Compare Llama 3.1 70B Instruct vs. Gemini Ultra

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Overview

	Llama 3.1 70B Instruct	Gemini Ultra
Provider The entity that provides this model.	Meta	Google
Input Context Window The number of tokens supported by the input context window.	128K 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.	July 23rd, 2024 4 months ago 2024-07-23	Unknown
Knowledge Cutoff Limit on the knowledge base used by the model.	December 2023 December 2023	Unknown
Open Source	Yes	No
API Providers The providers that offer this model. (This is not an exhaustive list.)	Azure AI, AWS Bedrock, Google Cloud Vertex AI Model Garden, NVIDIA NIM, IBM watsonx	Google Cloud Vertex AI Model Garden

Pricing

	Llama 3.1 70B Instruct	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 3.1 70B Instruct and Gemini Ultra.

	Llama 3.1 70B Instruct	Gemini Ultra
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.	83.6 (5-shot) Source	83.7 (5-shot) Source
MMMU A wide ranging multi-discipline and multimodal benchmark.	Benchmark not available.	59.4 (0-shot pass@1) Source
HellaSwag A challenging sentence completion benchmark.	Benchmark not available.	Benchmark not available.
GSM8K Grade-school math problems benchmark.	95.1 (8-shot) Source	88.9 (11-shot) Source
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings.	Benchmark not available.	74.4 (0-shot) Source
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.	68.0 (0-shot) Source	53.2 (4-shot Minerva Prompt) Source

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