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GPT-4.1 nano vs Llama 3.1 70B Instruct
Comprehensive side-by-side LLM comparison
Llama 3.1 70B Instruct leads with 2.6% higher average benchmark score. GPT-4.1 nano offers 824.3K more tokens in context window than Llama 3.1 70B Instruct. Both models have similar pricing. GPT-4.1 nano supports multimodal inputs. Llama 3.1 70B Instruct is available on 9 providers. Both models have their strengths depending on your specific coding needs.
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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.
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Meta
Llama 3.1 70B was created as a balanced open-source model, designed to provide strong performance with 70 billion parameters while remaining practical for many deployment scenarios. Built to serve as a versatile foundation for fine-tuning and application development, it combines capability with accessibility in the open-source ecosystem.
8 months newer
Llama 3.1 70B Instruct
Meta
2024-07-23
GPT-4.1 nano
OpenAI
2025-04-14
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Pricing Comparison
Cost per million tokens (USD)
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GPT-4.1 nano
Input:$0.10
Output:$0.40
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Llama 3.1 70B Instruct
Input:$0.20
Output:$0.20($0.10 cheaper)
Performance Metrics
Context window and performance specifications
Average performance across 3 common benchmarks
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GPT-4.1 nano
Average Score:68.3%
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Llama 3.1 70B Instruct
Average Score:70.9%(+2.6%)
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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
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GPT-4.1 nano
1 providers
OpenAI
Throughput: 200 tok/s
Latency: 2ms
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Llama 3.1 70B Instruct
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GPT-4.1 nano
Avg Score:68.3%
Providers:1
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Llama 3.1 70B Instruct
Avg Score:70.9%(+2.6%)
Providers:9