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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.

OpenAI

GPT-5 nano

OpenAI

GPT-5 nano is a multimodal language model developed by OpenAI. The model shows competitive results across 5 benchmarks. It excels particularly in AIME 2025 (85.2%), HMMT 2025 (75.6%), GPQA (71.2%). It supports a 528K token context window for handling large documents. 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 OpenAI's latest advancement in AI technology.

DeepSeek

DeepSeek-V3.1

DeepSeek

2025-01-10

OpenAI

GPT-5 nano

OpenAI

2025-08-07

6 months newer

Pricing Comparison

Cost per million tokens (USD)

DeepSeek

DeepSeek-V3.1

Input:$0.27
Output:$1.00
OpenAI

GPT-5 nano

$0.82 cheaper
Input:$0.05
Output:$0.40

Performance Metrics

Context window and performance specifications

DeepSeek

DeepSeek-V3.1

Max Context:327.7K
Parameters:671.0B
OpenAI

GPT-5 nano

Larger context
Max Context:528.0K

Average performance across 3 common benchmarks

DeepSeek

DeepSeek-V3.1

Average Score:33.1%
OpenAI

GPT-5 nano

+23.4%
Average Score:56.5%

Performance comparison across key benchmark categories

DeepSeek

DeepSeek-V3.1

general
+17.3%
57.3%
math
41.6%
OpenAI

GPT-5 nano

general
39.9%
math
+15.1%
56.8%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

GPT-5 nano

2024-05-30

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

OpenAI

GPT-5 nano

2 providers

ZeroEval

Throughput: 500 tok/s
Latency: 0.3ms

OpenAI

Throughput: 500 tok/s
Latency: 0.3ms
DeepSeek

DeepSeek-V3.1

Avg Score:33.1%
Providers:2
OpenAI

GPT-5 nano

+23.4%
Avg Score:56.5%
Providers:2