Understand and compare
Llama 2 Chat 70B
vs.
Gemini Ultra
Try
Podial
Turn your documents into engaging podcast discussions.
Overview
Llama 2 Chat 70B
|
Gemini Ultra
|
|
|---|---|---|
|
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
|
32.8K
characters
|
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
2,048
tokens
|
8,192
characters
|
|
Release Date
When the model was first released.
|
2023-07-18
|
Unknown
|
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
September 2022
|
Unknown
|
|
Open Source
|
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
|
Pricing
|
Llama 2 Chat 70B
|
Gemini Ultra
|
|
|---|---|---|
|
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 Llama 2 Chat 70B
and Gemini Ultra.
|
Llama 2 Chat 70B
|
Gemini Ultra
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
68.9
|
83.7
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
30.1
|
59.4
(0-shot pass@1)
|
|
HellaSwag
A challenging sentence completion benchmark.
|
85.3
(0-shot)
|
Benchmark not available.
|
|
GSM8K
Grade-school math problems benchmark.
|
56.8
(8-shot)
|
88.9
(11-shot)
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
29.9
(0-shot)
|
74.4
(0-shot)
|
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
Benchmark not available.
|
53.2
(4-shot Minerva Prompt)
|
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.
Gemini Ultra, developed by Google, features a large context window of 32768 tokens. The model has excelled in benchmarks like MMMU with a score of 59.4 in a 0-shot pass@1 scenario and MMLU with a score of 83.7 in a 5-shot scenario.
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