Deep Seek-R1 vs LLaMA 3.1 (405B): Benchmarks, Pricing, and Context Window Comparison
Deep Seek-R1 vs LLaMA 3.1 (405B) compares provider, context window, token pricing, benchmark performance, and release timeline in one side-by-side view. Use this page to quickly identify which model is a better fit for your production constraints, quality targets, and estimated cost per request.
Verdict
Deep Seek-R1 has lower listed token pricing, while LLaMA 3.1 (405B) can still be preferable if benchmark results better match your workload.
Author: Mirai Minds Research Team
Last updated:
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Overview
Deep Seek-R1 was released 6 months after LLaMA 3.1 (405B).
Provider The entity that provides this model. | ||
Input Context Window The number of tokens supported by the input context window. | 128K tokens | 128K tokens |
Maximum Output Tokens The number of tokens that can be generated by the model in a single request. | 32K tokens | 2,048 tokens |
Release Date When the model was first released. | Jan 21, 2025 over 1 yearago 2025-01-21 | Jul 23, 2024 over 1 year 2024-07-23 |
Leaderboard
Rank | Unknown | Unknown |
Arena Elo | Not specified. | Not specified. |
95% CI | Not specified. | Not specified. |
Votes | Not specified. | Not specified. |
License | Not specified. | Not specified. |
Knowledge Cutoff | Unknown | Unknown |
Pricing
Input Cost of input data provided to the model. | $0.55 per million tokens | $1.79 per million tokens |
Output Cost of output tokens generated by the model. | $2.19 per million tokens | $1.79 per million tokens |
Benchmarks
Compare relevant benchmarks between Deep Seek-R1 and LLaMA 3.1 (405B) Instruct.
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | 90.8 (5-shot) | 85.2 (5-shot) |
MMMU A wide ranging multi-discipline and multimodal benchmark. | Benchmark not available. | Benchmark not available. |
HellaSwag A challenging sentence completion benchmark. | Benchmark not available. | Benchmark not available. |
