Understand and compare
Llama 3 70B Instruct
vs.
Mistral Large
Try
Podial
Turn your documents into engaging podcast discussions.
Overview
Llama 3 70B Instruct was released
about 2 months after
Mistral Large.
Llama 3 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.
|
8,000
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.
|
2024-04-18
|
2024-02-26
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
December 2023
|
Unknown
|
|
Open Source
|
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
|
Pricing
|
Llama 3 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 70B Instruct
and Mistral Large.
|
Llama 3 70B Instruct
|
Mistral Large
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
82.0
(5-shot)
|
81.2
(5-shot)
|
|
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)
|
|
GSM8K
Grade-school math problems benchmark.
|
93.0
(8-shot)
|
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.
|
51.0
(0-shot)
|
Benchmark not available.
|
Llama 3 70B Instruct, developed by Meta, features a context window of 8000 tokens. The model was released on April 18, 2024, and achieved a score of 82.0 in the MMLU benchmark under a 5-shot scenario.
Mistral Large, developed by Mistral, features a context window of 32000 tokens. The model is priced at 0.8 cents per thousand tokens for both input and output. It was released on February 26, 2024, and has achieved impressive scores in benchmarks like MMLU (81.2 in a 5-shot scenario) and HellaSwag (89.2 in a 10-shot scenario).
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