Understand and compare
GPT-4
vs.
Mistral Large
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Podial
Turn your documents into engaging podcast discussions.
Overview
GPT-4 was released
12 months before
Mistral Large.
GPT-4
|
Mistral Large
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
OpenAI
|
Mistral
|
|
Input Context Window
The number of tokens supported by the input context window.
|
8,192
tokens
|
32K
tokens
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
8,192
tokens
|
4,096
tokens
|
|
Release Date
When the model was first released.
|
2023-03-14
|
2024-02-26
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
September 2021
|
Unknown
|
|
Open Source
|
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
|
Pricing
GPT-4 is
roughly 3.8x more expensive compared
to Mistral Large for input tokens and
roughly 7.5x more expensive
for output tokens.
|
GPT-4
|
Mistral Large
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
$30.00
per million tokens
|
$8.00
per million tokens
|
|
Output
Cost of output tokens generated by the model.
|
$60.00
per million tokens
|
$8.00
per million tokens
|
Benchmarks
Compare relevant benchmarks between GPT-4
and Mistral Large.
|
GPT-4
|
Mistral Large
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
86.4
(5-shot)
|
81.2
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
34.9
|
Benchmark not available.
|
|
HellaSwag
A challenging sentence completion benchmark.
|
95.3
(10-shot)
|
89.2
(10-shot)
|
|
GSM8K
Grade-school math problems benchmark.
|
92.0
(5-shot)
|
Benchmark not available.
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
67.0
(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.
|
GPT-4, developed by OpenAI, features a context window of 8192 tokens. The model costs 3.0 cents per thousand tokens for input and 6.0 cents per thousand tokens for output. It was released on March 14, 2023, and has achieved impressive scores in benchmarks like HellaSwag with a score of 95.3 in a 10-shot scenario and MMLU with a score of 86.4 in 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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