Improvement or Stagnant? Llama 3.1 and Mistral NeMo

Meta’s Llama 3.1 and Mistral’s NVIDIA-developed Mistral NeMo are new openly available language-model families that reflect growing competition in capable, locally deployable AI systems. Released in July, Llama 3.1 ranges from 8B to 405B parameters, offers instruction-tuned versions, a 128,000-token context window, multilingual capabilities, and broad Meta product integration, while Mistral NeMo is a 12B-parameter model positioned between smaller 8B and much larger 70B-class models, with the same context size and a Tekken tokenizer intended to improve compression for languages such as Korean and Arabic and for code. Official benchmark comparisons suggest Llama 3.1 8B outperformed NeMo on the shared MMLU metric, while Meta’s human evaluations positioned the 405B model competitively against leading proprietary systems, though its full-precision hardware requirements remain substantial. Informal local tests found that Llama 3.1 8B more reliably rejected an invented factual premise and correctly handled a spelling question, whereas Mistral NeMo produced a functional two-player JavaScript Pong game more consistently despite sometimes hallucinating details around the false scenario. The discussion also notes that benchmarks and isolated tests cannot fully represent real-world performance, and contrasts open-model advances with proprietary developments such as GPT-4o’s emerging conversational voice mode.

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