Understand and compare
GPT-3.5 Turbo
vs.
Mistral 7B Instruct
Overview
GPT-3.5 Turbo was released
10 months before
Mistral 7B Instruct.
GPT-3.5 Turbo
|
Mistral 7B Instruct
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
OpenAI
|
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.
|
4,096
tokens
|
8,192
tokens
|
|
Release Date
When the model was first released.
|
2022-11-28
|
2023-09-27
|
Pricing
GPT-3.5 Turbo is
roughly 2.0x more expensive compared
to Mistral 7B Instruct for input tokens and
roughly 6.0x more expensive
for output tokens.
|
GPT-3.5 Turbo
|
Mistral 7B Instruct
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
$0.50
per million tokens
|
$0.25
per million tokens
|
|
Output
Cost of output tokens generated by the model.
|
$1.50
per million tokens
|
$0.25
per million tokens
|
Benchmarks
Compare relevant benchmarks between GPT-3.5 Turbo
and Mistral 7B Instruct.
|
GPT-3.5 Turbo
|
Mistral 7B Instruct
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
70.0
(5-shot)
|
60.1
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
Benchmark not available.
|
Benchmark not available.
|
|
HellaSwag
A challenging sentence completion benchmark.
|
85.5
(10-shot)
|
Benchmark not available.
|
GPT-3.5 Turbo, developed by OpenAI, features a context window of 4096 tokens. It is priced at 0.05 cents per thousand tokens for input and 0.15 cents per thousand tokens for output. The model was released on November 28, 2022, and has achieved high scores in benchmarks like HellaSwag (85.5 in a 10-shot scenario) and MMLU (70.0 in a 5-shot scenario).
Mistral 7B Instruct, developed by Mistral, features a large context window of 32000 tokens. The model is priced at 0.025 cents per thousand tokens for both input and output. It was released on September 27, 2023, and achieved a score of 60.1 in the MMLU benchmark under a 5-shot scenario.
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