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Get all the details on Gemini 2.0 Flash, an AI model from Google. This page covers its token limits, pricing structure, key capabilities such as function_calling, long_context, multimodal_input, available API code samples, and performance strengths.
Key Metrics
Input Limit
1M tokens
Output Limit
No data tokens
Input Cost
$0.10/1M
Output Cost
$0.40/1M
Sample API Code
from google import genai
client = genai.Client(api_key="YOUR_API_KEY")
response = client.models.generate_content(
model="gemini-2.0-flash", contents="Explain how AI works in a few words"
)
print(response.text)Required Libraries
google-genai
@google/generative-ai
Benchmarks
| Benchmark | Score | Source | Notes |
|---|---|---|---|
1355 | lmarena.ai | Rank 8 (as gemini-2.0-flash-001) | |
1039 | lmarena.ai | Rank 17 (as Gemini-2.0-Flash-001) | |
1224 | lmarena.ai | Rank 8 (as gemini-2.0-flash-001) | |
1028 | lmarena.ai | Rank 7 (as gemini-2.0-flash-grounding) | |
60.05 | livebench.ai | - | |
44.25 | livebench.ai | - | |
64.74 | livebench.ai | - | |
63.19 | livebench.ai | - | |
59.92 | livebench.ai | - | |
42.39 | livebench.ai | - | |
85.79 | livebench.ai | - | |
53.6 | Vellum | - | |
62.1 | Vellum | - | |
51.8 | Vellum | - | |
89.7 | Vellum | - | |
60.42 | Vellum | - | |
22.2 | Vellum | - |
Notes
Our most balanced multimodal model with great performance across all tasks, with a 1 million token context window, and built for the era of Agents. Pricing varies by input type (text/image/video vs audio) and tier (Free vs Paid). Vertex AI pricing may differ.
Capabilities
function calling
long context
multimodal input
vision
Supported Data Types
Input Types
text
image
video
audio
Output Types
text
Strengths & Weaknesses
Exceptional at
instruction following
Good at
multimodal understanding
general reasoning
coding
mathematics
data analysis
language tasks
Additional Information
Latest Update
May 13, 2025
Knowledge Cutoff
No data