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Google's reasoning-tuned Flash-tier model for fast coding, agentic, and multi-step reasoning tasks with a million-token multimodal context window.
Overview. Gemini 3.8 Flash is Google's reasoning-tuned Flash-tier model, released in September 2026 as the successor to Gemini 3.7 Flash. It's built to sit between Google's high-throughput Flash-Lite models and its frontier Pro line, giving developers a faster, more affordable option for software engineering, agentic workflows, and multi-step reasoning. It accepts text, image, audio, and video input with a context window up to 1,048,576 tokens, and returns text output.
Key strengths. Independent benchmarking from Artificial Analysis puts its coding index at 74.1 and GPQA Diamond score at 0.94, both strong results for a Flash-tier model. Google also reports 54.9% on HLE-Verified, and the model carries a measurable agentic index (48.5) reflecting real multi-step tool-use performance. Reasoning is on by default, with configurable thinking effort (low, medium, high) letting you trade latency for deeper multi-step reasoning on harder tasks.
When to use it. Reach for Gemini 3.8 Flash when you need fast, interactive Inference for coding assistance, agentic tool-calling workflows, or tasks that benefit from multi-step reasoning without the latency of a frontier-tier model. Its million-token context window and native support for image, audio, and video input make it a fit for large-document or multimodal analysis pipelines where Flash-tier speed matters. It also natively supports prompt caching and a built-in web-search tool, which is useful for retrieval-augmented or up-to-date-information tasks.
Limitations. Output is capped at 64K tokens and is text-only — there's no image, video, or speech generation. Reasoning cannot be fully disabled (the lowest supported effort is 'low', not 'minimal'), so every call carries some reasoning overhead compared to non-reasoning Flash models. Google has not disclosed parameter count, architecture details, or a precise training cutoff for this model.
Sovereignty note. Gemini 3.8 Flash is built and served by Google, a US-headquartered creator, so deployments should assume standard CLOUD Act exposure rather than Sovereign, EU-only handling. No training-on-input policy or endpoint data-residency commitment was confirmed in current research.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €0.66 / 3.30+3 rates | Online |
13 use cases classified by the curator. The note says why.
1,048,576-token context window allows ingesting very large document sets directly for grounded answer generation without heavy chunking.
The 1M-token context window is a direct architectural fit for long-document question answering in a single pass.
Explicit tool-calling capability is the core requirement for function-calling applications; Flash-tier latency suits high-throughput app integration.
Native tool-calling support combined with a 1,048,576-token context window makes this well suited to orchestrating multi-tool workflows with large state.
Tool-calling support enables action-taking agent loops, but no vision or browser-specific benchmark is documented to confirm strength here.
Flash-tier models are typically optimized for low-latency, high-volume tasks like classification, though no accuracy benchmark is published for this variant.
Tool-calling enables backend lookups during chat, and Flash-tier speed is well suited to interactive support latency requirements.
Long context window comfortably fits full meeting transcripts, and Flash-tier latency suits near-real-time summarisation workloads.
Tool-calling and a very large context window support multi-step task tracking, though as a Flash-tier model it likely trades some reasoning depth for speed versus larger siblings.
Massive context window supports synthesizing across many source documents at once, though Flash-tier positioning implies speed over deepest reasoning.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "google/gemini-3.8-flash",
"reasoning_effort": "high",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at