Google · Balanced
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
- 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.
| Gemini 2.0 Pro ReasonerGoogleThis model | Gemma 4 26B A4B CybersecurityGoogleRemove | |
|---|---|---|
| Provider | ||
| Discovery tier | Balanced | Open weights |
| Added | Jan 1, 2024 | Sep 19, 2026 |
| Limits | ||
| Context window | 128K | 262.14KBest |
| Max output | 65.54KBest | 32.77K |
| Average output | — | 600 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $1.29 | $0.11Best |
| Output | $5.00 | $0.33Best |
| Cache read | $0.32 | $0.0528Best |
| Performance | ||
| Request success (24h) | — | 61.4% |
| Benchmark indexes | ||
| Intelligence Index | 16.1 | — |
| Coding Index | 33.3 | — |
| Math Index | 87.7 | — |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | No | Yes |
| Vision | No | Yes |
| Audio input | Yes | No |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Gemini 2.0 Pro Reasoner
- Gemma 4 26B A4B Cybersecurity
Intelligence Index
- Gemini 2.0 Pro Reasoner16.1
- Gemma 4 26B A4B Cybersecurity—
Gemini 2.0 Pro Reasoner: Better than 41% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Gemini 2.0 Pro Reasoner: 33.3
- Gemma 4 26B A4B Cybersecurity: —
Math Index
Composite of the math evaluations
- Gemini 2.0 Pro Reasoner: 87.7
- Gemma 4 26B A4B Cybersecurity: —
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Gemini 2.0 Pro Reasoner: 2.2%
- Gemma 4 26B A4B Cybersecurity: —
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Gemini 2.0 Pro Reasoner: 0.0%
- Gemma 4 26B A4B Cybersecurity: —
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Gemini 2.0 Pro Reasoner: 302 Elo
- Gemma 4 26B A4B Cybersecurity: —
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Gemini 2.0 Pro Reasoner: 455 Elo
- Gemma 4 26B A4B Cybersecurity: —
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Gemini 2.0 Pro Reasoner: 10.2%
- Gemma 4 26B A4B Cybersecurity: —
AA-LCRv1.1
Reasoning across long documents
- Gemini 2.0 Pro Reasoner: 69.0%
- Gemma 4 26B A4B Cybersecurity: —
MLCR-AA
Long-context reasoning over medical records
- Gemini 2.0 Pro Reasoner: 12.2%
- Gemma 4 26B A4B Cybersecurity: —
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Gemini 2.0 Pro Reasoner: 22.5%
- Gemma 4 26B A4B Cybersecurity: —
GPQA Diamond
Graduate-level science questions
- Gemini 2.0 Pro Reasoner: 84.4%
- Gemma 4 26B A4B Cybersecurity: —
MMLU-Pro
Multi-subject knowledge and reasoning
- Gemini 2.0 Pro Reasoner: 86.2%
- Gemma 4 26B A4B Cybersecurity: —
IFBench
Following precise output instructions
- Gemini 2.0 Pro Reasoner: 48.7%
- Gemma 4 26B A4B Cybersecurity: —
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Gemini 2.0 Pro Reasoner: 0.0%
- Gemma 4 26B A4B Cybersecurity: —
SciCode
Scientific computing in Python
- Gemini 2.0 Pro Reasoner: 46.3%
- Gemma 4 26B A4B Cybersecurity: —
LiveCodeBench
Recent competitive programming problems
- Gemini 2.0 Pro Reasoner: 80.1%
- Gemma 4 26B A4B Cybersecurity: —
Math
AIME 2025
Competition mathematics, 2025 problems
- Gemini 2.0 Pro Reasoner: 87.7%
- Gemma 4 26B A4B Cybersecurity: —
AIME
Competition mathematics
- Gemini 2.0 Pro Reasoner: 88.7%
- Gemma 4 26B A4B Cybersecurity: —
MATH-500
Competition math problems
- Gemini 2.0 Pro Reasoner: 96.7%
- Gemma 4 26B A4B Cybersecurity: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Gemini 2.0 Pro Reasoner: 26.5%
- Gemma 4 26B A4B Cybersecurity: —
τ²-Bench Telecom
Tool use in a customer-support simulation
- Gemini 2.0 Pro Reasoner: 54.1%
- Gemma 4 26B A4B Cybersecurity: —
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Gemini 2.0 Pro Reasoner: 69.0%
- Gemma 4 26B A4B Cybersecurity: —
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.
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)Related models
Other Google models, starting with the same family.