Understand and compare
Llama 2 Chat 13B
vs.
Gemini 1.5 Pro
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Podial
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Overview
Llama 2 Chat 13B was released
7 months before
Gemini 1.5 Pro.
Llama 2 Chat 13B
|
Gemini 1.5 Pro
|
|
|---|---|---|
|
Provider
The entity that provides this model.
|
Meta
|
Google
|
|
Input Context Window
The number of tokens supported by the input context window.
|
4,096
tokens
|
1M
tokens
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
2,048
tokens
|
8,192
tokens
|
|
Release Date
When the model was first released.
|
2023-07-18
|
2024-02-15
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
September 2022
|
November 2023
|
|
Open Source
|
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
|
Pricing
|
Llama 2 Chat 13B
|
Gemini 1.5 Pro
|
|
|---|---|---|
|
Input
Cost of input data provided to the model.
|
Pricing not available.
|
$7.00
per million tokens
|
|
Output
Cost of output tokens generated by the model.
|
Pricing not available.
|
$21.00
per million tokens
|
Benchmarks
Compare relevant benchmarks between Llama 2 Chat 13B
and Gemini 1.5 Pro.
|
Llama 2 Chat 13B
|
Gemini 1.5 Pro
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
54.8
|
81.9
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
Benchmark not available.
|
58.5
(0-shot)
|
|
HellaSwag
A challenging sentence completion benchmark.
|
80.7
(10-shot)
|
93.3
(10-shot)
|
|
GSM8K
Grade-school math problems benchmark.
|
28.7
(8-shot)
|
90.8
(11-shot)
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
18.3
(0-shot)
|
84.1
(0-shot)
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
Benchmark not available.
|
67.7
(4-shot Minerva Prompt)
|
Llama 2 Chat 13B, developed by Meta, features a context window of 4096 tokens. The model was released on July 18, 2023, and achieved a score of 54.8 in the MMLU benchmark.
Gemini 1.5 Pro by Google features a vast context window of 1,000,000 tokens. The model is priced at 0.7 cents per thousand tokens for input and 2.1 cents per thousand tokens for output. It was launched on February 15, 2024. In benchmark tests, it achieved a score of 58.5 in MMMU with a 0-shot scenario and 81.9 in MMLU with a 5-shot scenario.
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