Understand and compare
Llama 2 Chat 70B
vs.
Gemini 1.5 Pro
Try
Shepherd
Make it trivial for customers to self-host your SaaS.
Overview
Llama 2 Chat 70B was released
7 months before
Gemini 1.5 Pro.
Llama 2 Chat 70B
|
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 70B
|
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 70B
and Gemini 1.5 Pro.
|
Llama 2 Chat 70B
|
Gemini 1.5 Pro
|
|
|---|---|---|
|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
68.9
|
81.9
(5-shot)
|
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
30.1
|
58.5
(0-shot)
|
|
HellaSwag
A challenging sentence completion benchmark.
|
85.3
(0-shot)
|
93.3
(10-shot)
|
|
GSM8K
Grade-school math problems benchmark.
|
56.8
(8-shot)
|
90.8
(11-shot)
|
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
29.9
(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 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 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.
GPT-4 Turbo 2024-04-09
Claude 3 Haiku
Measure & Improve LLM
Product Performance.
Get Started
© 2023-2024 Context