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Gemini 2.0 Pro 1206

Added Dec 6, 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.

  • Vision
  • Audio input
Input
Text, Images, Audio
Output
Text
Discovery tier
Balanced

Pricing

USD per 1M tokens
Input
$1.26
Output
$5.00
Cache read
$0.63

Reference prices as published by the model provider. API example

Specifications

Context window
2.1M
Max output
8.2K

Side by side

Gemini 2.0 Pro 1206 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 1206GoogleThis modelGemini 4 ArgonGoogleRemove
ProviderGoogleGoogle
Discovery tierBalancedFrontier
AddedDec 6, 2024Oct 13, 2026
Limits
Context window2.1MBest1.05M
Max output8.19K65.54KBest
Average output—1.6K tokens
Pricing, USD per 1M tokens
Input$1.26Best$2.00
Output$5.00Best$10.00
Cache read$0.63$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 Index—52.6
Capabilities
ReasoningNoYes
ToolsNoYes
VisionYesYes
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 1206
  • Gemini 4 Argon

Intelligence Index

  • Gemini 2.0 Pro 1206—
  • Gemini 4 Argon52.6
3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Gemini 2.0 Pro 1206: —
    • Gemini 4 Argon: 77.5%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Gemini 2.0 Pro 1206: —
    • Gemini 4 Argon: 53.1%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Gemini 2.0 Pro 1206: —
    • Gemini 4 Argon: 3.9%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Gemini 2.0 Pro 1206: —
    • Gemini 4 Argon: 1,486 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Gemini 2.0 Pro 1206: —
    • Gemini 4 Argon: 1,624 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Gemini 2.0 Pro 1206: —
    • Gemini 4 Argon: 21.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • Gemini 2.0 Pro 1206: —
    • Gemini 4 Argon: 79.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Gemini 2.0 Pro 1206: —
    • Gemini 4 Argon: 13.9%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Gemini 2.0 Pro 1206: —
    • Gemini 4 Argon: 57.1%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Gemini 2.0 Pro 1206: —
    • Gemini 4 Argon: 57.1%

  • SciCode

    Scientific computing in Python

    • Gemini 2.0 Pro 1206: —
    • Gemini 4 Argon: 61.8%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Gemini 2.0 Pro 1206: —
    • Gemini 4 Argon: 79.7%

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 $0.76 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-exp-1206",
    "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-exp-1206",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.