New open weights models: GigaChat-3.1-Ultra-702B and GigaChat-3.1-Lightning-10B-A1.8B

Hey, folks!

We've released the weights of our GigaChat-3.1-Ultra and Lightning models under MIT license at our HF. These models are pretrained from scratch on our hardware and target both high resource environments (Ultra is a large 702B MoE) and local inference (Lightning is a tiny 10B A1.8B MoE). Why?

1. Because we believe that having more open weights models is better for the ecosystem
   
2. Because we want to create a good, native for CIS language model
   

More about the models:

- Both models are pretrained from scratch using our own data and compute -- thus, it's not a DeepSeek finetune.
- GigaChat-3.1-Ultra is a 702B A36B DeepSeek MoE, which outperforms DeepSeek-V3-0324 and Qwen3-235B. It is trained with native FP8 during DPO stage, supports MTP and can be ran on 3 HGX instances.
- GigaChat-3.1-Lightning is a 10B A1.8B DeepSeek MoE, which outperforms Qwen3-4B-Instruct-2507 and Gemma-3-4B-it on our benchmarks, while being as fast as Qwen3-1.7B due to native FP8 DPO and MTP support and has highly efficient 256k context due to DeepSeekV3 architecture.
- Both models are optimized for English and Russian languages, but are trained on 14 languages, achieving good multilingual results.
- We've optimized our models for tool calling, with GigaChat-3.1-Lightning having a whopping 0.76 on BFCLv3 benchmark.

Metrics:

GigaChat-3.1-Ultra:

GigaChat-3.1-Lightning

Lightning throughput tests:

(measured using vllm 0.17.1rc1.dev158+g600a039f5, concurrency=32, 1xH100 80gb SXM5. Link to benchmarking script.)

Once again, weights and GGUFs are available at our HuggingFace, and you can read a technical report at our Habr (unfortunately, in Russian -- but you can always use translation).