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Discover how Google's Gemini Flash 2.5 and Google's Gemini Ultra stack up against each other in this comprehensive comparison of two leading AI
language models.
Released in April 2025 and December 2023 respectively, these models represent significant advancements in artificial intelligence,
with Gemini Flash 2.5 offering a 1,000,000-token context
window and Gemini Ultra featuring a 32,800-token
capacity. Their distinct approaches to natural language processing are reflected in their
benchmark performances, with Gemini Flash 2.5 achieving null% on MMLU and Gemini Ultra scoring 83.7%, making this comparison essential
for developers and organizations seeking the right AI solution for their specific needs.
Models Overview
 Gemini Flash 2.5 | Gemini Ultra | |
|---|---|---|
Provider Company that developed the model | ||
Context Length Maximum number of tokens the model can process | 1M | 32.8K |
Maximum Output Maximum number of tokens the model can generate in a single response | 64K | 8192 |
Release Date Date when the model was released | 17-04-2025 | 06-12-2023 |
Knowledge Cutoff Training data cutoff date | January 2025 | Unknown |
Open Source Whether the model's code is open-source | FALSE | FALSE |
API Providers API providers that offer access to the model | Vertex AI | Vertex AI |
Pricing Comparison
Compare the pricing of Google's Gemini Flash 2.5 and Google's Gemini Ultra to determine the most cost-effective solution for your AI needs.
| Gemini Flash 2.5 | Gemini Ultra | |
|---|---|---|
Input Cost Cost per million input tokens | $0.15 / 1M tokens | Pricing not available |
Output Cost Cost per million tokens generated | $0.6 / 1M tokens | Pricing not available |
Comparing Benchmarks and Performance
Compare the performances of Google's Gemini Flash 2.5 and Google's Gemini Ultra on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.
| Gemini Flash 2.5 | Gemini Ultra | |
|---|---|---|
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | Benchmark not available | 83.7% |
MMMU A wide ranging multi-discipline and multimodal benchmark. | 76.7% | 59.4% |
HellaSwag A challenging sentence completion benchmark. | Benchmark not available | Benchmark not available |
GSM8K Grade-school math problems benchmark. | Benchmark not available | 88.9% |
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings. | Benchmark not available | 74.4% |
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines. | Benchmark not available | 53.2% |
