Understand and compare
GPT-4 Turbo 1106
vs.
Llama 2 Chat 70B
Try
Shepherd
Make it trivial for customers to self-host your SaaS.
Overview
GPT-4 Turbo 1106 was released
4 months after
Llama 2 Chat 70B.
![]() |
![]() |
|
---|---|---|
Provider
The entity that provides this model.
|
![]() |
![]() |
Input Context Window
The number of tokens supported by the input context window.
|
128K
tokens
|
4,096
tokens
|
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
|
4,096
tokens
|
2,048
tokens
|
Release Date
When the model was first released.
|
2023-11-06
|
2023-07-18
|
Knowledge Cutoff
Limit on the knowledge base used by the model.
|
April 2023
|
September 2022
|
Open Source
|
|
|
API Providers
The providers that offer this model. (This is not an exhaustive list.)
|
|
|
Pricing
![]() |
![]() |
|
---|---|---|
Input
Cost of input data provided to the model.
|
$10.00
per million tokens
|
Pricing not available.
|
Output
Cost of output tokens generated by the model.
|
$30.00
per million tokens
|
Pricing not available.
|
Benchmarks
Compare relevant benchmarks between GPT-4 Turbo 1106
and Llama 2 Chat 70B.
![]() |
![]() |
|
---|---|---|
MMLU
Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
|
84.7
(5-shot)
|
68.9
|
MMMU
A wide ranging multi-discipline and multimodal benchmark.
|
Benchmark not available.
|
30.1
|
HellaSwag
A challenging sentence completion benchmark.
|
Benchmark not available.
|
85.3
(0-shot)
|
GSM8K
Grade-school math problems benchmark.
|
Benchmark not available.
|
56.8
(8-shot)
|
HumanEval
A benchmark to measure functional correctness for synthesizing programs from docstrings.
|
83.7
(0-shot)
|
29.9
(0-shot)
|
MATH
Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
|
64.3
(0-shot)
|
Benchmark not available.
|

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.

Measure & Improve LLM
Product Performance.
Get Started