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Discover how Google's Gemini Ultra and Google's Gemma 2 9B stack up against each other in this comprehensive comparison of two leading AI language models.

Released in December 2023 and June 2024 respectively, these models represent significant advancements in artificial intelligence, with Gemini Ultra offering a 32,800-token context window and Gemma 2 9B featuring a 8,192-token capacity. Their distinct approaches to natural language processing are reflected in their benchmark performances, with Gemini Ultra achieving 83.7% on MMLU and Gemma 2 9B scoring 71.3%, making this comparison essential for developers and organizations seeking the right AI solution for their specific needs.

Models Overview

Google Gemini Ultra
Google Gemma 2 9B

Provider

Company that developed the model
Google Google

Context Length

Maximum number of tokens the model can process
32.8K undefined

Maximum Output

Maximum number of tokens the model can generate in a single response
8192 Unknown

Release Date

Date when the model was released
06-12-2023 27-06-2024

Knowledge Cutoff

Training data cutoff date
Unknown Unknown

Open Source

Whether the model's code is open-source
FALSE TRUE

API Providers

API providers that offer access to the model
Vertex AI Hugging Face, Vertex AI

Pricing Comparison

Compare the pricing of Google's Gemini Ultra and Google's Gemma 2 9B to determine the most cost-effective solution for your AI needs.

Google Gemini Ultra
Google Gemma 2 9B

Input Cost

Cost per million input tokens
Pricing not available Pricing not available

Output Cost

Cost per million tokens generated
Pricing not available Pricing not available

Comparing Benchmarks and Performance

Compare the performances of Google's Gemini Ultra and Google's Gemma 2 9B on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Google Gemini Ultra
Google Gemma 2 9B

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
83.7% 71.3%

MMMU

A wide ranging multi-discipline and multimodal benchmark.
59.4% Benchmark not available

HellaSwag

A challenging sentence completion benchmark.
Benchmark not available 81.9%

GSM8K

Grade-school math problems benchmark.
88.9% 68.6%

HumanEval

A benchmark to measure functional correctness for synthesizing programs from docstrings.
74.4% 40.2%

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
53.2% 36.6%

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