Llama 3.3 70B vs DeepSeek V3

Llama 3.3 70B vs DeepSeek V3: which should you choose?

Llama 3.3 70B — Meta large language model (2024) with a 128K-token context window and an MMLU score of 86.0%, released with open weights.

DeepSeek V3 — DeepSeek large language model (2024) with a 128K-token context window and an MMLU score of 88.5%, released with open weights.

Llama 3.3 70B vs DeepSeek V3: DeepSeek V3 scores higher on the MMLU benchmark. DeepSeek V3 leads general capability (MMLU 88.5% vs 86.0%).

Capability and reasoning

On MMLU — a 57-subject benchmark of general knowledge and reasoning — the DeepSeek V3 scores 88.5% 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 Llama 3.3 70B 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 3.3 70B is open weights and DeepSeek V3 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.