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Google DeepMind's efficiency-tier multimodal model with a 1M-token context window, optional chain-of-thought thinking, and native text, image, audio, video, and file input.
Overview. Gemini 2.5 Flash is Google DeepMind's closed-weight, sparse mixture-of-experts model built as the efficiency-tier member of the Gemini 2.5 family. It combines a 1,048,576-token context window with native text, image, audio, video, and file input, and it supports optional chain-of-thought thinking that developers can switch on for harder problems. Teams reach for it when they need fast, high-throughput Inference over large multimodal or long-document inputs without committing to the flagship Pro model's depth.
Key strengths. The model supports native function calling and structured tool use, making it a reasonable fit for agentic workflows that call external tools mid-conversation. Its 1M-token context window and native multimodal input across text, image, audio, video, and file formats support long-document and mixed-media tasks that shorter-context models can't handle in a single pass. Because extended thinking is flag-driven rather than always-on, developers can choose faster responses for routine requests and enable deeper reasoning only when a task warrants the added latency.
When to use it. Use Gemini 2.5 Flash for interactive applications that need fast responses across mixed media — image, audio, video, or long documents fed in as file input. It suits agentic pipelines involving structured tool calls, given its native function-calling support. When a task needs deeper multi-step reasoning, enable the optional thinking flag rather than switching models; for latency-critical paths, leave it off. Its long context window also makes it a good choice for large-codebase or large-document analysis where the full 1M-token window matters more than maximum reasoning depth.
Limitations. Output is text-only — the model does not generate audio or images despite accepting them as input. Its training data cutoff is January 31, 2025, so it won't have knowledge of events after that date. It is closed-weight and served only through Google's API, so there is no option to self-host or fine-tune the base weights, and enabling extended thinking will add latency versus the default fast-response mode.
Sovereignty note. Gemini 2.5 Flash is created and served by Google, a US-based company with confirmed CLOUD Act exposure, and it is available only as a closed, API-only Inference endpoint — there is no Sovereign deployment option for this model.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €0.26 / 2.20+5 rates | Online |
18 use cases classified by the curator. The note says why.
Native vision and file input capabilities with a 1M-token context window make this well-suited to OCR over large scanned document sets.
1M-token context window enables full-document ingestion without chunking, ideal for long-document Q&A workloads.
Multimodal input and large context window support entity extraction over long documents and mixed-media sources.
Tool-calling plus native multimodal file input supports reliable structured extraction pipelines across text, image, and document inputs.
1M-token context plus low-latency design makes this a strong fit for summarizing large document sets quickly and cheaply.
Vision plus extended-thinking mode can walk through chart interpretation, though not a reasoning flagship.
Vision capability supports chart parsing, but lack of a flagship-tier quality score means results should be validated for high-stakes use.
Speed-tier design suits high-throughput classification tasks, though quality index of 20.3 suggests accuracy tradeoffs versus flagship models.
Native tool-calling capability supports function-calling app integration at low latency and cost.
Native vision input supports image Q&A workflows; efficiency-tier positioning trades peak accuracy for speed and cost.
Extended-thinking mode aids multi-step reasoning, but weaker quality index versus Google's top-tier models limits reliability on complex planning chains.
US-jurisdiction CLOUD Act exposure makes this an active counter-recommendation for PII-sensitive redaction workflows requiring data sovereignty.
US CLOUD Act exposure rules this model out for sovereignty-sensitive compliance and policy Q&A workloads.
CLOUD Act exposure and lack of sovereignty guarantees make this a weak choice for sensitive redaction tasks despite adequate extraction capability.
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, ifbench, 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-2.5-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