Gemini

Our most intelligent AI models

A dark, abstract background with soft, vertical streaks of deep blue light and very small white dots, evoking a subtle night sky or faint aurora effect.

Gemini Image

Generate, transform and edit images with simple text prompts, or combine multiple images to create something new. All in Gemini.

Try Gemini Image in Gemini

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AI-generated image of: Fashion portrait with a person in a voluminous red and white patterned garment and wide-brimmed hat, set against a solid royal blue background. The pattern appears to be stylized origami cranes, and the person wears blue lipstick.

Gemini 2.5 Deep Think

An enhanced reasoning mode that uses cutting edge research techniques in parallel thinking and reinforcement learning to significantly improve Gemini’s ability to solve complex problems.

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Gemini 2.5 models are capable of reasoning through their thoughts before responding, resulting in enhanced performance and improved accuracy.

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Model family

Gemini 2.5 builds on the best of Gemini — with native multimodality and a long context window.

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2.5 Pro

Best for coding and highly complex tasks

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2.5 Flash

Best for fast performance on everyday tasks

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2.5 Flash Image

Best for image generation and editing

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2.5 Flash-Lite

Best for high volume, cost efficient tasks

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Create and edit images with Gemini 2.5 Flash Image

Generate, transform and edit images with simple text prompts, or combine multiple images to create something new. All in Gemini.

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AI-generated image of: High fashion image of a woman in a voluminous red feathered dress standing in a glacial lake with snowy peaks.

AI-generated image of: Colourful architectural image featuring sharp planes of red, green, and yellow, with a person in a yellow suit.

AI-generated image of: Five instant Polaroid photos of the same woman in different 1980s fashion styles and hairstyles.

AI-generated image of: Close-up of a happy Golden Retriever with its tongue out and ears flapping, sticking its head out a car window.

Hands-on with Gemini 2.5

See how Gemini 2.5 uses its reasoning capabilities to create interactive simulations and do advanced coding.

Code a fractal visualization

See how Gemini 2.5 Pro creates a simulation of intricate fractal patterns to explore a Mandelbrot set.

Make an interactive animation

See how Gemini 2.5 Pro uses its reasoning capabilities to create an interactive animation of “cosmic fish” with a simple prompt.

Create your own dinosaur game

Watch Gemini 2.5 Pro create an endless runner game, using executable code from a single line prompt.

Plot interactive economic data

Watch Gemini 2.5 Pro use its reasoning capabilities to create an interactive bubble chart to visualize economic and health indicators over time.

Animate complex behavior

See how Gemini 2.5 Pro creates an interactive Javascript animation of colorful boids inside a spinning hexagon.

Code particle simulations

Watch Gemini 2.5 Pro use its reasoning capabilities to create an interactive simulation of a reflection nebula.

Adaptive and budgeted thinking

Adaptive controls and adjustable thinking budgets allow you to balance performance and cost.

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Calibrated

The model explores diverse thinking strategies, leading to more accurate and relevant outputs.

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Controllable

Developers have fine-grained control over the model's thinking process, allowing them to manage resource usage.

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Adaptive

When no thinking budget is set, the model assesses the complexity of a task and calibrates the amount of thinking accordingly.

Gemini 2.5 Deep Think

An enhanced reasoning mode that uses cutting edge research techniques.

In parallel thinking and reinforcement learning to significantly improve Gemini’s ability to solve complex problems.

Deep Think can better help tackle problems that require creativity, strategic planning, and making improvements step-by-step.

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Iterative development and design

We’ve seen impressive results on tasks that require building something by making small changes over time.

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Aiding scientific and mathematical discovery

By reasoning through complex problems, Deep Think can act as a powerful tool for researchers.

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Algorithmic development and code

Deep Think excels at tough coding problems where problem formulation and careful consideration of tradeoffs and time complexity is paramount.

Performance

Gemini 2.5 is state-of-the-art across a wide range of benchmarks.

In addition to its strong performance on academic benchmarks, Gemini 2.5 tops the popular coding leaderboard WebDev Arena.

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Benchmark	Notes	Gemini 2.5 Flash-Lite Non-thinking	Gemini 2.5 Flash-Lite Thinking	Gemini 2.5 Flash Non-thinking	Gemini 2.5 Flash Thinking	Gemini 2.5 Pro Thinking
Input price	$/1M tokens (no caching)	$0.10	$0.10	$0.30	$0.30	$1.25 $2.50 > 200k tokens
Output price	$/1M tokens	$0.40	$0.40	$2.50	$2.50	$10.00 $15.00 > 200k tokens
Reasoning & knowledge Humanity's Last Exam (no tools)		5.1%	6.9%	8.4%	11.0%	21.6%
Science GPQA diamond		64.6%	66.7%	78.3%	82.8%	86.4%
Mathematics AIME 2025		49.8%	63.1%	61.6%	72.0%	88.0%
Code generation LiveCodeBench (UI: 1/1/2025-5/1/2025)		33.7%	34.3%	41.1%	55.4%	69.0%
Code editing Aider Polyglot		26.7%	27.1%	44.0%	56.7%	82.2%
Agentic coding SWE-bench Verified	single attempt	31.6%	27.6%	50.0%	48.9%	59.6%
Agentic coding SWE-bench Verified	multiple attempt	42.6%	44.9%	60.0%	60.3%	67.2%
Factuality SimpleQA		10.7%	13.0%	25.8%	26.9%	54.0%
Factuality FACTS grounding		84.1%	86.8%	83.4%	85.3%	87.8%
Visual reasoning MMMU		72.9%	72.9%	76.9%	79.7%	82.0%
Image understanding Vibe-Eval (Reka)		51.3%	57.5%	66.2%	65.4%	67.2%
Long context MRCR v2 (8-needle)	128k (average)	16.6%	30.6%	34.1%	54.3%	58.0%
Long context MRCR v2 (8-needle)	1M (pointwise)	4.1%	5.4%	16.8%	21.0%	16.4%
Multilingual performance Global MMLU (Lite)		81.1%	84.5%	85.8%	88.4%	89.2%

Methodology

Gemini results: All Gemini scores are pass @1."Single attempt" settings allow no majority voting or parallel test-time compute; "multiple attempts" settings allow test-time selection of the candidate answer. They are all run with the AI Studio API with default sampling settings. To reduce variance, we average over multiple trials for smaller benchmarks. Aider Polyglot score is the pass rate average of 3 trials. Vibe-Eval results are reported using Gemini as a judge. Google's scaffolding for "multiple attempts" for SWE-Bench includes drawing multiple trajectories and re-scoring them using model's own judgement. For Aider results differ from the official leaderboard due to a difference in the settings used for evaluation (non-default).

Result sources: Where provider numbers are not available we report numbers from leaderboards reporting results on these benchmarks: Humanity's Last Exam results are sourced from https://agi.safe.ai/ and https://scale.com/leaderboard/humanitys_last_exam, LiveCodeBench results are from https://livecodebench.github.io/leaderboard.html (1/1/2025 - 5/1/2025 in the UI), Aider Polyglot numbers come from https://aider.chat/docs/leaderboards/. FACTS come from https://www.kaggle.com/benchmarks/google/facts-grounding. For MRCR v2 which is not publically available yet we include 128k results as a cumulative score to ensure they can be comparable with other models and a pointwise value for 1M context window to show the capability of the model at full length. The methodology has changed in this table vs previously published results for MRCR v2 as we have decided to focus on a harder, 8-needle version of the benchmark going forward.

Input and output price reflects text, image and video modalities.