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 3.3 70B Instruct

Meta

Llama 3.3 70B Instruct is a language model developed by Meta. It achieves strong performance with an average score of 79.9% across 9 benchmarks. It excels particularly in IFEval (92.1%), MGSM (91.1%), HumanEval (88.4%). The model shows particular specialization in code tasks with an average performance of 89.4%. It supports a 256K token context window for handling large documents. The model is available through 9 API providers. Released in 2024, it represents Meta's latest advancement in AI technology.

Meta

Llama 3.3 70B Instruct

Meta

2024-12-06

Google

Gemini 2.0 Flash-Lite

Google

2025-02-05

2 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 2.0 Flash-Lite

$0.03 cheaper
Input:$0.07
Output:$0.30
Meta

Llama 3.3 70B Instruct

Input:$0.20
Output:$0.20

Performance Metrics

Context window and performance specifications

Google

Gemini 2.0 Flash-Lite

Larger context
Max Context:1.1M
Meta

Llama 3.3 70B Instruct

Max Context:256.0K
Parameters:70.0B

Average performance across 3 common benchmarks

Google

Gemini 2.0 Flash-Lite

+4.5%
Average Score:70.0%
Meta

Llama 3.3 70B Instruct

Average Score:65.5%

Performance comparison across key benchmark categories

Google

Gemini 2.0 Flash-Lite

code
28.9%
math
71.0%
general
55.5%
Meta

Llama 3.3 70B Instruct

code
+60.5%
89.4%
math
+13.0%
84.0%
general
+15.2%
70.7%
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 3.3 70B Instruct

9 providers

Sambanova

Throughput: 1096 tok/s
Latency: 0.65ms

Together

Throughput: 65 tok/s
Latency: 0.65ms

Hyperbolic

Throughput: 42 tok/s
Latency: 0.65ms

DeepInfra

Throughput: 37 tok/s
Latency: 0.65ms

Fireworks

Throughput: 197 tok/s
Latency: 0.65ms

Groq

Throughput: 268 tok/s
Latency: 0.65ms

Bedrock

Throughput: 100 tok/s
Latency: 0.5ms

Lambda

Throughput: 42 tok/s
Latency: 0.65ms

Cerebras

Throughput: 2220 tok/s
Latency: 0.65ms
Google

Gemini 2.0 Flash-Lite

+4.5%
Avg Score:70.0%
Providers:1
Meta

Llama 3.3 70B Instruct

Avg Score:65.5%
Providers:9