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DeepSeek-R1 vs Llama 3.2 11B Instruct
Comprehensive side-by-side LLM comparison
DeepSeek-R1 offers 6.1K more tokens in context window than Llama 3.2 11B Instruct. Llama 3.2 11B Instruct is $2.64 cheaper per million tokens. Llama 3.2 11B Instruct supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
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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.
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Meta
Llama 3.2 11B was introduced as a mid-sized variant in the Llama 3.2 family, designed to offer enhanced capabilities while maintaining efficiency. Built to provide a balanced option for applications requiring more than lightweight models but less than flagship sizes, it serves diverse use cases in the open-source community.
3 months newer
Llama 3.2 11B Instruct
Meta
2024-09-25
DeepSeek-R1
DeepSeek
2025-01-20
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Pricing Comparison
Cost per million tokens (USD)
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DeepSeek-R1
Input:$0.55
Output:$2.19
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Llama 3.2 11B Instruct
Input:$0.05
Output:$0.05($2.64 cheaper)
Performance Metrics
Context window and performance specifications
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Knowledge Cutoff
Training data recency comparison
Llama 3.2 11B Instruct
2023-12-31
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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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
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DeepSeek-R1
Avg Score:0.0%
Providers:5
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Llama 3.2 11B Instruct
Avg Score:0.0%
Providers:6