0. Run it locally.