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Gemini 2.0 Pro Reasoner

Added Jan 1, 2024

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Use in the app

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
Input
Text, Audio
Output
Text
Discovery tier
Balanced

Pricing

USD per 1M tokens
Input
$1.29
Output
$5.00
Cache read
$0.32

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
65.5K

Side by side

Gemini 2.0 Pro Reasoner compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Gemini 2.0 Pro ReasonerGoogleThis modelGemini 4 ArgonGoogleRemove
ProviderGoogleGoogle
Discovery tierBalancedFrontier
AddedJan 1, 2024Oct 13, 2026
Limits
Context window128K1.05MBest
Max output65.54K65.54K
Average output—1.6K tokens
Pricing, USD per 1M tokens
Input$1.29Best$2.00
Output$5.00Best$10.00
Cache read$0.32$0.10Best
Cache write—$0.19
Performance
Throughput—101.4 tok/s
Time to first token—13.0 s
Request success (24h)—98.6%
Benchmark indexes
Intelligence Index16.152.6Best
Coding Index33.3—
Math Index87.7—
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
PDF inputNoYes
Audio inputYesYes
Video inputNoYes
Reasoning effort—low, medium, high

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Gemini 2.0 Pro Reasoner
  • Gemini 4 Argon

Intelligence Index

  • Gemini 2.0 Pro Reasoner16.1
  • Gemini 4 Argon52.6

Gemini 2.0 Pro Reasoner: Better than 41% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Gemini 2.0 Pro Reasoner: 33.3
    • Gemini 4 Argon: —

  • Math Index

    Composite of the math evaluations

    • Gemini 2.0 Pro Reasoner: 87.7
    • Gemini 4 Argon: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 2.0 Pro Reasoner: 2.2%
    • Gemini 4 Argon: 77.5%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 2.0 Pro Reasoner: 0.0%
    • Gemini 4 Argon: 53.1%Top

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Gemini 2.0 Pro Reasoner: —
    • Gemini 4 Argon: 3.9%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 2.0 Pro Reasoner: 302 Elo
    • Gemini 4 Argon: 1,486 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 2.0 Pro Reasoner: 455 Elo
    • Gemini 4 Argon: 1,624 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 2.0 Pro Reasoner: 10.2%
    • Gemini 4 Argon: 21.8%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 2.0 Pro Reasoner: 69.0%
    • Gemini 4 Argon: 79.7%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Gemini 2.0 Pro Reasoner: 12.2%
    • Gemini 4 Argon: 13.9%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 2.0 Pro Reasoner: 22.5%
    • Gemini 4 Argon: 57.1%Top

  • GPQA Diamond

    Graduate-level science questions

    • Gemini 2.0 Pro Reasoner: 84.4%
    • Gemini 4 Argon: —

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Gemini 2.0 Pro Reasoner: 86.2%
    • Gemini 4 Argon: —

  • IFBench

    Following precise output instructions

    • Gemini 2.0 Pro Reasoner: 48.7%
    • Gemini 4 Argon: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 2.0 Pro Reasoner: 0.0%
    • Gemini 4 Argon: 57.1%Top

  • SciCode

    Scientific computing in Python

    • Gemini 2.0 Pro Reasoner: 46.3%
    • Gemini 4 Argon: 61.8%Top

  • LiveCodeBench

    Recent competitive programming problems

    • Gemini 2.0 Pro Reasoner: 80.1%
    • Gemini 4 Argon: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Gemini 2.0 Pro Reasoner: 87.7%
    • Gemini 4 Argon: —

  • AIME

    Competition mathematics

    • Gemini 2.0 Pro Reasoner: 88.7%
    • Gemini 4 Argon: —

  • MATH-500

    Competition math problems

    • Gemini 2.0 Pro Reasoner: 96.7%
    • Gemini 4 Argon: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Gemini 2.0 Pro Reasoner: 26.5%
    • Gemini 4 Argon: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Gemini 2.0 Pro Reasoner: 54.1%
    • Gemini 4 Argon: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 2.0 Pro Reasoner: 69.0%
    • Gemini 4 Argon: 79.7%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Prompt cache hits
79.6%
About $0.52 per 1M input tokens with caching

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-2.0-pro-reasoner",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="gemini-2.0-pro-reasoner",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.