Model Comparison

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

OpenAI

GPT-5 nano

OpenAI

GPT-5 nano is a multimodal language model developed by OpenAI. The model shows competitive results across 5 benchmarks. It excels particularly in AIME 2025 (85.2%), HMMT 2025 (75.6%), GPQA (71.2%). It supports a 528K token context window for handling large documents. The model is available through 2 API providers. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents OpenAI's latest advancement in AI technology.

Meta

Llama 4 Scout

Meta

Llama 4 Scout is a multimodal language model developed by Meta. It achieves strong performance with an average score of 67.3% across 12 benchmarks. It excels particularly in DocVQA (94.4%), MGSM (90.6%), ChartQA (88.8%). The model shows particular specialization in vision tasks with an average performance of 81.9%. With a 20.0M token context window, it can handle extensive documents and complex multi-turn conversations. 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 2025, it represents Meta's latest advancement in AI technology.

Meta

Llama 4 Scout

Meta

2025-04-05

OpenAI

GPT-5 nano

OpenAI

2025-08-07

4 months newer

Pricing Comparison

Cost per million tokens (USD)

OpenAI

GPT-5 nano

Input:$0.05
Output:$0.40
Meta

Llama 4 Scout

$0.07 cheaper
Input:$0.08
Output:$0.30

Performance Metrics

Context window and performance specifications

OpenAI

GPT-5 nano

Max Context:528.0K
Meta

Llama 4 Scout

Larger context
Max Context:20.0M
Parameters:109.0B

Average performance across 1 common benchmarks

OpenAI

GPT-5 nano

+14.0%
Average Score:71.2%
Meta

Llama 4 Scout

Average Score:57.2%

Performance comparison across key benchmark categories

OpenAI

GPT-5 nano

math
9.6%
general
60.2%
Meta

Llama 4 Scout

math
+60.9%
70.5%
general
+6.1%
66.3%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

GPT-5 nano

2024-05-30

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

OpenAI

GPT-5 nano

2 providers

ZeroEval

Throughput: 500 tok/s
Latency: 0.3ms

OpenAI

Throughput: 500 tok/s
Latency: 0.3ms
Meta

Llama 4 Scout

6 providers

Together

Throughput: 106.9 tok/s
Latency: 0.54ms

DeepInfra

Throughput: 76.1 tok/s
Latency: 0.31ms

Fireworks

Throughput: 116.1 tok/s
Latency: 0.53ms

Groq

Throughput: 776.1 tok/s
Latency: 1.08ms

Novita

Throughput: 69.82 tok/s
Latency: 0.85ms

Lambda

Throughput: 139.7 tok/s
Latency: 0.43ms
OpenAI

GPT-5 nano

+14.0%
Avg Score:71.2%
Providers:2
Meta

Llama 4 Scout

Avg Score:57.2%
Providers:6