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Discover how Anthropic's Claude 3.5 Haiku and Anthropic's Claude 3 Opus stack up against each other in this comprehensive comparison of two leading AI language models.

Released in October 2024 and March 2024 respectively, these models represent significant advancements in artificial intelligence, with Claude 3.5 Haiku offering a 200,000-token context window and Claude 3 Opus featuring a 200,000-token capacity. Their distinct approaches to natural language processing are reflected in their benchmark performances, with Claude 3.5 Haiku achieving null% on MMLU and Claude 3 Opus scoring 88.2%, making this comparison essential for developers and organizations seeking the right AI solution for their specific needs.

Models Overview

Anthropic Claude 3.5 Haiku
Anthropic Claude 3 Opus

Provider

Company that developed the model
Anthropic Anthropic

Context Length

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

Maximum Output

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

Release Date

Date when the model was released
22-10-2024 04-03-2024

Knowledge Cutoff

Training data cutoff date
April 2024 August 2023

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 Anthropic API, Vertex AI, AWS Bedrock

Pricing Comparison

Compare the pricing of Anthropic's Claude 3.5 Haiku and Anthropic's Claude 3 Opus to determine the most cost-effective solution for your AI needs.

Anthropic Claude 3.5 Haiku
Anthropic Claude 3 Opus

Input Cost

Cost per million input tokens
$0.25 / 1M tokens $15 / 1M tokens

Output Cost

Cost per million tokens generated
$1.25 / 1M tokens $75 / 1M tokens

Comparing Benchmarks and Performance

Compare the performances of Anthropic's Claude 3.5 Haiku and Anthropic's Claude 3 Opus on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.

Anthropic Claude 3.5 Haiku
Anthropic Claude 3 Opus

MMLU

Evaluating LLM knowledge acquisition in zero-shot and few-shot settings.
Benchmark not available 88.2%

MMMU

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

HellaSwag

A challenging sentence completion benchmark.
Benchmark not available 95.4%

GSM8K

Grade-school math problems benchmark.
Benchmark not available 95%

HumanEval

A benchmark to measure functional correctness for synthesizing programs from docstrings.
88.1% 84.9%

MATH

Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines.
69.4% 60.1%

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