Compare Llama 3.1 70B Instruct vs. Mistral Large

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Understand and compare Llama 3.1 70B Instruct vs. Mistral Large

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Overview

Llama 3.1 70B Instruct was released 5 months after Mistral Large.

	Llama 3.1 70B Instruct	Mistral Large
Provider The entity that provides this model.	Meta	Mistral
Input Context Window The number of tokens supported by the input context window.	128K tokens	32K tokens
Maximum Output Tokens The number of tokens that can be generated by the model in a single request.	2,048 tokens	4,096 tokens
Release Date When the model was first released.	July 23rd, 2024 about 2 months ago 2024-07-23	February 26th, 2024 7 months ago 2024-02-26
Knowledge Cutoff Limit on the knowledge base used by the model.	December 2023 December 2023	Unknown
Open Source	Yes	Yes
API Providers The providers that offer this model. (This is not an exhaustive list.)	Azure AI, AWS Bedrock, Google Cloud Vertex AI Model Garden, NVIDIA NIM, IBM watsonx	Azure AI, AWS Bedrock, Google Cloud Vertex AI Model Garden, Snowflake Cortex

Pricing

	Llama 3.1 70B Instruct	Mistral Large
Input Cost of input data provided to the model.	Pricing not available.	$8.00 per million tokens
Output Cost of output tokens generated by the model.	Pricing not available.	$8.00 per million tokens

Benchmarks

Compare relevant benchmarks between Llama 3.1 70B Instruct and Mistral Large.

	Llama 3.1 70B Instruct	Mistral Large
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.	83.6 (5-shot) Source	81.2 (5-shot) Source
MMMU A wide ranging multi-discipline and multimodal benchmark.	Benchmark not available.	Benchmark not available.
HellaSwag A challenging sentence completion benchmark.	Benchmark not available.	89.2 (10-shot) Source
GSM8K Grade-school math problems benchmark.	95.1 (8-shot) Source	Benchmark not available.
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings.	Benchmark not available.	Benchmark not available.
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.	68.0 (0-shot) Source	Benchmark not available.