Gemma 4 31B

Google's Gemma 4 31B instruction-tuned model for heavier reasoning, coding, agentic workflows, and long-context multimodal understanding. This route keeps tokenizer thinking disabled for faster direct answers. Requests containing video cost $0.14 per million input tokens and $0.40 per million output tokens.

  • Reasoning
  • Vision
  • Video Input
  • Structured Output

Added Apr 2, 2026

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.100
Output
$0.33
Cache read
$0.050
Compare provider prices

Specifications

Context window
262.1K
Max output
131.1K
Parameters
31B
Avg output (7d)
330 tokens
Longer than 30% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

14.7

Better than 55% of models compared

Coding Index

43.4

Better than 50% of models compared

Agentic Index

4.2

Better than 36% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

4.9%

Better than 28% of models compared

Harvey LAB-AA

Legal agentic work criterion pass rate

47.2%

Better than 4% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

365 Elo

Better than 19% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

623 Elo

Better than 31% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

6.0%

Better than 30% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

69.7%

Better than 62% of models compared

Reasoning

HLE

Humanity's Last Exam

23.6%

Better than 72% of models compared

IFBench

Instruction-following benchmark

75.6%

Better than 93% of models compared

CritPt

Research-level physics reasoning

1.4%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 16% of models compared

SciCode

Python programming for scientific computing

45.5%

Better than 41% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

20.0%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

85.0%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

85.7%

Better than 79% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

36.4%

Better than 83% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

59.9%

Better than 56% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

69.7%

Better than 62% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

5.7%

Last updated Oct 3, 2026

Artificial Analysis

Providers

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