Understand and compare
Llama 2 Chat 70B
vs.
Mistral Large
Try
Podial
Turn your documents into engaging podcast discussions.
Overview
Llama 2 Chat 70B was released
7 months before
Mistral Large.
Llama 2 Chat 70B
|
Mistral Large
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
Meta
|
Mistral
|
|
Input Context Window
The number of tokens supported by the input context window.
|
4,096
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.
|
2023-07-18
|
2024-02-26
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
September 2022
|
Unknown
|
|
Open Source
|
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
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Pricing
|
Llama 2 Chat 70B
|
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 2 Chat 70B
and Mistral Large.
|
Llama 2 Chat 70B
|
Mistral Large
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
68.9
|
81.2
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
30.1
|
Benchmark not available.
|
|
HellaSwag
A challenging sentence completion benchmark.
|
85.3
(0-shot)
|
89.2
(10-shot)
|
|
GSM8K
Grade-school math problems benchmark.
|
56.8
(8-shot)
|
Benchmark not available.
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
29.9
(0-shot)
|
Benchmark not available.
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
Benchmark not available.
|
Benchmark not available.
|
Llama 2 Chat 70B, developed by Meta, features a context window of 4096 tokens. The model was released on July 18, 2023, and has achieved a score of 30.1 in the MMMU benchmark and 68.9 in the MMLU benchmark.
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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