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Discover how Anthropic's Claude 3.5 Sonnet and Google's Gemini Ultra stack up against each other in this comprehensive comparison of two leading AI language models.

Released in June 2024 and December 2023 respectively, these models represent significant advancements in artificial intelligence, with Claude 3.5 Sonnet offering a 200,000-token context window and Gemini Ultra featuring a 32,800-token capacity. Their distinct approaches to natural language processing are reflected in their benchmark performances, with Claude 3.5 Sonnet achieving 90.4% on MMLU and Gemini Ultra scoring 83.7%, making this comparison essential for developers and organizations seeking the right AI solution for their specific needs.

Models Overview

Anthropic Claude 3.5 Sonnet
Anthropic Gemini Ultra

Provider

Company that developed the model
Anthropic Google

Context Length

Maximum number of tokens the model can process
200K 32.8K

Maximum Output

Maximum number of tokens the model can generate in a single response
4096 8192

Release Date

Date when the model was released
20-06-2024 06-12-2023

Knowledge Cutoff

Training data cutoff date
April 2024 Unknown

Open Source

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

API Providers

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

Pricing Comparison

Compare the pricing of Anthropic's Claude 3.5 Sonnet and Google's Gemini Ultra to determine the most cost-effective solution for your AI needs.

Anthropic Claude 3.5 Sonnet
Anthropic Gemini Ultra

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 3.5 Sonnet and Google's Gemini Ultra on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Anthropic Claude 3.5 Sonnet
Anthropic Gemini Ultra

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
90.4% 83.7%

MMMU

A wide ranging multi-discipline and multimodal benchmark.
70.4% 59.4%

HellaSwag

A challenging sentence completion benchmark.
Benchmark not available Benchmark not available

GSM8K

Grade-school math problems benchmark.
96.4% 88.9%

HumanEval

A benchmark to measure functional correctness for synthesizing programs from docstrings.
93.7% 74.4%

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
78.3% 53.2%

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