Model Comparison

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

Google

Gemini 2.5 Flash-Lite

Google

Gemini 2.5 Flash-Lite is a multimodal language model developed by Google. The model shows competitive results across 13 benchmarks. It excels particularly in FACTS Grounding (84.1%), Global-MMLU-Lite (81.1%), MMMU (72.9%). 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.

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

Google

Gemini 2.5 Flash-Lite

Google

2025-06-17

9 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 2.5 Flash-Lite

Input:$0.10
Output:$0.40
Alibaba

Qwen2.5-Coder 32B Instruct

$0.32 cheaper
Input:$0.09
Output:$0.09

Performance Metrics

Context window and performance specifications

Google

Gemini 2.5 Flash-Lite

Larger context
Max Context:1.1M
Alibaba

Qwen2.5-Coder 32B Instruct

Max Context:256.0K
Parameters:32.0B

Average performance across 1 common benchmarks

Google

Gemini 2.5 Flash-Lite

+2.3%
Average Score:33.7%
Alibaba

Qwen2.5-Coder 32B Instruct

Average Score:31.4%

Performance comparison across key benchmark categories

Google

Gemini 2.5 Flash-Lite

factuality
+29.9%
84.1%
reasoning
2.5%
general
35.8%
code
42.5%
Alibaba

Qwen2.5-Coder 32B Instruct

factuality
54.2%
reasoning
+75.6%
78.1%
general
+25.8%
61.5%
code
+15.7%
58.2%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemini 2.5 Flash-Lite

2025-01-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemini 2.5 Flash-Lite

1 providers

Google

Throughput: 5.69 tok/s
Latency: 0.44ms
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
Google

Gemini 2.5 Flash-Lite

+2.3%
Avg Score:33.7%
Providers:1
Alibaba

Qwen2.5-Coder 32B Instruct

Avg Score:31.4%
Providers:4