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Discover how Anthropic's Claude 3.7 Sonnet and Google's Gemma 2 27B stack up against each other in this comprehensive comparison of two leading AI language models.

Released in February 2025 and June 2024 respectively, these models represent significant advancements in artificial intelligence, with Claude 3.7 Sonnet offering a 200,000-token context window and Gemma 2 27B featuring a 8,192-token capacity. Their distinct approaches to natural language processing are reflected in their benchmark performances, with Claude 3.7 Sonnet achieving null% on MMLU and Gemma 2 27B scoring 75.2%, making this comparison essential for developers and organizations seeking the right AI solution for their specific needs.

Models Overview

Anthropic Claude 3.7 Sonnet
Anthropic Gemma 2 27B

Provider

Company that developed the model
Anthropic Google

Context Length

Maximum number of tokens the model can process
200K 8192

Maximum Output

Maximum number of tokens the model can generate in a single response
64K Unknown

Release Date

Date when the model was released
25-02-2025 27-06-2024

Knowledge Cutoff

Training data cutoff date
October 2024 Unknown

Open Source

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

API Providers

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

Pricing Comparison

Compare the pricing of Anthropic's Claude 3.7 Sonnet and Google's Gemma 2 27B to determine the most cost-effective solution for your AI needs.

Anthropic Claude 3.7 Sonnet
Anthropic Gemma 2 27B

Input Cost

Cost per million input tokens
$3 / 1M tokens Pricing not available

Output Cost

Cost per million tokens generated
$15 / 1M tokens Pricing not available

Comparing Benchmarks and Performance

Compare the performances of Anthropic's Claude 3.7 Sonnet and Google's Gemma 2 27B on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Anthropic Claude 3.7 Sonnet
Anthropic Gemma 2 27B

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
Benchmark not available 75.2%

MMMU

A wide ranging multi-discipline and multimodal benchmark.
75% Benchmark not available

HellaSwag

A challenging sentence completion benchmark.
Benchmark not available 86.4%

GSM8K

Grade-school math problems benchmark.
Benchmark not available 74%

HumanEval

A benchmark to measure functional correctness for synthesizing programs from docstrings.
Benchmark not available 51.8%

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
96.2% 42.3%

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