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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 2.5 Pro Preview 06-05

Google

Gemini 2.5 Pro Preview 06-05 is a multimodal language model developed by Google. It achieves strong performance with an average score of 68.8% across 13 benchmarks. It excels particularly in Global-MMLU-Lite (89.2%), AIME 2025 (88.0%), FACTS Grounding (87.8%). 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 2.5 Pro Preview 06-05

Google

2025-06-05

4 months newer

Pricing Comparison

Cost per million tokens (USD)

DeepSeek

DeepSeek-V3.1

$9.98 cheaper
Input:$0.27
Output:$1.00
Google

Gemini 2.5 Pro Preview 06-05

Input:$1.25
Output:$10.00

Performance Metrics

Context window and performance specifications

DeepSeek

DeepSeek-V3.1

Max Context:327.7K
Parameters:671.0B
Google

Gemini 2.5 Pro Preview 06-05

Larger context
Max Context:1.1M

Average performance across 6 common benchmarks

DeepSeek

DeepSeek-V3.1

Average Score:58.3%
Google

Gemini 2.5 Pro Preview 06-05

+5.3%
Average Score:63.7%

Performance comparison across key benchmark categories

DeepSeek

DeepSeek-V3.1

factuality
+21.7%
92.6%
math
41.6%
code
56.5%
general
57.3%
Google

Gemini 2.5 Pro Preview 06-05

factuality
70.9%
math
+46.4%
88.0%
code
+16.4%
72.8%
general
+8.8%
66.1%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemini 2.5 Pro Preview 06-05

2025-01-31

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 2.5 Pro Preview 06-05

1 providers

Google

Throughput: 85 tok/s
Latency: 0.7ms
DeepSeek

DeepSeek-V3.1

Avg Score:58.3%
Providers:2
Google

Gemini 2.5 Pro Preview 06-05

+5.3%
Avg Score:63.7%
Providers:1