Gemini Flash
by Google
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Gemini Flash is Google DeepMind's family of high-speed, low-cost multimodal models that balance near-frontier reasoning with very low latency and price per token. It handles text, images, audio, video, and large context windows, making it the default workhorse model for scalable AI applications.

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Pricing
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Articles mentioning Gemini Flash (2)
01Qwen3.8-Omni-Flash_Beats_Gemini_Flash_Price_Matches_Multimodal_BenchmarksSep 19, 2026 02Google_Launches_3_Gemini_Flash_Models_But_3.5_Pro_Still_MIAJul 21, 2026
Key Features
Native multimodal input across text, images, audio, video, and PDFs
Very large context window (up to ~1M tokens on recent versions) for long documents and codebases
Adjustable thinking budgets for tuning the trade-off between reasoning depth, latency, and cost
Function calling, structured JSON output, and tool/grounding support (Search, code execution)
Available through Google AI Studio, the Gemini API, Vertex AI, and consumer Gemini apps
Pros & Cons
Pros
Excellent price-to-performance ratio, often several times cheaper than comparable frontier models
Low latency and high throughput, well suited to real-time and high-volume workloads
Generous free tier in Google AI Studio for prototyping and testing
Tight integration with Google Cloud, Vertex AI, and the broader Gemini ecosystem
Cons
Trails Gemini Pro and top competitors on the hardest reasoning and coding benchmarks
Pricing, rate limits, and model versions change frequently, complicating cost planning
Data residency, availability, and quota constraints vary by region and tier
Output can be overly cautious or verbose due to safety filtering and default reasoning traces