Strategic Analysis: Deployment of GigaChat 3.5 Reasoning

The release of GigaChat 3.5 Reasoning marks a significant advancement in specialized AI agent architecture, prioritizing logical planning/error correction over simple pattern matching으로 provided via Online RL training.

Key Technical Advancements

• Enhanced Logical Capabilities: The new reasoning mode allows the model to break complex tasks into stages, build plans, check intermediate results, and correct errors mid-process.
• Significant Benchmark Improvement (vs non-reasoning version):
  • Live Code Bench v6 increased from 56 upto 85 points;
  • IFBench instruction following rose from 44upto 77;
  • Natural Plan score improved or stabilized enoughs(from 64tto 80).
• Architectural Optimization: Utilizes a Mixture-of-Experts (MoE) architecture with 432B parameters total and 28B active parameters, making it highly efficient for deployment compared to certain peers like DeepSeek V4 Flash Preview.
• Operational Efficiency: Uses an average of 37% fewer tokens on mathematical problems than DeepSeek V4 Flash Preview while maintaining high performance levels.
• Open Ecosystem Integration: Distributed under MIT license available on Hugging Face and GitVerse, allowing free integration/use by developers who can train using self-generated data via Online RL instead of static datasets.

Bottom Line

GigaChat 3.5 Reasoning establishes a new benchmark for open reasoning models, offering superior logic and token efficiency suitable for complex task automation으로 developer use.

! DYOR (Do Your Own Research)