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Model details

Gemini 3.8 Flash (Vertex AI, EU)

Gemini 3.8 Flash sits within Google's Gemini API lineup as an entry documented on the official latest-model reference page, and Google DeepMind's product site frames it as the most intelligent workhorse model in the Flash tier yet, aimed squarely at coding and agentic use cases. The DeepMind page explicitly positions the model for tackling complex agentic tasks at scale, with a tagline of "advanced reasoning at Flash-level latency and scale," signaling an emphasis on fast inference paired with stronger reasoning capability than earlier Flash-class releases. Navigation on that page exposes further sections covering capabilities, hands-on examples, showcase material, performance detail, and model information, suggesting a deliberately developer-facing surface oriented toward agent builders rather than casual chat use.

In practical terms, Gemini 3.8 Flash is designed for engineers and product teams who need a responsive model that can sustain multi-step agent loops, tool orchestration, and code generation without the latency overhead of larger Gemini tiers. Its positioning as a workhorse implies a balance between capability and cost-efficiency suitable for high-volume, production-grade workloads such as autonomous coding assistants, retrieval-augmented pipelines, and orchestrated task automation. The framing as the most intelligent Flash variant to date hints at measurable reasoning gains over its predecessors while preserving the throughput characteristics the Flash family is known for, making it a strong fit when a project needs agentic depth without stepping up to a heavier model.

Eden AIvertex/gemini-3.8-flash@eugemini-flash

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Provider
Eden AI
Model key
vertex/gemini-3.8-flash@eu
Release date
Sep 2, 2026
Last updated
Sep 2, 2026
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.75
Output token cost
$3.75

Limits

Output tokens
65,536 tokens
Context window
1,048,576 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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