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Discover how Google's Gemini Flash and Google's Gemini Flash 2.0 stack up against each other in this comprehensive comparison of two leading AI
language models.
Released in May 2023 and February 2025 respectively, these models represent significant advancements in artificial intelligence,
with Gemini Flash offering a 1,000,000-token context
window and Gemini Flash 2.0 featuring a 1,000,000-token
capacity. Their distinct approaches to natural language processing are reflected in their
benchmark performances, with Gemini Flash achieving 78.9%
on MMLU and Gemini Flash 2.0 scoring 76.4%, making this comparison essential for developers and organizations seeking the right AI
solution for their specific needs.
Models Overview
 Gemini Flash | Gemini Flash 2.0 | |
|---|---|---|
Provider Company that developed the model | ||
Context Length Maximum number of tokens the model can process | 1M | 1M |
Maximum Output Maximum number of tokens the model can generate in a single response | 8192 | 8192 |
Release Date Date when the model was released | 14-05-2023 | 05-02-2025 |
Knowledge Cutoff Training data cutoff date | November 2023 | June 2024 |
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 and Google's Gemini Flash 2.0 to determine the most cost-effective solution for your AI needs.
| Gemini Flash | Gemini Flash 2.0 | |
|---|---|---|
Input Cost Cost per million input tokens | $0.13 / 1M tokens | $0.1 / 1M tokens |
Output Cost Cost per million tokens generated | $0.38 / 1M tokens | $0.4 / 1M tokens |
Comparing Benchmarks and Performance
Compare the performances of Google's Gemini Flash and Google's Gemini Flash 2.0 on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.
| Gemini Flash | Gemini Flash 2.0 | |
|---|---|---|
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | 78.9% | 76.4% |
MMMU A wide ranging multi-discipline and multimodal benchmark. | 56.1% | 71.7% |
HellaSwag A challenging sentence completion benchmark. | 86.5% | Benchmark not available |
GSM8K Grade-school math problems benchmark. | 86.2% | Benchmark not available |
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings. | 74.3% | Benchmark not available |
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines. | 54.9% | Benchmark not available |
