---
title: "Gemini 3.1 Pro"
source: "https://systems-analysis.info/wiki/Gemini_3.1_Pro"
wiki: "systems-analysis.info/wiki"
article: "Gemini_3.1_Pro"
language: "ru"
categories:
  - "Категория:Gemini"
  - "Категория:Google"
  - "Категория:Russian"
  - "Категория:Большие языковые модели"
  - "Категория:Семейства LLM"
revision_id: 556
wiki_created_at: 2026-11-10T18:14:20Z
wiki_modified_at: 2026-11-11T00:53:59Z
downloaded_at: 2026-11-11T01:08:43Z
---

# Gemini 3.1 Pro

**Gemini 3.1 Pro** — проприетарная нативно-мультимодальная модель рассуждений Google DeepMind из семейства Gemini 3, основанная на [Gemini 3 Pro](https://systems-analysis.info/wiki/Gemini_3_Pro "Gemini 3 Pro") с разреженной архитектурой Transformer типа «смесь экспертов» (MoE) и улучшениями в программировании и агентном использовании инструментов. Модель была впервые представлена 19 февраля 2026 года,

## Оценка

Gemini 3.1 Pro оценивалась по широкому набору тестов (бенчмарков), охватывающих способности к рассуждению, мультимодальные возможности, использование инструментов в агентных сценариях, многоязычную обработку данных и работу с длинным контекстом. Дополнительные тесты, сведения о подходе к оценке, результатах и применяемых методологиях опубликованы в <a href="https://deepmind.google/models/evals-methodology/gemini-3-1-pro" class="external text" rel="nofollow">методологии оценки Gemini 3.1 Pro</a>.

Согласно Model Card Google DeepMind, Gemini 3.1 Pro значительно превосходит Gemini 3 Pro в ряде бенчмарков, требующих развитых способностей к рассуждению и мультимодальной обработке данных. Приведённые ниже результаты актуальны по состоянию на февраль 2026 года и получены в режиме размышления (Thinking).<sup>[\[1\]](https://systems-analysis.info/wiki/Gemini_3.1_Pro#cite_note-1)</sup>

