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Model Comparison

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

DeepSeek

DeepSeek-V3.1

DeepSeek

DeepSeek-V3.1 is a language model developed by DeepSeek. The model shows competitive results across 16 benchmarks. It excels particularly in SimpleQA (93.4%), MMLU-Redux (91.8%), MMLU-Pro (83.7%). The model shows particular specialization in factuality tasks with an average performance of 92.6%. It supports a 328K 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 2.0 Flash-Lite

Google

Gemini 2.0 Flash-Lite is a multimodal language model developed by Google. The model shows competitive results across 13 benchmarks. It excels particularly in MATH (86.8%), FACTS Grounding (83.6%), Global-MMLU-Lite (78.2%). 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.

DeepSeek

DeepSeek-V3.1

DeepSeek

2025-01-10

Google

Gemini 2.0 Flash-Lite

Google

2025-02-05

26 days newer

Pricing Comparison

Cost per million tokens (USD)

DeepSeek

DeepSeek-V3.1

Input:$0.27
Output:$1.00
Google

Gemini 2.0 Flash-Lite

$0.90 cheaper
Input:$0.07
Output:$0.30

Performance Metrics

Context window and performance specifications

DeepSeek

DeepSeek-V3.1

Max Context:327.7K
Parameters:671.0B
Google

Gemini 2.0 Flash-Lite

Larger context
Max Context:1.1M

Average performance across 2 common benchmarks

DeepSeek

DeepSeek-V3.1

+41.9%
Average Score:88.5%
Google

Gemini 2.0 Flash-Lite

Average Score:46.6%

Performance comparison across key benchmark categories

DeepSeek

DeepSeek-V3.1

factuality
+40.0%
92.6%
math
41.6%
general
57.3%
code
+27.6%
56.5%
Google

Gemini 2.0 Flash-Lite

factuality
52.6%
math
+29.4%
71.0%
general
+3.0%
60.3%
code
28.9%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemini 2.0 Flash-Lite

2024-06-01

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

DeepSeek

DeepSeek-V3.1

2 providers

DeepInfra

Novita

Google

Gemini 2.0 Flash-Lite

1 providers

Google

Throughput: 85 tok/s
Latency: 0.7ms
DeepSeek

DeepSeek-V3.1

+41.9%
Avg Score:88.5%
Providers:2
Google

Gemini 2.0 Flash-Lite

Avg Score:46.6%
Providers:1