Understand and compare
Gemini Pro
vs.
Llama 2 Chat 70B
Try
Podial
Turn your documents into engaging podcast discussions.
Overview
Gemini Pro was released
5 months after
Llama 2 Chat 70B.
Gemini Pro
|
Llama 2 Chat 70B
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
Google
|
Meta
|
|
Input Context Window
The number of tokens supported by the input context window.
|
32.8K
characters
|
4,096
tokens
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
8,192
characters
|
2,048
tokens
|
|
Release Date
When the model was first released.
|
2023-12-13
|
2023-07-18
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
Unknown
|
September 2022
|
|
Open Source
|
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
|
Pricing
|
Gemini Pro
|
Llama 2 Chat 70B
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
Pricing not available.
|
Pricing not available.
|
|
Output
Cost of output tokens generated by the model.
|
Pricing not available.
|
Pricing not available.
|
Benchmarks
Compare relevant benchmarks between Gemini Pro
and Llama 2 Chat 70B.
|
Gemini Pro
|
Llama 2 Chat 70B
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
71.8
(5-shot)
|
68.9
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
47.9
(pass@1)
|
30.1
|
|
HellaSwag
A challenging sentence completion benchmark.
|
84.7
(10-shot)
|
85.3
(0-shot)
|
|
GSM8K
Grade-school math problems benchmark.
|
77.9
(11-shot)
|
56.8
(8-shot)
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
67.7
(0-shot)
|
29.9
(0-shot)
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
32.6
(4-shot Minerva Prompt)
|
Benchmark not available.
|
Gemini Pro, developed by Google, features a context window of 32768 tokens. The model costs 0.0125 cents per thousand tokens for input and 0.0375 cents per thousand tokens for output. It was released on December 13, 2023, and has achieved a score of 47.9 in the MMMU benchmark with a "pass@1" caveat and a score of 71.8 in the MMLU benchmark in a 5-shot scenario.
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.
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