Llama 3.1 405B vs GPT-4o
Head-to-head specifications
| Metric | Llama 3.1 405B | GPT-4o | Difference |
|---|---|---|---|
| MMLU (general capability) | 88.6% | 88.7% | -0.1% |
| Context window | 128K tokens | 128K tokens | — |
| Price (input / output per 1M) | Open weights | $2.5 / $10 | — |
| Access | Open weights | Proprietary API | — |
- GPT-4o leads general capability (MMLU 88.7% vs 88.6%).
Verdict: Llama 3.1 405B or GPT-4o?
Llama 3.1 405B advantages
- No decisive advantage on the tracked metrics.
GPT-4o advantages
- No decisive advantage on the tracked metrics.
Value for money
Llama 3.1 405B is open-weight and can be self-hosted, which can dramatically lower cost at scale versus a per-token API.
Llama 3.1 405B vs GPT-4o: 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.
GPT-4o — OpenAI large language model (2024) with a 128K-token context window and an MMLU score of 88.7%.
Llama 3.1 405B vs GPT-4o: GPT-4o scores higher on the MMLU benchmark. GPT-4o leads general capability (MMLU 88.7% vs 88.6%).
Capability and reasoning
On MMLU — a 57-subject benchmark of general knowledge and reasoning — the GPT-4o scores 88.7% 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 GPT-4o is proprietary api. 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 GPT-4o?
On paper these are near-identical; decide on price, availability or brand preference. GPT-4o leads general capability (MMLU 88.7% vs 88.6%).
What is the main difference between the Llama 3.1 405B and the GPT-4o?
GPT-4o leads general capability (MMLU 88.7% vs 88.6%).
Which is better value?
Llama 3.1 405B is open-weight and can be self-hosted, which can dramatically lower cost at scale versus a per-token API.
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.