Compare to

Discover how Google's Gemini 1.5 Pro and Anthropic's Claude 2.1 stack up against each other in this comprehensive comparison of two leading AI language models.

Released in February 2024 and November 2024 respectively, these models represent significant advancements in artificial intelligence, with Gemini 1.5 Pro offering a 1,000,000-token context window and Claude 2.1 featuring a 200,000-token capacity. Their distinct approaches to natural language processing are reflected in their benchmark performances, with Gemini 1.5 Pro achieving 81.9% on MMLU and Claude 2.1 scoring Unknown%, making this comparison essential for developers and organizations seeking the right AI solution for their specific needs.

Models Overview

Google Gemini 1.5 Pro
Google Claude 2.1

Provider

Company that developed the model
Google Anthropic

Context Length

Maximum number of tokens the model can process
1M 200K

Maximum Output

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

Release Date

Date when the model was released
15-02-2024 23-11-2024

Knowledge Cutoff

Training data cutoff date
November 2023 Early 2023

Open Source

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

API Providers

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

Pricing Comparison

Compare the pricing of Google's Gemini 1.5 Pro and Anthropic's Claude 2.1 to determine the most cost-effective solution for your AI needs.

Google Gemini 1.5 Pro
Google Claude 2.1

Input Cost

Cost per million input tokens
$7 / 1M tokens $8 / 1M tokens

Output Cost

Cost per million tokens generated
$21 / 1M tokens $24 / 1M tokens

Comparing Benchmarks and Performance

Compare the performances of Google's Gemini 1.5 Pro and Anthropic's Claude 2.1 on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Google Gemini 1.5 Pro
Google Claude 2.1

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
81.9% Benchmark not available

MMMU

A wide ranging multi-discipline and multimodal benchmark.
58.5% Benchmark not available

HellaSwag

A challenging sentence completion benchmark.
93.3% Benchmark not available

GSM8K

Grade-school math problems benchmark.
90.8% Benchmark not available

HumanEval

A benchmark to measure functional correctness for synthesizing programs from docstrings.
84.1% Benchmark not available

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
67.7% Benchmark not available

Compare More Models