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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 Flash

Google

Gemini 2.5 Flash is a multimodal language model developed by Google. It achieves strong performance with an average score of 62.5% across 14 benchmarks. It excels particularly in Global-MMLU-Lite (88.4%), AIME 2024 (88.0%), FACTS Grounding (85.3%). With a 1.1M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 2 API providers. 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 Flash

Google

2025-05-20

4 months newer

Pricing Comparison

Cost per million tokens (USD)

DeepSeek

DeepSeek-V3.1

$1.53 cheaper
Input:$0.27
Output:$1.00
Google

Gemini 2.5 Flash

Input:$0.30
Output:$2.50

Performance Metrics

Context window and performance specifications

DeepSeek

DeepSeek-V3.1

Max Context:327.7K
Parameters:671.0B
Google

Gemini 2.5 Flash

Larger context
Max Context:1.1M

Average performance across 6 common benchmarks

DeepSeek

DeepSeek-V3.1

+6.6%
Average Score:60.0%
Google

Gemini 2.5 Flash

Average Score:53.4%

Performance comparison across key benchmark categories

DeepSeek

DeepSeek-V3.1

factuality
+36.5%
92.6%
math
41.6%
code
56.5%
general
57.3%
Google

Gemini 2.5 Flash

factuality
56.1%
math
+30.4%
72.0%
code
+7.3%
63.7%
general
+2.6%
59.9%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemini 2.5 Flash

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 Flash

2 providers

Google

Throughput: 85 tok/s
Latency: 0.7ms

ZeroEval

Throughput: 85 tok/s
Latency: 0.7ms
DeepSeek

DeepSeek-V3.1

+6.6%
Avg Score:60.0%
Providers:2
Google

Gemini 2.5 Flash

Avg Score:53.4%
Providers:2