Model Comparison

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

Google

Gemini 2.0 Flash-Lite

Google

Gemini 2.0 Flash-Lite is a multimodal language model developed by Google. The model shows competitive results across 13 benchmarks. It excels particularly in MATH (86.8%), FACTS Grounding (83.6%), Global-MMLU-Lite (78.2%). 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 2025, it represents Google's latest advancement in AI technology.

Meta

Llama 3.2 3B Instruct

Meta

Llama 3.2 3B Instruct is a language model developed by Meta. The model shows competitive results across 15 benchmarks. It excels particularly in NIH/Multi-needle (84.7%), ARC-C (78.6%), GSM8k (77.7%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. Released in 2024, it represents Meta's latest advancement in AI technology.

Meta

Llama 3.2 3B Instruct

Meta

2024-09-25

Google

Gemini 2.0 Flash-Lite

Google

2025-02-05

4 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 2.0 Flash-Lite

Input:$0.07
Output:$0.30
Meta

Llama 3.2 3B Instruct

$0.34 cheaper
Input:$0.01
Output:$0.02

Performance Metrics

Context window and performance specifications

Google

Gemini 2.0 Flash-Lite

Larger context
Max Context:1.1M
Meta

Llama 3.2 3B Instruct

Max Context:256.0K
Parameters:3.2B

Average performance across 2 common benchmarks

Google

Gemini 2.0 Flash-Lite

+28.8%
Average Score:69.2%
Meta

Llama 3.2 3B Instruct

Average Score:40.4%

Performance comparison across key benchmark categories

Google

Gemini 2.0 Flash-Lite

code
28.9%
math
+9.8%
71.0%
general
+13.0%
55.5%
Meta

Llama 3.2 3B Instruct

code
+48.5%
77.4%
math
61.3%
general
42.5%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemini 2.0 Flash-Lite

2024-06-01

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-Lite

1 providers

Google

Throughput: 85 tok/s
Latency: 0.7ms
Meta

Llama 3.2 3B Instruct

1 providers

DeepInfra

Throughput: 171.5 tok/s
Latency: 0.24ms
Google

Gemini 2.0 Flash-Lite

+28.8%
Avg Score:69.2%
Providers:1
Meta

Llama 3.2 3B Instruct

Avg Score:40.4%
Providers:1