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Gemini 2.5 Flash
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Gemini 2.5 Flash

Google's best price-performance model, first Flash model featuring thinking capabilities. Supports 1M token context (expanding to 2M soon), provides adjustable reasoning dial (0-24,576 tokens). Features multimodal input support and native audio functionality, achieves 250+ tokens/second inference speed. Extremely cost-effective, suitable for applications of all scales.
Intelligence(Medium)
Speed(Fast)
Input Supported Modalities
Yes
Is Reasoning Model
1,000,000
Context Window
65,536
Maximum Output Tokens
2025-01-31
Knowledge Cutoff
Go Compare

Pricing

￥2.16 /M tokens
Input
￥18 /M tokens
Output
￥1.89 /M tokens
Blended Price

Quick Simple Comparison

Input

Output

Gemini 2.0 Flash Thinking Experimental (Dec '24)
Gemini 1.5 Pro (Sep '24)
￥2.5
Gemini 2.0 Pro Experimental (Feb '25)

Basic Parameters

Gemini 2.5 FlashTechnical Parameters
Parameter Count
Not Announced
Context Length
1.0M tokens
Training Data Cutoff
2025-01-31
Open Source Category
Proprietary
Multimodal Support
Text, Image
Throughput
85
Release Date
2025-05-20
Response Speed
279.10,846 tokens/s

Benchmark Scores

Below is the performance of Gemini 2.5 Flash in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
53.43
Large Language Model Intelligence Level
Coding Index
39.29
Indicator of AI model performance on coding tasks
Math Index
71.6
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
80.9
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
68.3
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
5.1
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
49.5
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
29.1
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
95.1
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
93.2
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
50
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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