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Discover how Open AI's GPT-3.5 Turbo and Google's Gemini 1.5 Pro stack up against each other in this comprehensive comparison of two leading AI
language models.
Released in November 2022 and February 2024 respectively, these models represent significant advancements in artificial intelligence,
with GPT-3.5 Turbo offering a 16,385-token context
window and Gemini 1.5 Pro featuring a 1,000,000-token
capacity. Their distinct approaches to natural language processing are reflected in their
benchmark performances, with GPT-3.5 Turbo achieving 70% on MMLU and Gemini 1.5 Pro scoring 81.9%, making this comparison essential
for developers and organizations seeking the right AI solution for their specific needs.
Models Overview
 GPT-3.5 Turbo |  Gemini 1.5 Pro | |
|---|---|---|
Provider Company that developed the model | Open AI | |
Context Length Maximum number of tokens the model can process | 16.39K | 1M |
Maximum Output Maximum number of tokens the model can generate in a single response | 4096 | 8192 |
Release Date Date when the model was released | 28-11-2022 | 15-02-2024 |
Knowledge Cutoff Training data cutoff date | September 2021 | November 2023 |
Open Source Whether the model's code is open-source | FALSE | FALSE |
API Providers API providers that offer access to the model | OpenAI API | Vertex AI |
Pricing Comparison
Compare the pricing of Open AI's GPT-3.5 Turbo and Google's Gemini 1.5 Pro to determine the most cost-effective solution for your AI needs.
| GPT-3.5 Turbo | Gemini 1.5 Pro | |
|---|---|---|
Input Cost Cost per million input tokens | $0.5 / 1M tokens | $7 / 1M tokens |
Output Cost Cost per million tokens generated | $1.5 / 1M tokens | $21 / 1M tokens |
Comparing Benchmarks and Performance
Compare the performances of Open AI's GPT-3.5 Turbo and Google's Gemini 1.5 Pro on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.
| GPT-3.5 Turbo | Gemini 1.5 Pro | |
|---|---|---|
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | 70% | 81.9% |
MMMU A wide ranging multi-discipline and multimodal benchmark. | Benchmark not available | 58.5% |
HellaSwag A challenging sentence completion benchmark. | 85.5% | 93.3% |
GSM8K Grade-school math problems benchmark. | Benchmark not available | 90.8% |
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings. | Benchmark not available | 84.1% |
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines. | 43.1% | 67.7% |