<table class="wikitable" style="text-align:center;">
<colgroup>
<col style="width: 11%" />
<col style="width: 11%" />
<col style="width: 11%" />
<col style="width: 11%" />
<col style="width: 11%" />
<col style="width: 11%" />
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<col style="width: 11%" />
<col style="width: 11%" />
</colgroup>
<thead>
<tr class="header">
<th style="text-align: left;">Benchmark</th>
<th>Description</th>
<th></th>
<th>Gemini 3.1 Pro<br />
Thinking (High)</th>
<th><a href="https://systems-analysis.info/wiki/Gemini_3_Pro" title="Gemini 3 Pro">Gemini 3 Pro</a><br />
Thinking (High)</th>
<th>Sonnet 4.6<br />
Thinking (Max)</th>
<th><a href="https://systems-analysis.info/wiki/Claude_Opus_4.6" title="Claude Opus 4.6">Opus 4.6</a><br />
Thinking (Max)</th>
<th><a href="https://systems-analysis.info/wiki/GPT-5.2" title="GPT-5.2">GPT-5.2</a><br />
Thinking (High)</th>
<th>GPT-5.3-Codex<br />
Thinking (High)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: left;"><a href="https://systems-analysis.info/wiki/Humanity%27s_Last_Exam" title="Humanity&#39;s Last Exam">Humanity’s Last Exam</a></td>
<td>Academic reasoning (full set,<br />
text + MM)</td>
<td>No tools<br />
Search (blocklist) +<br />
code execution</td>
<td style="background: #e6f3ff"><strong>44.4%</strong><br />
51.4%</td>
<td>37.5%<br />
45.8%</td>
<td>33.2%<br />
49.0%</td>
<td>40.0%<br />
<strong>53.1%</strong></td>
<td>34.5%<br />
45.5%</td>
<td>—<br />
—</td>
</tr>
<tr class="even">
<td style="text-align: left;">ARC-AGI-2</td>
<td>Abstract reasoning puzzles</td>
<td>ARC Prize Verified</td>
<td style="background: #e6f3ff"><strong>77.1%</strong></td>
<td>31.1%</td>
<td>58.3%</td>
<td>68.8%</td>
<td>52.9%</td>
<td>—</td>
</tr>
<tr class="odd">
<td style="text-align: left;">GPQA Diamond</td>
<td>Scientific knowledge</td>
<td>No tools</td>
<td style="background: #e6f3ff"><strong>94.3%</strong></td>
<td>91.9%</td>
<td>89.9%</td>
<td>91.3%</td>
<td>92.4%</td>
<td>—</td>
</tr>
<tr class="even">
<td style="text-align: left;">Terminal-Bench 2.0</td>
<td>Agentic terminal coding</td>
<td>Terminus-2 harness<br />
Other best self-reported<br />
harness</td>
<td style="background: #e6f3ff"><strong>68.5%</strong><br />
—</td>
<td>56.9%<br />
—</td>
<td>59.1%<br />
—</td>
<td>65.4%<br />
—</td>
<td>54.0%<br />
62.2% (Codex)</td>
<td>64.7%<br />
<strong>77.3%</strong> (Codex)</td>
</tr>
<tr class="odd">
<td style="text-align: left;">SWE-Bench Verified</td>
<td>Agentic coding</td>
<td>Single attempt</td>
<td style="background: #e6f3ff">80.6%</td>
<td>76.2%</td>
<td>79.6%</td>
<td><strong>80.8%</strong></td>
<td>80.0%</td>
<td>—</td>
</tr>
<tr class="even">
<td style="text-align: left;">SWE-Bench Pro (Public)</td>
<td>Diverse agentic coding tasks</td>
<td>Single attempt</td>
<td style="background: #e6f3ff">54.2%</td>
<td>43.3%</td>
<td>—</td>
<td>—</td>
<td>55.6%</td>
<td><strong>56.8%</strong></td>
</tr>
<tr class="odd">
<td style="text-align: left;">LiveCodeBench Pro</td>
<td>Competitive coding problems<br />
from Codeforces, ICPC, and IOI</td>
<td>Elo Rating</td>
<td style="background: #e6f3ff"><strong>2,887</strong></td>
<td>2,439</td>
<td>—</td>
<td>—</td>
<td>2,393</td>
<td>—</td>
</tr>
<tr class="even">
<td style="text-align: left;">SciCode</td>
<td>Scientific research coding</td>
<td></td>
<td style="background: #e6f3ff"><strong>59%</strong></td>
<td>56%</td>
<td>47%</td>
<td>52%</td>
<td>52%</td>
<td>—</td>
</tr>
<tr class="odd">
<td style="text-align: left;">APEX-Agents</td>
<td>Long-horizon professional tasks</td>
<td></td>
<td style="background: #e6f3ff"><strong>33.5%</strong></td>
<td>18.4%</td>
<td>—</td>
<td>29.8%</td>
<td>23.0%</td>
<td>—</td>
</tr>
<tr class="even">
<td style="text-align: left;">GDPval-AA Elo</td>
<td>Expert tasks</td>
<td>Elo Rating</td>
<td style="background: #e6f3ff">1,317</td>
<td>1,195</td>
<td><strong>1,633</strong></td>
<td>1,606</td>
<td>1,462</td>
<td>—</td>
</tr>
<tr class="odd">
<td style="text-align: left;">τ2-bench</td>
<td>Agentic tool use</td>
<td>Retail<br />
Telecom</td>
<td style="background: #e6f3ff">90.8%<br />
<strong>99.3%</strong></td>
<td>85.3%<br />
98.0%</td>
<td>91.7%<br />
97.9%</td>
<td><strong>91.9%</strong><br />
<strong>99.3%</strong></td>
<td>82.0%<br />
98.7%</td>
<td>—<br />
—</td>
</tr>
<tr class="even">
<td style="text-align: left;">MCP Atlas</td>
<td>Multi-step workflows<br />
using MCP</td>
<td></td>
<td style="background: #e6f3ff"><strong>69.2%</strong></td>
<td>54.1%</td>
<td>61.3%</td>
<td>59.5%</td>
<td>60.6%</td>
<td>—</td>
</tr>
<tr class="odd">
<td style="text-align: left;">BrowseComp</td>
<td>Agentic search</td>
<td>Search + Python +<br />
Browse</td>
<td style="background: #e6f3ff"><strong>85.9%</strong></td>
<td>59.2%</td>
<td>74.7%</td>
<td>84.0%</td>
<td>65.8%</td>
<td>—</td>
</tr>
<tr class="even">
<td style="text-align: left;">MMMU Pro</td>
<td>Multimodal understanding and<br />
reasoning</td>
<td>No tools</td>
<td style="background: #e6f3ff">80.5%</td>
<td><strong>81.0%</strong></td>
<td>74.5%</td>
<td>73.9%</td>
<td>79.5%</td>
<td>—</td>
</tr>
<tr class="odd">
<td style="text-align: left;">MMMLU</td>
<td>Multilingual Q&amp;A</td>
<td></td>
<td style="background: #e6f3ff"><strong>92.6%</strong></td>
<td>91.8%</td>
<td>89.3%</td>
<td>91.1%</td>
<td>89.6%</td>
<td>—</td>
</tr>
<tr class="even">
<td style="text-align: left;">MRCR v2 (B-needle)</td>
<td>Long context performance</td>
<td>128k (average)<br />
1M (pointwise)</td>
<td style="background: #e6f3ff"><strong>84.9%</strong><br />
26.3%</td>
<td>77.0%<br />
26.3%</td>
<td><strong>84.9%</strong><br />
not supported</td>
<td>84.0%<br />
not supported</td>
<td>83.8%<br />
not supported</td>
<td>—<br />
—</td>
</tr>
</tbody>
</table>

1.  <span id="cite_note-1">[↑](https://systems-analysis.info/wiki/Gemini_3.1_Pro#cite_ref-1) Google DeepMind. *Gemini 3.1 Pro — Model Card*. Выпуск: февраль 2026 года. С. 4.</span>

## См. также

- [Gemini 3 Pro](https://systems-analysis.info/wiki/Gemini_3_Pro "Gemini 3 Pro")
- [Gemini 3 Flash](https://systems-analysis.info/wiki/Gemini_3_Flash "Gemini 3 Flash")
