Qwopus3.6 35B A3B Coder MTP GGUF

Qwopus3.6 35B A3B Coder MTP GGUF by Jackrong, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

Comparison

Feature	Qwopus3.6 35B A3B Coder MTP GGUF	Interfaze
Input Modalities	text, image	image, text, audio, video, document
Native OCR	No	Yes
Long Document Processing	No	Yes
Language Support	5 partial	162+
Native Speech-to-Text	No	Yes
Native Object Detection	No	Yes
Guardrail Controls	No	Yes
Context Input Size	262.1K	1M
Tool Calling	Yes	Tool calling supported + built in browser, code execution and web search

Scaling

Feature	Qwopus3.6 35B A3B Coder MTP GGUF	Interfaze
Scaling	Self-hosted/Provider-hosted with quantization	Unlimited

View model card on Hugging Face

[!WARNING] Community Release Notice: Qwopus-3.6-35B-A3B-Coder is an experimental community model intended for research, local coding-agent evaluation, and workflow exploration. It has not undergone complete safety evaluation or broad general-domain benchmarking.

[!IMPORTANT] Evaluation Mode: The central design target and comparison framing in this card is thinking-off execution. The model is evaluated for whether it can remain useful and stable without relying on long visible reasoning traces at every step.

🎯 1. Fine-Tuning Objective: Less Overthinking, More Execution

💡 2. Base Model, Training Stack & Collaboration

📊 3. Thinking-Off Agentic Evaluation

🎮 4. Live Agent Demo: RTS Game Sample

🗺️ 5. Training & Workflow Design

The training and evaluation philosophy for this release centers on agent execution rather than visible chain length. The model should know when to act directly, when to inspect more context, and when to stop and summarize.

[ Qwopus-3.6-35B-A3B-Coder: Agentic Execution Pipeline ]

  Base MoE Foundation
  Qwen3.6-35B-A3B / Qwopus3.6-35B-A3B-v1
          │
          ▼
  Coding + Tool-Use Adaptation
  repository tasks, debugging traces, tool schemas, multi-turn feedback
          │
          ▼
  Thinking-Off Behavior Target
  faster next-step decisions, less overthinking, lower token waste
          │
          ▼
  Agent Harness Workflows
  read files → choose tool → edit code → run tests → inspect errors → iterate → report
          │
          ▼
  Final Objective
  stable long-horizon code execution with practical local latency

[!NOTE] This model card intentionally frames thinking-off behavior as a product target. Long thinking can still be useful for difficult reasoning, but the release focuses on whether the model can complete real coding-agent work without paying that cost on every step.

✅ 6. Recommended Use Cases & Known Limits

[!CAUTION] Deployment note: For agent use, ensure that tool definitions, system prompts, output parsing, and retry behavior are consistent. Thinking-off models can be fast, but the harness still needs clean schemas, useful error feedback, and strict task boundaries.