Model Comparison

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

Gemma 3n E4B Instructed

Google

Gemma 3n E4B Instructed is a multimodal language model developed by Google. The model shows competitive results across 18 benchmarks. Notable strengths include HumanEval (75.0%), MGSM (67.0%), MMLU (64.9%). 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.

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.

Qwen2.5-Coder 32B Instruct

Alibaba

2024-09-19

Gemma 3n E4B Instructed

Google

2025-06-26

9 months newer

Pricing Comparison

Cost per million tokens (USD)

Gemma 3n E4B Instructed

Input:$20.00

Output:$40.00

Qwen2.5-Coder 32B Instruct

$59.82 cheaper

Input:$0.09

Output:$0.09

Performance Metrics

Context window and performance specifications

Gemma 3n E4B Instructed

Max Context:64.0K

Parameters:8.0B

Qwen2.5-Coder 32B Instruct

Larger context

Max Context:256.0K

Parameters:32.0B

Average performance across 5 common benchmarks

Gemma 3n E4B Instructed

Average Score:53.5%

Qwen2.5-Coder 32B Instruct

+14.5%

Average Score:68.0%

Performance comparison across key benchmark categories

Gemma 3n E4B Instructed

math

52.4%

general

41.6%

code

38.9%

Qwen2.5-Coder 32B Instruct

math

+21.8%

74.2%

general

+19.9%

61.5%

code

+19.3%

58.2%

Benchmark Scores - Detailed View

Side-by-side comparison of all benchmark scores

Knowledge Cutoff

Training data recency comparison

Gemma 3n E4B Instructed

2024-06-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Gemma 3n E4B Instructed

1 providers

Together

Throughput: 42.09 tok/s

Latency: 0.43ms

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

Gemma 3n E4B Instructed

Avg Score:53.5%

Providers:1

Qwen2.5-Coder 32B Instruct

+14.5%

Avg Score:68.0%

Providers:4