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

Qwen3 32B

Alibaba

Qwen3 32B is a language model developed by Alibaba. It achieves strong performance with an average score of 75.3% across 9 benchmarks. It excels particularly in CodeForces (95.2%), Arena Hard (93.8%), AIME 2024 (81.4%). The model shows particular specialization in code tasks with an average performance of 80.4%. It supports a 256K token context window for handling large documents. The model is available through 3 API providers. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Alibaba's latest advancement in AI technology.

Alibaba

Qwen3 32B

Alibaba

2025-04-29

Google

Gemini 2.5 Flash-Lite

Google

2025-06-17

1 month newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 2.5 Flash-Lite

Input:$0.10
Output:$0.40
Alibaba

Qwen3 32B

$0.10 cheaper
Input:$0.10
Output:$0.30

Performance Metrics

Context window and performance specifications

Google

Gemini 2.5 Flash-Lite

Larger context
Max Context:1.1M
Alibaba

Qwen3 32B

Max Context:256.0K
Parameters:32.8B

Average performance across 2 common benchmarks

Google

Gemini 2.5 Flash-Lite

Average Score:41.8%
Alibaba

Qwen3 32B

+27.6%
Average Score:69.3%

Performance comparison across key benchmark categories

Google

Gemini 2.5 Flash-Lite

code
42.5%
general
35.8%
Alibaba

Qwen3 32B

code
+37.9%
80.4%
general
+37.8%
73.6%
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

Qwen3 32B

3 providers

Sambanova

Throughput: 327.7 tok/s
Latency: 1.08ms

DeepInfra

Throughput: 26.95 tok/s
Latency: 1.19ms

Novita

Throughput: 32.43 tok/s
Latency: 0.93ms
Google

Gemini 2.5 Flash-Lite

Avg Score:41.8%
Providers:1
Alibaba

Qwen3 32B

+27.6%
Avg Score:69.3%
Providers:3