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 3.1 405B Instruct

Meta

Llama 3.1 405B Instruct is a language model developed by Meta. It achieves strong performance with an average score of 79.2% across 18 benchmarks. It excels particularly in ARC-C (96.9%), GSM8k (96.8%), API-Bank (92.0%). It supports a 256K token context window for handling large documents. The model is available through 8 API providers. Released in 2024, it represents Meta's latest advancement in AI technology.

Meta

Llama 3.1 405B Instruct

Meta

2024-07-23

Google

Gemini 2.0 Flash-Lite

Google

2025-02-05

6 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 2.0 Flash-Lite

$1.41 cheaper
Input:$0.07
Output:$0.30
Meta

Llama 3.1 405B Instruct

Input:$0.89
Output:$0.89

Performance Metrics

Context window and performance specifications

Google

Gemini 2.0 Flash-Lite

Larger context
Max Context:1.1M
Meta

Llama 3.1 405B Instruct

Max Context:256.0K
Parameters:405.0B

Average performance across 3 common benchmarks

Google

Gemini 2.0 Flash-Lite

+4.0%
Average Score:70.0%
Meta

Llama 3.1 405B Instruct

Average Score:65.9%

Performance comparison across key benchmark categories

Google

Gemini 2.0 Flash-Lite

math
71.0%
code
28.9%
general
55.5%
Meta

Llama 3.1 405B Instruct

math
+16.4%
87.4%
code
+52.5%
81.4%
general
+17.7%
73.2%
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 3.1 405B Instruct

8 providers

Together

Throughput: 35 tok/s
Latency: 0.5ms

Hyperbolic

Throughput: 40 tok/s
Latency: 0.5ms

DeepInfra

Throughput: 27 tok/s
Latency: 0.5ms

Google

Throughput: 42 tok/s
Latency: 0.4ms

Fireworks

Throughput: 78 tok/s
Latency: 0.5ms

Bedrock

Throughput: 100 tok/s
Latency: 0.5ms

Lambda

Throughput: 42 tok/s
Latency: 0.5ms

Replicate

Throughput: 22 tok/s
Latency: 0.5ms
Google

Gemini 2.0 Flash-Lite

+4.0%
Avg Score:70.0%
Providers:1
Meta

Llama 3.1 405B Instruct

Avg Score:65.9%
Providers:8