Gemini Pro
Our best model for general performance across a wide range of tasks
Natively multimodal, with an updated long context window of up to two million tokens — the longest of any large-scale foundation model.
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Longer context
1.5 Pro introduces a breakthrough context window of up to two million tokens — the longest context window of any large scale foundation model yet. It achieves near-perfect recall on long-context retrieval tasks across modalities, unlocking the ability to accurately process large-scale documents, thousands of lines of code, hours of audio, video, and more.
Pros on Pro
Developers have been putting 1.5 Pro to the test using Google AI Studio and Vertex AI.
Relentless innovation
Our research team is continually exploring new ideas at the frontier of AI, building innovative products that show consistent progress on a range of benchmarks.
Capability |
Benchmark |
Description |
Gemini 1.5 Flash (May 2024) |
Gemini 1.5 Flash (Sep 2024) |
Gemini 1.5 Pro (May 2024) |
Gemini 1.5 Pro (Sep 2024) |
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General MMLU-Pro Enhanced version of popular MMLU dataset with questions across multiple subjects with higher difficulty tasks |
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General |
MMLU-Pro |
Enhanced version of popular MMLU dataset with questions in 57 subjects (incl. STEM, humanities, and others) with higher difficulty tasks |
Gemini 1.5 Flash (May 2024) 59.1% |
Gemini 1.5 Flash (Sep 2024) 67.3% |
Gemini 1.5 Pro (May 2024) 69.0% |
Gemini 1.5 Pro (May 2024) 75.8% |
Code Natural2Code Code generation across Python, Java, C++, JS, Go . Held out dataset HumanEval-like, not leaked on the web |
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Code |
Natural2Code |
Code generation across Python, Java, C++, JS, Go . Held out dataset HumanEval-like, not leaked on the web |
Gemini 1.5 Flash (May 2004) 77.2% |
Gemini 1.5 Flash (Sep 2024) 79.8% |
Gemini 1.5 Pro (May 2024) 82.6% |
Gemini 1.5 Pro (Sep 2024) 85.4% |
Math MATH Challenging math problems (incl. algebra, geometry, pre-calculus, and others) |
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Math |
MATH |
Challenging math problems (incl. algebra, geometry, pre-calculus, and others) |
Gemini 1.5 Flash (May 2004) 54.9% |
Gemini 1.5 Flash (Sep 2024) 77.9% |
Gemini 1.5 Pro (May 2024) 67.7% |
Gemini 1.5 Pro 86.5% |
HiddenMath Competition-level math problems, Held out dataset AIME/AMC-like, crafted by experts and not leaked on the web |
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HiddenMath |
Competition-level math problems, Held out dataset AIME/AMC-like, crafted by experts and not leaked on the web |
Gemini 1.5 Flash (May 2004) 20.3% |
Gemini 1.5 Flash (Sep 2024) 47.2% |
Gemini 1.5 Pro (May 2024) 28.0% |
Gemini 1.5 Pro 52.0% |
|
Reasoning GPQA (diamond) Challenging dataset of questions written by domain experts in biology, physics, and chemistry |
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Reasoning |
GPQA (diamond) |
Challenging dataset of questions written by domain experts in biology, physics, and chemistry |
Gemini 1.5 Flash (May 2024) 41.4% |
Gemini 1.5 Flash (Sep 2024) 51.0% |
Gemini 1.5 Pro (May 2024) 46.0% |
Gemini 1.5 Pro (Sep 2024) 59.1% |
Multilingual WMT23 Language translation |
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Multilingual |
WMT23 |
Language translation |
Gemini 1.5 Flash (May 2024) 74.1 |
Gemini 1.5 Flash (Sep 2024) 73.9 |
Gemini 1.5 Pro (May 2024) 75.3 |
Gemini 1.5 Pro (Sep 2024) 75.1 |
Long Context RULER (at 1M) Diagnostic suite checking long-context ability of the models over a range of tasks |
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Long Context |
RULER (at 1M) |
Diagnostic suite checking long-context ability of the models over a range of tasks |
Gemini 1.5 Flash (May 2024) 69.6% |
Gemini 1.5 Flash (Sep 2024) 82.3% |
Gemini 1.5 Pro (May 2024) 40.1% |
Gemini 1.5 Pro (Sep 2024) 86.4% |
MRCR (1M) Diagnostic long-context understanding evaluation |
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MRCR (1M) |
Diagnostic long-context understanding evaluation |
Gemini 1.5 Flash (May 2024) 70.1% |
Gemini 1.5 Flash (Sep 2024) 71.9% |
Gemini 1.5 Pro (May 2024) 70.5% |
Gemini 1.5 Pro (Sep 2024) 82.6% |
|
Image MMMU Multi-discipline college-level reasoning problems |
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Image |
MMMU |
Multi-discipline college-level reasoning problems |
Gemini 1.5 Flash (May 2024) 56.1% |
Gemini 1.5 Flash (Sep 2024) 62.3% |
Gemini 1.5 Pro (May 2024) 62.2% |
Gemini 1.5 Pro (Sep 2024) 65.9% |
Vibe-Eval (Reka) Visual understanding in chat models with challenging everyday examples. Evaluated with a Gemini Flash model as a rater |
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Vibe-Eval (Reka) |
Visual understanding in chat models with challenging everyday examples. Evaluated with a Gemini Flash model as a rater |
Gemini 1.5 Flash (May 2024) 44.8% |
Gemini 1.5 Flash (Sep 2024) 48.9% |
Gemini 1.5 Pro (May 2024) 48.9% |
Gemini 1.5 Pro (Sep 2024) 53.9% |
|
Image MathVista Mathematical reasoning in visual contexts |
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MathVista |
Mathematical reasoning in visual contexts |
Gemini 1.5 Flash (May 2024) 58.4% |
Gemini 1.5 Flash (Sep 2024) 65.8% |
Gemini 1.5 Pro (May 2024) 63.9% |
Gemini 1.5 Pro (Sep 2024) 68.1% |
|
Audio FLEURS (55 languages) Automatic speech recognition (based on word error rate, lower is better) |
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Audio |
FLEURS (55 languages) |
Automatic speech recognition (based on word error rate, lower is better) |
Gemini 1.5 Flash (May 2024) 9.8% |
Gemini 1.5 Flash (Sep 2024) 9.6% |
Gemini 1.5 Pro (May 2024) 6.5% |
Gemini 1.5 Pro (May 2024) 6.7% |
Video Video-MME Video analysis across multiple domains |
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Video |
Video-MME |
Video analysis across multiple domains |
Gemini 1.5 Flash (May 2024) 74.7% |
Gemini 1.5 Flash (Sep 2024) 76.1% |
Gemini 1.5 Pro (May 2024) 77.9% |
Gemini 1.5 Pro (May 2024) 78.6% |
Safety XSTest Measures how often models refuse to respond to safe/benign prompts. The score represents how frequently models correctly fulfill requests |
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Safety |
XSTest |
Measures how often models refuse to respond to safe/benign prompts. The score represents how frequently models correctly fulfill requests |
Gemini 1.5 Flash (May 2024) 86.9% |
Gemini 1.5 Flash (Sep 2024) 97.0% |
Gemini 1.5 Pro (May 2024) 88.4% |
Gemini 1.5 Pro (May 2024) 98.8% |
Try Gemini 1.5 Pro
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