Understand and compare
Claude 3 Haiku
vs.
Mistral Large
Overview
Claude 3 Haiku was released
16 days after
Mistral Large.
Claude 3 Haiku
|
Mistral Large
|
|
---|---|---|
Provider
The entity that provides this model.
|
Anthropic
|
Mistral
|
Input Context Window
The number of tokens supported by the input context window.
|
200K
tokens
|
32K
tokens
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
4,096
tokens
|
4,096
tokens
|
Release Date
When the model was first released.
|
2024-03-13
|
2024-02-26
|
Pricing
Claude 3 Haiku is
roughly 32x cheaper compared
to Mistral Large for input tokens and
roughly 6.4x cheaper
for output tokens.
Claude 3 Haiku
|
Mistral Large
|
|
---|---|---|
Input
Cost of input data provided to the model.
|
$0.25
per million tokens
|
$8.00
per million tokens
|
Output
Cost of output tokens generated by the model.
|
$1.25
per million tokens
|
$8.00
per million tokens
|
Benchmarks
Compare relevant benchmarks between Claude 3 Haiku
and Mistral Large.
Claude 3 Haiku
|
Mistral Large
|
|
---|---|---|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
76.7
(5-shot CoT)
|
81.2
(5-shot)
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
50.2
|
Benchmark not available.
|
HellaSwag
A challenging sentence completion benchmark.
|
85.9
(10-shot)
|
89.2
(10-shot)
|
Claude 3 Haiku, developed by Anthropic, features a context window of 200,000 tokens. The model costs 0.025 cents per thousand tokens for input and 0.125 cents per thousand tokens for output. It was released on March 13, 2024. In benchmarks, it achieved a score of 50.2 in MMMU, 85.9 in HellaSwag in a 10-shot scenario, and 76.7 in MMLU in a 5-shot CoT 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).
Measure & Improve LLM
Product Performance.
Get Started