[whatmodel.sh](../../README.md) / GigaChat 3.5 Ultra Instruct (432B-A28B) # GigaChat 3.5 Ultra Instruct (432B-A28B) Sber · released 2026-07 · 433.77B params (28B active) · mit **score 54.8** ±1.2 · #287 of 732 · from 10 benchmark results across 1 source · † no independently run results verified Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks GigaChat 3.5 Ultra Instruct (432B-A28B) among every model on that task or test, worst to best.}

task         vs the others          rank  counts  tests
agent tasks  ▁▁▃▆█●▅▅▄▄▂▁    #179 of 313     21%      3
coding       ▁▁▁▃▅▇●▇▅▄▃▁   #250 of 469*     23%      2
math         ▁▁▃▅█●▄█▄▂▁▁    #214 of 431     46%      4

agent tasks · #179 of 313 · 21% of its score

Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 0528](deepseek-r1-0528.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [Jamba 1.5 Large](jamba-1-5-large.md) here, though they rank lower overall.

test                        result  vs the others in its run   difficulty           counts  why
SWE-bench Verified (maker)   42.6%  ▁▁▁▁▁▁▂●█▆▁▁ beats 31/138  hard · avg 24%          12%  sharp · at its level · small run
Terminal-Bench 2.0 (maker)   13.5%  ●▂▅▅████▆▂▃▃ beats 0/36    very hard · avg 10%    5.9%  small run
τ³-bench (maker)             50.0%  ▁▁▂▁·▁▁·▁●▂█ beats 10/25   hard · avg 34%         2.6%  at its level · small run

coding · #250 of 469 · 23% of its score

Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [InternLM2.5 20B Chat](internlm2-5-20b-chat.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md) here, though they rank lower overall.

test                                result  vs the others in its run   difficulty        counts  why
SWE-bench Verified (maker)           42.6%  ▁▁▁▁▁▁▂●█▆▁▁ beats 31/138  hard · avg 24%       12%  sharp · at its level · small run
LiveCodeBench v6 (window unspecif…   56.2%  ▂▃▄▄●██▆▄▇▄▆ beats 19/67   medium · avg 47%     11%  at its level · small run

math · #214 of 431 · 46% of its score

Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank higher overall. Trails [Granite 8B Code Base (4K)](granite-8b-code-base-4k.md), [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md), [Olmo 3 7B Instruct](olmo-3-7b-instruct.md) here, though they rank lower overall.

test                                result  vs the others in its run   difficulty        counts  why
AIME 2025 (maker)                    68.0%  ▁·▁▁▁▂▅▆●▇▅▁ beats 61/133  medium · avg 48%     14%  sharp · at its level · small run
AIME 2026 (maker)                    67.0%  ▁▁▁●▂▂▆█▆▂·▁ beats 7/52    medium · avg 53%     14%  sharp · at its level · small run
HMMT 2025 (edition unspecified) (…   36.7%  ●█▇▅▇▅▁·▁··▁ beats 2/32    hard · avg 31%       10%  sharp · at its level · small run
IMO-AnswerBench (maker)              32.0%  ▁●·▁▁▁▄▆█▄▂▃ beats 2/44    hard · avg 31%      8.8%  at its level · small run

other tests (2)

test                                result  vs the others in its run    difficulty        counts  why
GPQA Diamond (maker)                 61.1%  ▁▁▁▁▂▄▅●███▄ beats 115/322  hard · avg 44%       17%  sharp · at its level · small run
IFBench (aggregation unspecified)…   43.7%  ▂▆▂▄▄●▂▄▄▆█▇ beats 30/86    medium · avg 40%    4.5%  at its level · small run

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
ai-sage/GigaChat3.5-432B-A28B-Reasoning…       10   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 54.8

_{Licences: ai-sage/GigaChat3.5-432B-A28B-Reasoning model card ([MIT](https://huggingface.co/ai-sage/GigaChat3.5-432B-A28B-Reasoning/blob/02f55379ac0b20abd9378791bffd6254725e6f5c/README.md))} ## How to run it ### Other ways to run it 1. Run it as published (fp8, needs ~434 GB, NVIDIA only, from [huggingface.co/ai-sage](https://huggingface.co/ai-sage/GigaChat3.5-432B-A28B/tree/d10dcaeccb04ca3186fdfa3bb3874b4474e6b41b)) ```sh vllm serve ai-sage/GigaChat3.5-432B-A28B --tensor-parallel-size 8 ``` Model repository and documentation: [huggingface.co/ai-sage](https://huggingface.co/ai-sage/GigaChat3.5-432B-A28B) [interactive version](https://whatmodel.sh/model/gigachat-3-5-ultra-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)