+
+
+
+
+
+
+
+
VS
VS
VS
VS
VS
VS
DeepSeek-R1 vs GPT-4.1 nano
Comprehensive side-by-side LLM comparison
GPT-4.1 nano offers 818.2K more tokens in context window than DeepSeek-R1. GPT-4.1 nano is $2.24 cheaper per million tokens. GPT-4.1 nano supports multimodal inputs. DeepSeek-R1 is available on 5 providers. Both models have their strengths depending on your specific coding needs.
+
+
+
+
DeepSeek
DeepSeek-R1 was developed as a reasoning-focused language model, designed to combine chain-of-thought reasoning with reinforcement learning techniques. Built to excel at complex problem-solving through trial-and-error learning and deliberate analytical processes, it demonstrates the power of efficient training methods in open-source model development.
+
+
+
+
OpenAI
GPT-4.1 Nano was developed as the smallest and most efficient variant in the GPT-4.1 family, designed for applications requiring minimal latency and resource usage. Built to enable AI capabilities on edge devices and resource-constrained environments, it distills GPT-4.1 capabilities into an ultra-compact form factor.
2 months newer
DeepSeek-R1
DeepSeek
2025-01-20
GPT-4.1 nano
OpenAI
2025-04-14
+
+
+
+
+
+
+
+
+
+
+
+
Pricing Comparison
Cost per million tokens (USD)
+
+
+
+
DeepSeek-R1
Input:$0.55
Output:$2.19
+
+
+
+
GPT-4.1 nano
Input:$0.10
Output:$0.40($2.24 cheaper)
Performance Metrics
Context window and performance specifications
+
+
+
+
Knowledge Cutoff
Training data recency comparison
GPT-4.1 nano
2024-05-31
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
+
+
+
+
DeepSeek-R1
5 providers
DeepInfra
Throughput: 0.9 tok/s
Latency: 0.3ms
DeepSeek
Throughput: 9 tok/s
Latency: 0.3ms
Fireworks
Throughput: 2.1 tok/s
Latency: 0.3ms
Together
Throughput: 4 tok/s
Latency: 0.6ms
ZeroEval
+
+
+
+
DeepSeek-R1
Avg Score:0.0%
Providers:5
+
+
+
+
GPT-4.1 nano
Avg Score:0.0%
Providers:1