LANGUAGE MODEL Google/DeepMind Last updated:

Gemini 3.1 Pro Preview

Gemini 3.1 Pro Preview is an API model from Google. It’s positioned for general text tasks—work that benefits from iteration, not just one-shot answers.

Try Gemini API Docs ↗
Intelligence
Top 5%
Speed
Medium
124 tok/s output
Cost
High
$2.00 in / $12.00 out
Context
1M
Up to 1,000,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on Artificial Analysis. Cost buckets are fixed dollar thresholds keyed off output-token price ($/M out).
Intelligence
  • Top 1%≤ 1%
  • Top 5%≤ 5%
  • Top 10%≤ 10%
  • Good≤ 25%
  • Medium≤ 50%
  • Below avg> 50%
Speed
  • Top 1%≥ 345 tok/s
  • Top 5%≥ 237 tok/s
  • Top 10%≥ 196 tok/s
  • Good≥ 146 tok/s
  • Medium≥ 90 tok/s
  • Slow< 90 tok/s
Cost
  • Freeopen weights · self-host
  • Low< $1 / M out
  • Moderate$1–5 / M out
  • High≥ $5 / M out
Official ↗ Artificial Analysis ↗

Core Capabilities

Long Documents
Handles entire codebases, books, and multi-doc RAG.

Context Window

1M tokens
≈ entire codebase
4k Chat 聊天
32k Long docs 长文档
128k Books 整本书
400k Multi-doc 多文档
1M This model 本模型
10M

Availability

API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales

Pricing Model

Pay per token
Input and output billed separately.
Pay-per-token

Capability / Performance

Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).

Lower 20% Upper 80% This model
Reasoning
AA Intelligence Index · scaled to 10
1.7
5.6
8.2
Coding
SciCode · scaled to 10
1.8
4.3
5.9
Agentic tasks
Terminal-Bench Hard · scaled to 10
0.2
3.6
5.4
Context / memory
Context window size · log-scaled
6.0
9.0
9.0
Cost efficiency
Input price ($/M tokens) · cheaper scores higher
6.2
10.0
6.2
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.

What it feels like

  • Tops LMArena at 1501 Elo at release — first model past 1500 on the human-preference leaderboard
  • 37.5% on Humanity's Last Exam without tools, 91.9% on GPQA Diamond — PhD-level reasoning headline
  • MathArena Apex 23.4% — new state-of-the-art on contest-grade math
Reviews: Google — Introducing Gemini 3 ↗ · Google DeepMind — Gemini 3.1 Pro model page ↗ · Vellum — Gemini 3 benchmarks explained ↗

Best use cases

  • Multimodal reasoning over images, video, and long documents
  • Hardest math and science research tasks (esp. with Deep Think enabled)
  • Whole-codebase or long-context analysis at frontier quality

Tools to try

Gemini app AI Studio Vertex AI Gemini CLI

Not ideal for

  • Self-hosted / open-weights deployments
  • Workflows where the early-launch instability is unacceptable (some users report regressions)