Compare to
Discover how DeepSeek's DeepSeek R1 and Google's Gemini Pro stack up against each other in this comprehensive comparison of two leading AI
language models.
Released in January 2024 and December 2023 respectively, these models represent significant advancements in artificial intelligence,
with DeepSeek R1 offering a 64,000-token context
window and Gemini Pro featuring a 32,800-token
capacity. Their distinct approaches to natural language processing are reflected in their
benchmark performances, with DeepSeek R1 achieving 90.8% on MMLU and Gemini Pro scoring 71.8%, making this comparison essential
for developers and organizations seeking the right AI solution for their specific needs.
Models Overview
 DeepSeek R1 |  Gemini Pro | |
|---|---|---|
Provider Company that developed the model | DeepSeek | |
Context Length Maximum number of tokens the model can process | 64K | 32.8K |
Maximum Output Maximum number of tokens the model can generate in a single response | 8192 | 8192 |
Release Date Date when the model was released | 20-01-2024 | 13-12-2023 |
Knowledge Cutoff Training data cutoff date | July 2024 | Unknown |
Open Source Whether the model's code is open-source | TRUE | FALSE |
API Providers API providers that offer access to the model | DeepSeek, Fireworks AI, Hyperbolic | Vertex AI |
Pricing Comparison
Compare the pricing of DeepSeek's DeepSeek R1 and Google's Gemini Pro to determine the most cost-effective solution for your AI needs.
| DeepSeek R1 | Gemini Pro | |
|---|---|---|
Input Cost Cost per million input tokens | $0.55 / 1M tokens | Pricing not available |
Output Cost Cost per million tokens generated | $2.19 / 1M tokens | Pricing not available |
Comparing Benchmarks and Performance
Compare the performances of DeepSeek's DeepSeek R1 and Google's Gemini Pro on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.
| DeepSeek R1 | Gemini Pro | |
|---|---|---|
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | 90.8% | 71.8% |
MMMU A wide ranging multi-discipline and multimodal benchmark. | Benchmark not available | 47.9% |
HellaSwag A challenging sentence completion benchmark. | Benchmark not available | 84.7% |
GSM8K Grade-school math problems benchmark. | Benchmark not available | 77.9% |
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings. | Benchmark not available | 67.7% |
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines. | 97.3% | 32.6% |
