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LatestWeiboAI3B
WeiboAIReasoning

Weibo AI Releases VibeThinker-3B, a Compact Reasoning Model

The new 3-billion-parameter model from the Chinese tech giant focuses on challenging benchmarks in mathematics, coding, and graduate-level questions.

Jun 12, 2026
NotableOther
VibeThinker-3B

Chinese technology company Weibo has introduced VibeThinker-3B, a new small language model focused on advanced reasoning. At just three billion parameters, the model is part of a growing class of highly efficient models designed to deliver specialized performance without the massive computational overhead of their larger counterparts.

According to the release card on Hugging Face, VibeThinker-3B was developed to excel at specific, difficult tasks. The creators highlight its performance on benchmarks that test mathematical ability (GSM8K, MATH), code generation (HumanEval), and graduate-level, Google-proof question answering (GPQA), indicating a focus on deep, domain-specific problem-solving rather than general conversation.

A Niche Specialist

The model's deliberate focus on reasoning-intensive domains is what sets it apart. While many small models aim for broad competence, VibeThinker-3B is positioned as a specialist. This strategy allows smaller models to potentially outperform much larger ones on targeted tasks, making them valuable components for applications requiring reliable logic, math, or code intelligence.

Why it matters: The release of specialized models like VibeThinker-3B demonstrates a maturing ecosystem where developers can choose the right tool for the job. Instead of relying on a single, monolithic model, teams can deploy smaller, more cost-effective models fine-tuned for specific needs. Prospective users should note the model is released under a custom license, and its terms should be reviewed before use.

Sources

  • WeiboAI/VibeThinker-3B

    Hugging Face

    Visit
OlderMoonshot AI Releases Kimi, a Multimodal Coding ModelMoonshot AI · Code · 5 months agoNewerMoonshot AI releases Kimi K3, a 2.8T-parameter MoE modelMoonshot AI · Text / LLM · 5 months ago

Get the model

Hugging Face

Specs

Parameters3B
Context window131K tokens
Size6.2 GB
PrecisionBF16
ArchitectureQwen2ForCausalLM
LicenseOTHER
Downloads3.1K
Likes864

Can you run it?

Runs on just about anything.

  • BF16 (as published)

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    7.5 GB
  • 8-bit

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    4.6 GB
  • 4-bit

    Any modern laptop · 8 GB RAM

    3.2 GB
Your machine
GGUF builds
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

ReasoningText / LLMCode

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