GPT-4o vs Llama 3.1 70B

GPT-4o vs Llama 3.1 70B: which should you choose?

GPT-4o — OpenAI large language model (2024) with a 128K-token context window and an MMLU score of 88.7%.

Llama 3.1 70B — Meta large language model (2024) with a 128K-token context window and an MMLU score of 86.0%, released with open weights.

GPT-4o vs Llama 3.1 70B: GPT-4o scores higher on the MMLU benchmark. GPT-4o leads general capability (MMLU 88.7% vs 86.0%).

Capability and reasoning

On MMLU — a 57-subject benchmark of general knowledge and reasoning — the GPT-4o scores 88.7% versus 86.0%. 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 GPT-4o 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

GPT-4o is proprietary api and Llama 3.1 70B 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.