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What is GigaChat 3 Ultra?
GigaChat 3 Ultra is a breakthrough open-source LLM, offering 702 billion parameters built on an advanced MoE architecture that keeps computation efficient while delivering frontier-level performance. Its design activates only 36 billion parameters per step, combining high intelligence with practical deployment speeds, even for research and enterprise workloads. The model is trained entirely from scratch on a 14-trillion-token dataset spanning ten+ languages, expansive natural corpora, technical literature, competitive programming problems, academic datasets, and more than 5.5 trillion synthetic tokens engineered to enhance reasoning depth. This approach enables the model to achieve exceptional Russian-language capabilities, strong multilingual performance, and competitive global benchmark scores across math (GSM8K, MATH-500), programming (HumanEval+), and domain-specific evaluations. GigaChat 3 Ultra is optimized for compatibility with modern open-source tooling, enabling fine-tuning, inference, and integration using standard frameworks without complex custom builds. Advanced engineering techniques—including MTP, MLA, expert balancing, and large-scale distributed training—ensure stable learning at enormous scale while preserving fast inference. Beyond raw intelligence, the model includes upgraded alignment, improved conversational behavior, and a refined chat template using TypeScript-based function definitions for cleaner, more efficient interactions. It also features a built-in code interpreter, enhanced search subsystem with query reformulation, long-term user memory capabilities, and improved Russian-language stylistic accuracy down to punctuation and orthography. With leading performance on Russian benchmarks and strong showings across international tests, GigaChat 3 Ultra stands among the top five largest and most advanced open-source LLMs in the world. It represents a major engineering milestone for the open community.
What is Chinchilla?
Chinchilla represents a cutting-edge language model that operates within a compute budget similar to Gopher while boasting 70 billion parameters and utilizing four times the amount of training data. This model consistently outperforms Gopher (which has 280 billion parameters), along with other significant models like GPT-3 (175 billion), Jurassic-1 (178 billion), and Megatron-Turing NLG (530 billion) across a diverse range of evaluation tasks. Furthermore, Chinchilla’s innovative design enables it to consume considerably less computational power during both fine-tuning and inference stages, enhancing its practicality in real-world applications. Impressively, Chinchilla achieves an average accuracy of 67.5% on the MMLU benchmark, representing a notable improvement of over 7% compared to Gopher, and highlighting its advanced capabilities in the language modeling domain. As a result, Chinchilla not only stands out for its high performance but also sets a new standard for efficiency and effectiveness among language models. Its exceptional results solidify its position as a frontrunner in the evolving landscape of artificial intelligence.
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API Availability
API Availability
Pricing Information
Free
Open source
Free Version
Pricing Information
Pricing not provided
Supported Platforms
SaaS
Windows
Mac
On-Prem
Linux
Supported Platforms
SaaS
Windows
Mac
On-Prem
Linux
Customer Service / Support
Not specified
Customer Service / Support
Not specified
Training Options
Documentation Hub
Training Options
Documentation Hub
Company Facts
Organization Name
Sberbank
Date Founded
1841
Company Location
Russia
Company Website
giga.chat
Company Facts
Organization Name
Google DeepMind
Company Location
United States
Company Website
arxiv.org/abs/2203.15556
Categories and Features
AI Models
Not specified
Large Language Models
Not specified
Categories and Features
AI Models
Not specified
Large Language Models
Not specified