Model Comparison

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

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.

Alibaba

Qwen2.5-Coder 32B Instruct

Alibaba

Qwen2.5-Coder 32B Instruct is a language model developed by Alibaba. It achieves strong performance with an average score of 64.9% across 15 benchmarks. It excels particularly in HumanEval (92.7%), GSM8k (91.1%), MBPP (90.2%). The model shows particular specialization in reasoning tasks with an average performance of 78.1%. It supports a 256K token context window for handling large documents. The model is available through 4 API providers. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Alibaba's latest advancement in AI technology.

Alibaba

Qwen2.5-Coder 32B Instruct

Alibaba

2024-09-19

Meta

Llama 4 Scout

Meta

2025-04-05

6 months newer

Pricing Comparison

Cost per million tokens (USD)

Meta

Llama 4 Scout

Input:$0.08
Output:$0.30
Alibaba

Qwen2.5-Coder 32B Instruct

$0.20 cheaper
Input:$0.09
Output:$0.09

Performance Metrics

Context window and performance specifications

Meta

Llama 4 Scout

Larger context
Max Context:20.0M
Parameters:109.0B
Alibaba

Qwen2.5-Coder 32B Instruct

Max Context:256.0K
Parameters:32.0B

Average performance across 5 common benchmarks

Meta

Llama 4 Scout

+0.1%
Average Score:61.0%
Alibaba

Qwen2.5-Coder 32B Instruct

Average Score:60.9%

Performance comparison across key benchmark categories

Meta

Llama 4 Scout

math
70.5%
general
+4.8%
66.3%
code
50.3%
Alibaba

Qwen2.5-Coder 32B Instruct

math
+3.6%
74.2%
general
61.5%
code
+7.9%
58.2%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores

Provider Availability & Performance

Available providers and their performance metrics

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
Alibaba

Qwen2.5-Coder 32B Instruct

4 providers

Hyperbolic

Throughput: 100 tok/s
Latency: 0.5ms

DeepInfra

Throughput: 44 tok/s
Latency: 0.5ms

Fireworks

Throughput: 110 tok/s
Latency: 0.26ms

Lambda

Throughput: 42 tok/s
Latency: 0.5ms
Meta

Llama 4 Scout

+0.1%
Avg Score:61.0%
Providers:6
Alibaba

Qwen2.5-Coder 32B Instruct

Avg Score:60.9%
Providers:4