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Discover how Google's Gemma 2 27B and Google's Gemma 2 2B stack up against each other in this comprehensive comparison of two leading AI language models.

Released in June 2024 and June 2024 respectively, these models represent significant advancements in artificial intelligence, with Gemma 2 27B offering a 8,192-token context window and Gemma 2 2B featuring a 8,192-token capacity. Their distinct approaches to natural language processing are reflected in their benchmark performances, with Gemma 2 27B achieving 75.2% on MMLU and Gemma 2 2B scoring 51.3%, making this comparison essential for developers and organizations seeking the right AI solution for their specific needs.

Models Overview

Google Gemma 2 27B
Google Gemma 2 2B

Provider

Company that developed the model
Google Google

Context Length

Maximum number of tokens the model can process
undefined undefined

Maximum Output

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

Release Date

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

Knowledge Cutoff

Training data cutoff date
Unknown Unknown

Open Source

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

API Providers

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

Pricing Comparison

Compare the pricing of Google's Gemma 2 27B and Google's Gemma 2 2B to determine the most cost-effective solution for your AI needs.

Google Gemma 2 27B
Google Gemma 2 2B

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 Gemma 2 27B and Google's Gemma 2 2B on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Google Gemma 2 27B
Google Gemma 2 2B

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
75.2% 51.3%

MMMU

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

HellaSwag

A challenging sentence completion benchmark.
86.4% 73%

GSM8K

Grade-school math problems benchmark.
74% 23.9%

HumanEval

A benchmark to measure functional correctness for synthesizing programs from docstrings.
51.8% 17.7%

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
42.3% 15%

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