Model Comparison

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

DeepSeek

DeepSeek-V3.2-Exp

DeepSeek

DeepSeek-V3.2-Exp is a language model developed by DeepSeek. It achieves strong performance with an average score of 66.1% across 14 benchmarks. It excels particularly in SimpleQA (97.1%), AIME 2025 (89.3%), MMLU-Pro (85.0%). It supports a 229K token context window for handling large documents. The model is available through 2 API providers. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents DeepSeek's latest advancement in AI technology.

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.

Google

Gemini 2.5 Flash-Lite

Google

2025-06-17

DeepSeek

DeepSeek-V3.2-Exp

DeepSeek

2025-09-29

3 months newer

Pricing Comparison

Cost per million tokens (USD)

DeepSeek

DeepSeek-V3.2-Exp

Input:$0.27
Output:$0.41
Google

Gemini 2.5 Flash-Lite

$0.18 cheaper
Input:$0.10
Output:$0.40

Performance Metrics

Context window and performance specifications

DeepSeek

DeepSeek-V3.2-Exp

Max Context:229.4K
Parameters:685.0B
Google

Gemini 2.5 Flash-Lite

Larger context
Max Context:1.1M

Average performance across 19 common benchmarks

DeepSeek

DeepSeek-V3.2-Exp

+17.7%
Average Score:45.7%
Google

Gemini 2.5 Flash-Lite

Average Score:27.9%

Performance comparison across key benchmark categories

DeepSeek

DeepSeek-V3.2-Exp

factuality
+49.7%
97.1%
math
+36.7%
86.5%
code
+27.0%
64.3%
general
+22.3%
62.1%
Google

Gemini 2.5 Flash-Lite

factuality
47.4%
math
49.8%
code
37.2%
general
39.8%
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

DeepSeek

DeepSeek-V3.2-Exp

2 providers

Novita

ZeroEval

Throughput: 100 tok/s
Latency: 0.7ms
Google

Gemini 2.5 Flash-Lite

1 providers

Google

Throughput: 5.69 tok/s
Latency: 0.44ms
DeepSeek

DeepSeek-V3.2-Exp

+17.7%
Avg Score:45.7%
Providers:2
Google

Gemini 2.5 Flash-Lite

Avg Score:27.9%
Providers:1