Model Comparison

Comprehensive side-by-side analysis of model capabilities and performance

Google

Gemini 2.0 Flash

Google

Gemini 2.0 Flash is a multimodal language model developed by Google. It achieves strong performance with an average score of 66.7% across 13 benchmarks. It excels particularly in Natural2Code (92.9%), MATH (89.7%), FACTS Grounding (83.6%). With a 1.1M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2024, it represents Google's latest advancement in AI technology.

Meta

Llama 3.2 11B Instruct

Meta

Llama 3.2 11B Instruct is a multimodal language model developed by Meta. It achieves strong performance with an average score of 63.6% across 11 benchmarks. It excels particularly in AI2D (91.1%), DocVQA (88.4%), ChartQA (83.4%). It supports a 256K token context window for handling large documents. The model is available through 6 API providers. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2024, it represents Meta's latest advancement in AI technology.

Meta

Llama 3.2 11B Instruct

Meta

2024-09-25

Google

Gemini 2.0 Flash

Google

2024-12-01

2 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 2.0 Flash

Input:$0.10
Output:$0.40
Meta

Llama 3.2 11B Instruct

$0.40 cheaper
Input:$0.05
Output:$0.05

Performance Metrics

Context window and performance specifications

Google

Gemini 2.0 Flash

Larger context
Max Context:1.1M
Meta

Llama 3.2 11B Instruct

Max Context:256.0K
Parameters:10.6B

Average performance across 3 common benchmarks

Google

Gemini 2.0 Flash

+29.0%
Average Score:74.2%
Meta

Llama 3.2 11B Instruct

Average Score:45.1%

Performance comparison across key benchmark categories

Google

Gemini 2.0 Flash

math
+18.9%
76.4%
vision
+8.9%
70.7%
general
62.6%
Meta

Llama 3.2 11B Instruct

math
57.4%
vision
61.8%
general
+7.5%
70.1%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Llama 3.2 11B Instruct

2023-12-31

Gemini 2.0 Flash

2024-08-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemini 2.0 Flash

1 providers

Google

Throughput: 183 tok/s
Latency: 0.4ms
Meta

Llama 3.2 11B Instruct

6 providers

Sambanova

Throughput: 100 tok/s
Latency: 0.5ms

Together

Throughput: 168 tok/s
Latency: 0.5ms

DeepInfra

Throughput: 108 tok/s
Latency: 0.5ms

Fireworks

Throughput: 125 tok/s
Latency: 0.2ms

Groq

Throughput: 100 tok/s
Latency: 0.5ms

Bedrock

Throughput: 100 tok/s
Latency: 0.5ms
Google

Gemini 2.0 Flash

+29.0%
Avg Score:74.2%
Providers:1
Meta

Llama 3.2 11B Instruct

Avg Score:45.1%
Providers:6