Overview
VibeThinker-3B is a 3.1B-parameter reasoning model from WeiboAI, the AI team behind Weibo.
- License: Mit
- Context length: 128K tokens
- Languages: en
- Minimum hardware: ~2 GB VRAM
- Strengths: reasoning, coding and on-device inference
Highlights
- Verifiable reasoning — tuned for problems whose answers can be checked.
- Competition math — strong AIME and HMMT results in WeiboAI's reported benchmarks.
- Code generation & debugging — built on a Qwen2.5-Coder base.
- 128K context — compact 3.1B model that runs in about 2 GB at 4-bit.
What it is good at
VibeThinker-3B was tuned for verifiable reasoning: problems where an answer can be checked. Its capabilities cover thinking, reasoning, code, and tool calling.
- Competition math — the model thinks through multi-step problems and shows its work, the area where it posted strong AIME and HMMT scores in WeiboAI's reported benchmarks.
- Code generation and debugging — built on a Qwen2.5-Coder base, it writes functions, traces logic errors, and explains what a snippet does.
- Tool calling in agents — it supports structured tool calls, so it can drive local agent loops that hit a calculator, a search function, or your own scripts.
Running it locally
At 3.1B parameters the model is light. A 4-bit GGUF quantization runs in roughly 2 GB of memory, full bf16 weights need about 6 GB, and a 16 GB laptop or an Apple Silicon Mac handles it comfortably. The 131,072-token context is available, though a long context grows the KV cache and wants more headroom.
huggingface-cli download WeiboAI/VibeThinker-3B
You can serve the raw weights through Transformers or vLLM, or skip the setup entirely and load VibeThinker-3B in Atomic Chat with one click, which pulls a quantized build and runs it offline.
License
VibeThinker-3B is released under the MIT license. That permits commercial use, modification, fine-tuning, and redistribution, with the only requirement being that you keep the original copyright and license notice.