𝗡𝗲𝘄 𝗔𝗜 𝗧𝗼𝗼𝗹 𝗔𝗹𝗲𝗿𝘁: 𝗚𝗶𝗴𝗮𝗖𝗵𝗮𝘁 𝟯.𝟱 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 🚀 Want to solve complex coding & math problems faster?

❓ Tired of LLMs that hallucinate on complex tasks? What if your AI could: • Explore multiple step-by-step reasoning paths? • Use automated verification to reinforce correct answers? • Check its own work and self-correct? • Autonomously decide when to call external tools? GigaChat 3.5 Reasoning does all of this. This open-source LLM (built on GigaChat 3.5 Ultra) actually thinks before answering. It uses proprietary linear attention for efficient long-context handling, consuming 37% fewer tokens than DeepSeek V4 Flash Preview on math problems. Real-world performance: ✓ IFBench: 44 → 77 ✓ Natural Plan: 64 → 80 ✓ LiveCodeBench v6: 56 → 85 🔗 MIT License. Weights on Hugging Face: fp8 | bf16

🎯 GigaChat 3.5 Reasoning: 5 Key Features 1️⃣ Advanced Reasoning: Explores multiple step-by-step paths, using automated verification to reinforce correct answers and self-correct 2️⃣ Autonomous Tool Usage: Independently decides when to call external APIs or revise earlier steps 3️⃣ Linear Attention: Proprietary architecture retains key context points without re-matching from scratch 4️⃣ Token Economy: Uses 37% fewer tokens than DeepSeek V4 Flash Preview on math problems 5️⃣ Proven Performance: Open-source LLM (built on GigaChat 3.5 Ultra) with massive benchmark gains: • IFBench: 44 → 77 • Natural Plan: 64 → 80 • LiveCodeBench v6: 56 → 85 🔗 MIT License. Weights on Hugging Face: fp8 | bf16

GigaChat 3.5 Reasoning is a new open-source LLM designed to reason before generating responses. The model breaks problems into stages, builds execution plans, checks intermediate results, and self-corrects when needed. Built on GigaChat 3.5 Ultra, it was trained on math and coding tasks using multiple step-by-step reasoning paths. An automated verification step reinforces the paths that lead to correct answers, enabling the model to plan multi-step actions, decide when to call external tools, and revise earlier steps independently. The model uses a proprietary linear attention architecture, which improves efficiency on long contexts by retaining key processed points rather than re-matching queries against the entire prior text. On math problems, GigaChat 3.5 Reasoning uses on average 37% fewer tokens than DeepSeek V4 Flash Preview. Benchmark gains over the non-reasoning version: • IFBench: 44 → 77 • Natural Plan: 64 → 80 • LiveCodeBench v6: 56 → 85 The model is open-sourced under the MIT license. Weights are available on Hugging Face: fp8 | bf16

𝗡𝗲𝘄 𝗔𝗜 𝗧𝗼𝗼𝗹 𝗔𝗹𝗲𝗿𝘁: 𝗚𝗶𝗴𝗮𝗖𝗵𝗮𝘁 𝟯.𝟱 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 🚀 Want to solve complex coding & math problems faster? This new open-source LLM actually thinks before it answers! 💡 Built on GigaChat 3.5 Ultra: explores multiple step-by-step reasoning paths & uses automated verification 💡 Autonomously plans multi-step actions & decides when to call external tools 💡 Highly efficient: Linear attention retains key points, using 37% fewer tokens than DeepSeek V4 Flash Preview 📈 Massive benchmark gains over non-reasoning versions: • IFBench: 44 → 77 • Natural Plan: 64 → 80 • LiveCodeBench v6: 56 → 85 🎯 Perfect for Software Engineers, Data Scientists, and Students preparing for technical interviews! 🔗 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝘄𝗲𝗶𝗴𝗵𝘁𝘀 𝗵𝗲𝗿𝗲 👇 (MIT License): fp8 | bf16