GigaChat-3.5 Reasoning released: 432B-A28B MoE under MIT, trained via CISPO distillation

GigaChat-3.5 Reasoning is out, and the team behind it posted the release to r/LocalLLaMA itself. The model is a 432B-A28B mixture of experts, meaning 432 billion total parameters with 28 billion active per token, and it uses Gated DeltaNet layers for long-context efficiency, according to the announcement from u/netikas. Weights are published on Hugging Face under an MIT license in the ai-sage/gigachat-35-reasoning collection, and the model is live for testing at giga.chat, where it occupies the reasoning tab, the rightmost one.

The training recipe the post describes runs in two stages. The team trained domain experts for code, math, general work and other areas with CISPO, then distilled those experts into a single model through on-policy distillation. The claim is that specialist quality can be folded into one general-purpose model instead of served as a routing ensemble of separate experts.

In the team's own evaluations, the resulting model lands close to DeepSeek V4 Flash Preview while using 37% fewer tokens in its reasoning traces. Both halves of that comparison come from the same evals the releasing team ran. The post does not name the benchmarks, the scoring method, or the conditions under which the token counts were measured.

Token counts matter because reasoning models spend much of their output budget on intermediate steps, so a 37% reduction at comparable accuracy would lower cost and shorten responses per query. For a 432B model, the 28B active parameter figure holds serving cost below what the total parameter count implies, though running it still requires substantial hardware. The MIT license removes restrictions that would otherwise limit commercial or local use.

The announcement frames GigaChat-3.5 Reasoning as one entry in an existing lineup rather than a standalone release. It does not name the other models in that lineup, describe how this one differs from them, or say whether the same CISPO and distillation recipe was used to build them.

The only account of the release available is the Reddit post and the Hugging Face collection it links. No second outlet, no model card excerpt and no independent evaluation accompany it, and the benchmark comparison rests entirely on numbers the releasing team produced. Whether the collection's license terms match the MIT statement in the post is also unverified here.

Several items remain open to checking: the eval tables behind the DeepSeek comparison, the measurement behind the 37% token reduction, the full training and architecture documentation, and whether third-party leaderboards reproduce the result. Until someone outside the team runs those checks, the figures stand as the releasing team's own claims.