Llama 3.1 405B vs DeepSeek R1
Head-to-head specifications
| Metric | Llama 3.1 405B | DeepSeek R1 | Difference |
|---|---|---|---|
| MMLU (general capability) | 88.6% | 90.8% | -2.2% |
| Context window | 128K tokens | 128K tokens | — |
| Price (input / output per 1M) | Open weights | Open weights | — |
| Access | Open weights | Open weights | — |
- DeepSeek R1 leads general capability (MMLU 90.8% vs 88.6%).
Verdict: Llama 3.1 405B or DeepSeek R1?
Llama 3.1 405B advantages
- No decisive advantage on the tracked metrics.
DeepSeek R1 advantages
- No decisive advantage on the tracked metrics.
Value for money
Both are open-weight models you can self-host, so running cost depends on your own infrastructure rather than API pricing.
Llama 3.1 405B vs DeepSeek R1: which should you choose?
Llama 3.1 405B — Meta large language model (2024) with a 128K-token context window and an MMLU score of 88.6%, released with open weights.
DeepSeek R1 — DeepSeek large language model (2025) with a 128K-token context window and an MMLU score of 90.8%, released with open weights.
Llama 3.1 405B vs DeepSeek R1: DeepSeek R1 scores higher on the MMLU benchmark. DeepSeek R1 leads general capability (MMLU 90.8% vs 88.6%).
Capability and reasoning
On MMLU — a 57-subject benchmark of general knowledge and reasoning — the DeepSeek R1 scores 90.8% versus 88.6%. MMLU is a useful proxy for raw knowledge but does not capture instruction-following, coding, tool use, latency or safety, so treat it as one signal among several.
Context window
The Llama 3.1 405B handles up to 128K tokens per request, which sets how much documentation, transcript or code it can reason over at once — decisive for retrieval-augmented and long-document workflows.
Pricing and access
Llama 3.1 405B is open weights and DeepSeek R1 is open weights. Proprietary models bill per token via API; open-weight models can be self-hosted, trading per-call cost for infrastructure you manage. For production, weigh throughput, rate limits and data-residency needs alongside headline price.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the Llama 3.1 405B better than the DeepSeek R1?
On paper these are near-identical; decide on price, availability or brand preference. DeepSeek R1 leads general capability (MMLU 90.8% vs 88.6%).
What is the main difference between the Llama 3.1 405B and the DeepSeek R1?
DeepSeek R1 leads general capability (MMLU 90.8% vs 88.6%).
Which is better value?
Both are open-weight models you can self-host, so running cost depends on your own infrastructure rather than API pricing.
Methodology
Large language models are compared on the MMLU benchmark (a widely-cited 57-subject test of general knowledge and reasoning, reported as a percentage), maximum context window, and published API pricing per million input and output tokens. Open-weight models can also be self-hosted. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit.