Gemini 2.0 Flash (experimental)
Gemini 2.0 Flash (experimental)
The next-generation model features exceptional speed, native tool usage capabilities, multimodal generation capabilities, and a context window of up to one million tokens. It supports audio, image, video, and text input and has the capabilities of structured output, function calling, code execution, search, and multimodal operations.
Intelligence(Medium)
Speed(Fast)
Input Supported Modalities
Yes
Is Reasoning Model
1,000,000
Context Window
8,192
Maximum Output Tokens
2024-08-01
Knowledge Cutoff
Pricing
¥0.72 /M tokens
Input
¥5.04 /M tokens
Output
- /M tokens
Blended Price
Quick Simple Comparison
Gemini 1.5 Pro (May '24)
¥2.5
Gemini 2.0 Pro Experimental (Feb '25)
Gemini 1.5 Pro (Sep '24)
¥2.5
Basic Parameters
GPT-4.1 Technical Parameters
Parameter Count
Not Announced
Context Length
1.0M tokens
Training Data Cutoff
2024-08-01
Open Source Category
Proprietary
Multimodal Support
Text, Image
Throughput
183
Release Date
2024-12-11
Response Speed
228.36,607 tokens/s
Benchmark Scores
Below is the performance of claude-monet in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
4641
Large Language Model Intelligence Level
Coding Index
2749
Indicator of AI model performance on coding tasks
Math Index
-
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
78.2
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
63.6
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
4.7
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
21
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
34
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
90.7
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
91.1
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
30
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
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