gemini-2.5-pro-preview

completions

bygemini

gemini-2.5-pro-preview

completions

Published by: googleProvider:

gemini

Gemini 2.5 Pro Experimental is Google's state-of-the-art thinking model, capable of reasoning over complex problems in code, math, and STEM, as well as analyzing large datasets, codebases, and documents using long context.

Released

Jun 5, 2025

Knowledge

Jan 31, 2025

License

Proprietary

Context

Input

$1.25 / 1M tokens

Output

$10 / 1M tokens

Capabilities: tools

Accepts: text, image, audio, video

Returns: text

Released Jun 5, 2025Knowledge Cutoff: Jan 31, 2025License: Proprietary

Context: 1M Input: $1.25 / 1M tokensOutput: $10 / 1M tokensCapabilities: toolsAccepts: text, image, audio, videoReturns: text

Access gemini-2.5-pro-preview through LangDB AI Gateway

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Benchmark Results for gemini-2.5-pro-preview

Overall Performance: 71.35 average score across all categories

Detailed Benchmark Scores:

Benchmark	Score	Percentile	Domain
HLE	21.10	Top 4%	General Knowledge
AIME	88.70	Top 9%	Mathematics
GPQA	83.70	Top 4%	STEM (Physics, Chemistry, Biology)
MMMU	79.60	Top 28%	General Knowledge
SciCode	42.80	Top 5%	Scientific
MATH-500	96.70	Top 15%	Mathematics
MMLU-Pro	86.20	Top 5%	General Knowledge
LiveCodeBench	80.10	Top 8%	Programming
AA Math Index	87.70	Top 15%	Mathematics
AA Coding Index	49.30	Top 10%	Programming
AAII	59.60	Top 8%	General

GPQA Score: 83.70 - Graduate-level reasoning benchmark

Model Comparison:

Provider: gemini

Model Type: completions

Context Size: 1000000 tokens

Comparing against 320 models in the database

Category Scores

vision#9

Benchmark Tests

View Other Benchmarks

HLE

21.1

General Knowledge

AIME

88.7

Mathematics

GPQA

83.7

STEM (Physics, Chemistry, Biology)

MMMU

79.6

General Knowledge

SciCode

42.8

Scientific

MATH-500

96.7

Mathematics

MMLU-Pro

86.2

General Knowledge

LiveCodeBench

80.1

Programming

AA Math Index

87.7

Mathematics

AA Coding Index

49.3

Programming

AAII

59.6

General

Metric	HLE	AIME	GPQA	MMMU	SciCode	MATH-500	MMLU-Pro	LiveCodeBench	AA Math Index	AA Coding Index	AAII
Score	21.1	88.7	83.7	79.6	42.8	96.7	86.2	80.1	87.7	49.3	59.6

Compare with Similar Models

Code Examples

Integration samples and API usage

Code Samples for gemini-2.5-pro-preview

Python SDK Example:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.us-east-1.langdb.ai//projects/<your_project_id>",
    api_key="<your_api_key>"
)

response = client.chat.completions.create(
    model="gemini-2.5-pro-preview",
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ]
)

print(response.choices[0].message.content)

TypeScript SDK Example:

import OpenAI from 'openai';

const client = new OpenAI({
    baseURL: "https://api.us-east-1.langdb.ai//projects/<your_project_id>",
    apiKey: "<your_api_key>"
});

const response = await client.chat.completions.create({
    model: "gemini-2.5-pro-preview",
    messages: [
        { role: "user", content: "Hello, how are you?" }
    ]
});

console.log(response.choices[0].message.content);

cURL Example:

curl -X POST "https://api.us-east-1.langdb.ai//projects/<your_project_id>/v1/chat/completions" \
  -H "Authorization: Bearer <your_api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-2.5-pro-preview",
    "messages": [
        {"role": "user", "content": "Hello, how are you?"}
    ]
  }'

Model: gemini-2.5-pro-preview

Provider: gemini

API Endpoint: $https://api.us-east-1.langdb.ai/

Create API Key

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gemini-2.5-pro-preview by gemini - AI Model Details, Pricing, and Performance Metrics

gemini-2.5-pro-preview

gemini-2.5-pro-preview

Access gemini-2.5-pro-preview through LangDB AI Gateway

Category Scores

Benchmark Tests

Compare with Similar Models

Code Examples

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