%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
Gemini 2.5 Flash (Reasoning)
具备增强推理能力的Gemini 2.5 Flash版本，支持可调节思考预算和深度推理模式。能够在快速推理和深度思考之间灵活切换，在保持高推理速度的同时提供强大的逻辑分析能力。成本效益优秀，特别适合需要偶尔进行复杂推理但主要执行快速任务的应用场景。
Intelligence(Relatively Strong)
Speed(Fast)
Input Supported Modalities
Yes
Is Reasoning Model
1,000,000
Context Window
65,536
Maximum Output Tokens
2025-01-31
Knowledge Cutoff
Pricing
￥2.16 /M tokens
Input
￥18 /M tokens
Output
￥7.11 /M tokens
Blended Price
Quick Simple Comparison
Gemini 1.5 Pro (Sep '24)
￥2.5
Gemini 1.5 Pro (May '24)
￥2.5
Gemini 2.0 Flash Thinking Experimental (Dec '24)
Basic Parameters
Gemini 2.5 Flash (Reasoning)Technical 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
344.0,671 tokens/s
Benchmark Scores
Below is the performance of Gemini 2.5 Flash (Reasoning) in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
65.05
Large Language Model Intelligence Level
Coding Index
54.44
Indicator of AI model performance on coding tasks
Math Index
90.23
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
83.2
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
79
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
11.1
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
69.5
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
39.4
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
96.2
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
98.1
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
82.3
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
GPT 5 Mini
openai
￥1.8
Input tokens/million
￥14.4
Output tokens/million
400k
Context Length
GPT 5 Standard
openai
￥63
Input tokens/million
￥504
Output tokens/million
400k
Context Length
GPT 5 Nano
openai
￥0.36
Input tokens/million
￥2.88
Output tokens/million
400k
Context Length
GPT 5
openai
￥9
Input tokens/million
￥72
Output tokens/million
400k
Context Length
GLM 4.5
chatglm
￥0.43
Input tokens/million
￥1.01
Output tokens/million
131k
Context Length
Gemini 2.0 Flash Lite (Preview)
google
￥0.58
Input tokens/million
￥2.16
Output tokens/million
1M
Context Length
Gemini 1.0 Pro
google
￥3.6
Input tokens/million
￥10.8
Output tokens/million
33k
Context Length
Qwen2.5 Coder Instruct 32B
alibaba
￥0.65
Input tokens/million
￥0.65
Output tokens/million
131k
Context Length