OmniCoder-9B is a 9-billion parameter coding agent model built by Tesslate, fine-tuned on top of Qwen3.5-9B's hybrid architecture (Gated Delta Networks interleaved with standard attention). It was trained on 425,000+ curated agentic coding trajectories spanning real-world software engineering tasks, tool use, terminal operations, and multi-step reasoning.
The training data was specifically built from Claude Opus 4.6 agentic and coding reasoning traces, targeting scaffolding patterns from Claude Code, OpenCode, Codex, and Droid. The dataset includes successful trajectories from models like Claude Opus 4.6, GPT-5.4, GPT-5.3-Codex, and Gemini 3.1 Pro.
The model shows strong agentic behavior: it recovers from errors (read-before-write), responds to LSP diagnostics, and uses proper edit diffs instead of full rewrites. These patterns were learned directly from the real-world agent trajectories it was trained on.
Key Features
Trained on Frontier Agent Traces : Built from Claude Opus 4.6, GPT-5.3-Codex, GPT-5.4, and Gemini 3.1 Pro agentic coding trajectories across Claude Code, OpenCode, Codex, and Droid scaffolding
Hybrid Architecture : Inherits Qwen3.5's Gated Delta Networks interleaved with standard attention for efficient long-context processing
262K Native Context : Full 262,144 token context window, extensible to 1M+
Error Recovery : Learns read-before-write patterns, responds to LSP diagnostics, and applies minimal edit diffs instead of full rewrites
Thinking Mode : Supports <think>...</think> reasoning chains for complex problem decomposition
Apache 2.0 : Fully open weights, no restrictions
Benchmarks
Benchmark
OmniCoder-9B
Qwen3.5-9B
Qwen3-Next-80B
GPT-OSS-120B
GPT-OSS-20B
GLM-4.7-Flash
GLM 4.7
Claude Haiku 4.5
AIME 2025 (pass@5)
90
91.7
91.6
GPQA Diamond (pass@1)
83.8
81.7
77.2
80.1
71.5
73
GPQA Diamond (pass@3)
86.4
Terminal-Bench 2.0
23.6
14.6
33.4
27
GPQA Diamond pass@1: 83.8% (166/198). +2.1 points over the Qwen3.5-9B base model (81.7). At pass@3: 86.4 (171/198).
AIME 2025 pass@5: 90% (27/30).
Terminal-Bench 2.0: 23.6% (21/89). +8.99 points (+61% improvement) over the Qwen3.5-9B base model (14.6%, 13/89).
Quickstart
Transformers
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
23model_id ="Tesslate/OmniCoder-9B"45tokenizer = AutoTokenizer.from_pretrained(model_id)6model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")78messages =[9{"role":"system","content":"You are a helpful coding assistant."},10{"role":"user","content":"Write a Python function to find the longest common subsequence of two strings."},11]1213text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)14inputs = tokenizer([text], return_tensors="pt").to(model.device)1516outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95, top_k=20)17print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
1from openai import OpenAI
23client = OpenAI(base_url="http://localhost:8000/v1", api_key="token")4response = client.chat.completions.create(5 model="Tesslate/OmniCoder-9B",6 messages=[{"role":"user","content":"Explain the difference between a mutex and a semaphore."}],7 temperature=0.6,8)9print(response.choices[0].message.content)
