The 3B Model Going Toe to Toe with Opus 4.5 In Maths and Coding

2026-06-25 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, quick

Summary

VibeThinker-3B, a 3-billion-parameter dense model developed by a corporate research group at Sina Weibo, has demonstrated competitive performance against much larger frontier models like Claude Opus 4.5 on challenging math benchmarks. The model achieved scores of 96.7 on AIME25, 97.1 on AIME26, 95.4 on HMMT25, and 80.6 on IMO-AnswerBench, utilizing a CLR test-time boost. Its 97.1 score on a key math benchmark surpasses Claude Opus 4.5's 95.1, placing it in the same performance band as GLM-5, Kimi K2.5, Qwen3.6 Plus, and Gemini 3 Pro. This finding challenges the prevailing notion that reasoning capabilities are exclusively tied to increasingly larger model sizes and computational resources.

Key takeaway

For Machine Learning Engineers evaluating model deployment strategies, VibeThinker-3B's performance indicates that smaller, more energy-efficient models can achieve competitive reasoning capabilities through sophisticated post-training. You should explore advanced post-training methods and test-time boosts for your 3B-parameter models, as this approach offers a viable path to high performance without incurring the substantial inference costs associated with models like Claude Opus 4.5's \$15 per million output tokens.

Key insights

Smaller models can achieve frontier-level reasoning performance through advanced post-training, challenging the "bigger is better" paradigm.

Principles

• Reasoning capabilities are not solely dependent on model scale.
• Post-training optimization can significantly boost model performance.
• Competition math benchmarks are a strong indicator of reasoning.

Method

The VibeThinker-3B model leverages a "post-training story" rather than just parameter scaling, employing a CLR test-time boost to achieve its high scores on complex math benchmarks.

In practice

• Investigate post-training techniques for smaller models.
• Benchmark 3B-parameter models against frontier models.
• Explore CLR test-time boosting for math tasks.

Topics

• VibeThinker-3B
• Model Scaling
• Math Benchmarks
• Post-training
• Claude Opus 4.5
• Sina Weibo

Best for: AI Engineer, Research Scientist, AI Architect, AI Scientist, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.