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Discover how Open AI's GPT-4 Turbo and Anthropic's Claude 3.5 Sonnet stack up against each other in this comprehensive comparison of two leading AI
language models.
Released in November 2023 and June 2024 respectively, these models represent significant advancements in artificial intelligence,
with GPT-4 Turbo offering a 128,000-token context
window and Claude 3.5 Sonnet featuring a 200,000-token
capacity. Their distinct approaches to natural language processing are reflected in their
benchmark performances, with GPT-4 Turbo achieving 86.7%
on MMLU and Claude 3.5 Sonnet scoring 90.4%, making this comparison essential for developers and organizations seeking the right AI
solution for their specific needs.
Models Overview
 GPT-4 Turbo |  Claude 3.5 Sonnet | |
|---|---|---|
Provider Company that developed the model | Open AI | Anthropic |
Context Length Maximum number of tokens the model can process | 128K | 200K |
Maximum Output Maximum number of tokens the model can generate in a single response | 4096 | 8192 |
Release Date Date when the model was released | 06-11-2023 | 20-06-2024 |
Knowledge Cutoff Training data cutoff date | December 2023 | April 2024 |
Open Source Whether the model's code is open-source | FALSE | FALSE |
API Providers API providers that offer access to the model | OpenAI API | Anthropic API, Vertex AI, AWS Bedrock |
Pricing Comparison
Compare the pricing of Open AI's GPT-4 Turbo and Anthropic's Claude 3.5 Sonnet to determine the most cost-effective solution for your AI needs.
| GPT-4 Turbo | Claude 3.5 Sonnet | |
|---|---|---|
Input Cost Cost per million input tokens | $10 / 1M tokens | $3 / 1M tokens |
Output Cost Cost per million tokens generated | $30 / 1M tokens | $15 / 1M tokens |
Comparing Benchmarks and Performance
Compare the performances of Open AI's GPT-4 Turbo and Anthropic's Claude 3.5 Sonnet on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.
| GPT-4 Turbo | Claude 3.5 Sonnet | |
|---|---|---|
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | 86.7% | 90.4% |
MMMU A wide ranging multi-discipline and multimodal benchmark. | 63.1% | 70.4% |
HellaSwag A challenging sentence completion benchmark. | 96% | Benchmark not available |
GSM8K Grade-school math problems benchmark. | 92.95% | 96.4% |
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings. | 87.1% | 93.7% |
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines. | 72.6% | 78.3% |
