GigaChat3.5-Reasoning

Model architecture

GigaChat 3.5 Reasoning is a 432B Mixture-of-Experts model with 28B active parameters. It uses a custom hybrid architecture that combines Multi-head Latent Attention (MLA) with GatedDeltaNet linear-attention layers.

The model also uses GatedNorm, a learned multiplicative gate applied after RMSNorm, and has three MTP heads for speculative decoding. The maximum supported context length is 262K tokens.

Online RL

Post-training starts from an SFT checkpoint. We train six domain experts independently with online RL and then combine them into one release model with on-policy distillation (OPD).

The experts are trained with CISPO. Before training, the current checkpoint is evaluated on the task pool and tasks solved in more than 75% of attempts are removed. As the model improves, the training set shifts toward harder tasks.

Rewards are domain-specific but follow the same general construction: gated checks for hard constraints, additive rewards for answer quality, and an adaptive length penalty.

After RL, the six experts are combined with on-policy distillation. The student generates its own trajectory, while the expert for the corresponding domain provides token-level supervision on that trajectory.

Benchmark scores

* IMOAnswerBench uses Qwen-3-235B-Instruct-2507 as the judge. ** TAU3-bench is averaged across Airline, Retail, Telecom, and Banking. *** Natural Plan uses a corrected scorer that normalizes UTF-8 characters to ASCII. **** SWE-bench Verified and Terminal-Bench 2 use mini-swe-agent with a three-hour timeout. ***** Arena evaluations