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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

Gemma 3 4B

Google

Gemma 3 4B is a multimodal language model developed by Google. The model shows competitive results across 26 benchmarks. It excels particularly in IFEval (90.2%), GSM8k (89.2%), DocVQA (75.8%). It supports a 262K token context window for handling large documents. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Google's latest advancement in AI technology.

DeepSeek

DeepSeek-V3.1

DeepSeek

2025-01-10

Google

Gemma 3 4B

Google

2025-03-12

2 months newer

Pricing Comparison

Cost per million tokens (USD)

DeepSeek

DeepSeek-V3.1

Input:$0.27
Output:$1.00
Google

Gemma 3 4B

$1.21 cheaper
Input:$0.02
Output:$0.04

Performance Metrics

Context window and performance specifications

DeepSeek

DeepSeek-V3.1

Larger context
Max Context:327.7K
Parameters:671.0B
Google

Gemma 3 4B

Max Context:262.1K
Parameters:4.0B

Average performance across 3 common benchmarks

DeepSeek

DeepSeek-V3.1

+57.8%
Average Score:77.8%
Google

Gemma 3 4B

Average Score:20.1%

Performance comparison across key benchmark categories

DeepSeek

DeepSeek-V3.1

factuality
+55.6%
92.6%
math
41.6%
code
56.5%
general
+12.9%
57.3%
Google

Gemma 3 4B

factuality
37.0%
math
+22.8%
64.5%
code
+5.1%
61.5%
general
44.3%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemma 3 4B

2024-08-01

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

Gemma 3 4B

1 providers

DeepInfra

Throughput: 33 tok/s
Latency: 0.2ms
DeepSeek

DeepSeek-V3.1

+57.8%
Avg Score:77.8%
Providers:2
Google

Gemma 3 4B

Avg Score:20.1%
Providers:1