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Google's multimodal Flash model with a 1M+ token context and opt-in reasoning effort, built for fast Inference on complex, mixed-media tasks.
Overview. Gemini 3 Flash Preview is Google's closed-weight, multimodal instruct model, accepting text, image, audio, and video input and returning text output. Released in December 2025, it carries a 1,048,576-token context window and sits in the fast, Flash-tier of the Gemini 3 line. Developers reach for it when they need quick multimodal Inference over long documents or mixed-media inputs without committing to a heavier flagship model.
Key strengths. Artificial Analysis benchmarks show a GPQA Diamond score of 0.81 and an MMMU-Pro score of 0.79, both strong results for a Flash-tier model and evidence of solid graduate-level reasoning and multimodal understanding. Reasoning effort is configurable from minimal to high (default medium), so a single model can flex between fast, lightweight responses and deeper multi-step reasoning on demand. It also supports vision, audio, and document inputs alongside tool calling and structured/JSON output.
When to use it. Use this model for interactive, latency-sensitive workloads that involve mixed media — images, audio clips, or long documents — where the million-token-plus context window matters. Its opt-in reasoning effort makes it a good fit for tasks that are usually simple but occasionally need a harder reasoning pass, without switching to a different model. It is well suited to complex analytical or research-style prompts, but it is an instruct model with reasoning as an add-on rather than a dedicated reasoning-first model.
Limitations. Google has not disclosed parameter count, architecture details (including whether it uses a mixture-of-experts design), or a training data cutoff for this checkpoint. Output is text-only — there is no native audio or image generation. Independent leaderboard standing (LMSys Arena) could not be confirmed in the latest research pass due to a rate-limited fetch, so its ranking against other frontier-class models remains unresolved.
Sovereignty note. Gemini 3 Flash Preview is built by Google, a US-based creator subject to CLOUD Act exposure, and served exclusively through Google's own proprietary API — there is no open-weight or self-hosted option.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €0.44 / 2.64+7 rates | Online |
16 use cases classified by the curator. The note says why.
1M+ token context plus prompt-caching is well matched to RAG pipelines answering questions over large retrieved document sets.
Multimodal vision support is built for mixed-media tasks including chart and figure extraction.
Vision capability combined with the 1M+ token context supports OCR and analysis of long, image-heavy documents in a single pass.
1,048,576-token context window allows ingesting entire large documents or document sets for direct Q&A without chunking.
Explicit tool-calling capability with prompt-caching makes it well-suited for production function-calling apps needing low-latency repeated calls.
Native vision capability designed for fast inference on complex, mixed-media tasks including image Q&A.
Built for fast inference, suiting latency-sensitive chatbot deployments, though no chat-specific safety benchmarks are documented for this variant.
Large context window supports long-document generation and revision, though this is a fast-inference Flash tier optimized more for throughput than maximal creative depth.
Fast inference and long context support summarising lengthy meeting transcripts efficiently.
Opt-in reasoning effort (extended-thinking) supports planning tasks, though as a Flash-tier model it trades some depth for speed versus larger reasoning models.
Vision capability supports UI/screenshot interpretation, though no dedicated agentic-browser benchmarks are documented.
Long context and prompt-caching support search-adjacent workflows, though it is a generation-oriented model rather than a dedicated retrieval/embedding model.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, swe-bench-verified, livecodebench, humaneval, ifbench, aa-multilingual-index, 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-flash-preview",
"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