Gemini 2.0 Pro Reasoner

Note: This model is now being routed to Gemini 2.5 Pro because Google no longer has the Gemini 2.0 Pro model available and Gemini 2.5 Pro is an across the board improvement. 'DeepGemini', fusion of Gemini 2.5 Pro and Deepseek R1.

Audio Input

Pricing

Auto routing · per 1M tokens

Input: $1.29
Output: $5.00
Cache read: $0.32

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Specifications

Context window: 128K
Max output: 65.5K

Benchmarks

Artificial Analysis

LMArena

Vectara

Sourced from Artificial Analysis.

Intelligence Index

16.1

Better than 58% of models compared

Coding Index

33.3

Better than 40% of models compared

Agentic Index

1.6

Better than 28% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

2.2%

Better than 21% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

298 Elo

Better than 16% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

459 Elo

Better than 24% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

10.2%

Better than 41% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

69.0%

Better than 60% of models compared

Reasoning

HLE

Humanity's Last Exam

22.5%

Better than 71% of models compared

IFBench

Instruction-following benchmark

48.7%

Better than 58% of models compared

CritPt

Research-level physics reasoning

2.6%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 16% of models compared

SciCode

Python programming for scientific computing

46.3%

Better than 43% of models compared

LiveCodeBench

Contamination-free coding benchmark

80.1%

Better than 92% of models compared

Math

AIME 2025

American Invitational Mathematics Examination 2025

87.7%

Better than 85% of models compared

AIME

American Invitational Mathematics Examination

88.7%

Better than 95% of models compared

Math-500

Diverse mathematical problem solving benchmark

96.7%

Better than 86% of models compared

Knowledge

MMLU-Pro

Professional and academic subject knowledge

86.2%

Better than 95% of models compared

AA-Omniscience Accuracy

Proportion of correctly answered questions

39.1%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

90.9%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

84.4%

Better than 76% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

26.5%

Better than 68% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

54.1%

Better than 55% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

69.0%

Better than 60% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks