Gemini

Frontier intelligence with action

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Gemini 3.8 Flash

Best for tackling complex agentic tasks at scale

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Gemini 3.5 Flash-Lite

Best for high-volume tasks that need efficiency and intelligence

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3.5 Pro coming soon

Gemini 3.1 Pro

Best for complex tasks and bringing creative concepts to life

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Gemini 3.1 Deep Think

Best for modern challenges across science, research and engineering

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Explore the latest

Introducing our latest series of models combining frontier intelligence with action. Build more capable, intelligent agents efficiently.

Models
Capabilities
Performance
Hands-on
Showcase
Safety
Ecosystem
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Models

Completing everyday tasks, or solving your most challenging problems. Discover the right model for what you need

Capabilities

Agentic coding

Tackle complex, development tasks with advanced reasoning at speed.

Advanced multimodal understanding

Transform text, images, video and audio into rich interactive user interfaces.

Long horizon tasks

Execute sophisticated workflows over extended timeframes.

Multi-step problem-solving

Leverage advanced tools to solve demanding, real-world problems.

Performance

Benchmark	Notes	Gemini 3.8 Flash	Gemini 3.7 Flash	Claude Opus 5	Claude Sonnet 5	GPT-5.6 Sol	GPT-5.6 Terra
Input price $/1M tokens, no caching		$0.75($1.50 regular)	$0.75($1.50 regular)	$5.00	$2.00	$4.00	$2.00
Output price $/1M tokens		$3.75($7.50 regular)	$3.75($7.50 regular)	$25.00	$10.00	$20.00	$12.00
DeepSWE v1.1 Long-horizon software engineering		73.7%	65.3%	74.0%	53.8%	72.7%	69.6%
GDPVal-AA v2 Knowledge work	Elo	1545	1482	1824	1584	1710	1528
Vals Finance Agent v2 Financial analyst tasks		61.4%	59.0%	58.6%	53.9%	53.8%	54.4%
Harvey's Legal Agent Benchmark Complex legal workflows	All pass rate	10.0%	8.8%	6.7%	5.0%	2.5%	0.8%
Terminal-bench 2.1 Agentic terminal coding		89.4%	85.8%	89.1%	80.4%	88.8%	87.4%
Terminal-bench 4.0 General agent capabilities		19.1%	11.2%	51.8%	12.4%	37.3%	23.6%
GDP.PDF Expert PDF document comprehension	All pass rate	35.0%	34.0%	37.0%	28.0%	40.0%	29.0%
CharXiv Reasoning Information synthesis from complex charts	No tools	86.2%	84.5%	83.7%	70.1%	85.8%	85.9%
LVBench Long video understanding		87.8%(agentic)	85.4%	75.4%	68.5%	82.1%	78.9%
LVBench Long video understanding		87.1%(static)	85.4%	75.4%	68.5%	82.1%	78.9%
HLE-Verified Multidisciplinary expert reasoning		54.9%	53.6%	54.4%	31.0%	54.5%	51.1%
OSWorld-2.0 Agentic computer use	Partial score batch tool enabled	59.0%	50.6%	75.4%	42.6%	62.6%	50.2%
BioMysteryBench Bioinformatics research workflows	Human Solvable	88.8%	87.1%	90.1%	87.5%	79.5%	83.8%
BioMysteryBench Bioinformatics research workflows	Human Difficult	56.5%	43.5%	49.4%	34.1%	44.7%	49.4%
LABBench2 Biology real-world research tasks		86.2%	82.1%	84.2%	80.1%	82.1%	81.2%

Methodology: deepmind.google/models/evals-methodology/gemini-3-8-flash

* For 3.7 and 3.8 Flash, introductory price expires on December 31, 2026. Starting January 1, 2027, $1.50/1M input tokens and $7.50/1M output tokens will apply.

Hands-on

Explore what you can do with Gemini 3.8 Flash and Gemini 3.5 Flash-Lite

Chromancers Game

Gemini 3.8 Flash built this game with a simple prompt using a looping instruction in Google Antigravity. The game uses puzzles, environmental storytelling, and textures generated with Nano Banana to create an immersive 3D level in which you play a wizard navigating a castle.

DOS Google Maps

Gemini 3.8 Flash builds a fully functional DOS version of Google Maps in a single prompt in Google Antigravity that is fully playable with locations, directions, and Street View.

Topography

Explore realtime cross-sections, 2D projections, scientific explanations in a topographic map of famous geographical sites built with Gemini 3.8 Flash in Google Antigravity using real datasets from the U.S. Geological Survey and Openweather.

Hardware Anatomy

An interactive 3D visualizer built with Gemini 3.8 Flash in Google AI Studio that generates realistic Three.js renderings of physically-proportioned teardowns for hardware devices. It automatically decomposes devices into layers you can explode and inspect with a deconstruction slider.

3D game built in Google Antigravity

From a simple text prompt to a fully playable 3D game. We used Gemini 3.7 Flash combined with Nano Banana to dynamically generate characters, items, and textures in real-time.

3.5 Flash-Lite vs. 3.5 Flash latency

3.5 Flash-Lite executes high volume tasks at a lower latency than 3.5 Flash.

One prompt, 25 web design options

Working alongside 3.6 Flash as the master agent, 3.5 Flash-Lite instantly generates 25 unique, ready-to-explore web design concepts.

Receipt analysis and translation at scale

3.5 Flash-Lite can scale receipt translation and summarization with its multimodal understanding.

Rapid game development

3.5 Flash-Lite builds a game by instantly generating and iterating through multiple options.

Showcase

“At Loopit, we’re integrating Gemini 3.8 Flash into our AI game creation agent for coding, asset reasoning, and rapid visual validation. What stands out is how well-rounded it is. We see strong performance across these tasks, impressive speed, and consistently polished game visuals, helping creators to iterate in real time and turn ideas into playable games faster.”
**Haoyuan Guo, CTO, Loopit**

“Gemini 3.8 Flash excels at long-running, document-heavy workflows, completing more than three times as many tasks as Gemini 3.7 Flash in our evaluations. We're excited to bring its sustained reasoning capabilities to Glean customers who need to turn complex requests into finished artifacts.”
**Thai Tran, AI Product Lead, Glean**

“We’re excited by how well the new Gemini Flash model understands motion across time. For Cartwheel, that means more than recognizing what’s in a frame—it means following an action and reasoning about whether the motion is actually useful. We saw promising results on both fronts.”
**Andrew Carr, CoFounder and Chief Scientist, Cartwheel**

“Gemini 3.7 Flash is a significant improvement over prior Flash models on legal work. Based on our early testing, the model lifts all-pass by 2.6 pts compared to Gemini 3.6 Flash on Legal Agent Bench, with broad gains in quality across practice areas.”
**Niko Grupen, Head of Applied Research, Harvey**

“The new Gemini Flash model delivered Opus-level grounding performance, nearly matching information recall with materially higher precision. Combined with faster and more token-efficient outcomes, it makes for a very intriguing model.”
**Adithya Ramanathan, Head of Agent Development & Research, Hebbia**

“On a subset of Harbor-Index run inside our DeepAgents harness, the model scored in the same band as the strongest models we tested, and it was the only model in our comparison to make progress on some tasks that defeated all the others. For a model in this tier, that is an excellent result on multi-step agentic work.”
**Nick Hollon, Software Engineer, LangChain**

“Gemini 3.7 Flash achieves on-par performance with mid-sized models such as GPT-5.6-Terra on our benchmarks, but at around half the cost, making it a strong contender in that category.”
**Kyrill Hux, Co-Founder, Nunu.ai**

“Gemini 3.7 Flash very efficiently implemented and audited its work on tasks porting Figma designs into code, with precise visual parity that other frontier models struggle with. I'm genuinely in a design 'psychosis' moment with this model. It just works.”
**Luke Parker, Senior Software Engineer, OpenCode**

“On a complex neurodegenerative disorder study experimental plan, I found Gemini 3.7 Flash's answer original and remarkably consistent with the latest experimental data. Compared to other models, this is the most compelling and accurate answer I have received coming very very close to what I see in the lab."
**Piere Rodriguez Aliaga Ph.D., Basic Life Research Scientist, Stanford Department of Biology**

“Gemini 3.5 Flash-Lite delivers the intelligence, speed, and cost efficiency needed for Ashler’s agentic retrieval and tool-use tasks. It excels at analyzing fragmented infrastructure records while maintaining context across hundreds of deliverables, supporting our users as they build the next generation of power grids, transportation networks, and data centers.”
**Connor Goggins, Co-founder & CTO, Ashler**

“Google Gemini 3.5 Flash-Lite is a huge jump from Gemini 3.1 Flash-Lite, punching way above its weight class. Gemini 3.5 Flash-Lite has proven incredibly fast and reliable. Ultimately, the model unlocks direct accessibility, giving users a highly responsive option they can choose on demand for smooth, uninterrupted execution.”
**Ashwin Kannan, Principal AI Engineer, Palo Alto Networks**

“Gemini 3.5 Flash-Lite landed on the Pareto frontier in our receipt extraction benchmark, offering one of the best tradeoffs we’ve tested between accuracy, latency, and cost. That combination is critical at Ramp, where we need to process receipts quickly and reliably at scale.”
**Veeral Patel, Head of Applied AI, Ramp**

See how Shopify is using Gemini 3.5 Flash

Shopify is running subagents in parallel to analyze complex data over a long horizon for more accurate merchant growth forecasts at a global scale.

See how Macquarie Bank is using Gemini 3.5 Flash

Macquarie Bank is piloting how 3.5 Flash can accelerate customer onboarding by reasoning over complex 100+ page documents, retrieving relevant information and making reliable recommendations with low latency.

See how Salesforce is using Gemini 3.5 Flash

Salesforce is integrating 3.5 Flash into Agentforce to reliably automate complicated enterprise tasks by deploying multiple subagents that retain context and execute complex, multi-turn tool calling.

See how Ramp is using Gemini 3.5 Flash

3.5 Flash is helping Ramp enable smarter, more reliable OCR through multimodal understanding of complex invoices combined with reasoning over historical patterns.

See how Xero is using Gemini 3.5 Flash

Xero is deploying agents to autonomously manage complex, multi-week workflows, such as identifying suppliers and gathering information for 1099 tax forms, enabling small businesses to automate tedious admin tasks.

See how Databricks is using Gemini 3.5 Flash

Databricks is using agentic workflows to monitor and retrieve real-time information, reason across massive datasets to diagnose issues, identify fixes and propose solutions for data scientists.