Loading the catalogue…
Loading the catalogue…
Google's frontier reasoning model with mandatory chain-of-thought inference, a 1M-token context window, and full multimodal input for long-horizon, complex tasks.
Overview. Gemini 3.1 Pro Preview is Google's frontier reasoning model, released in public preview on February 19, 2026. It combines mandatory chain-of-thought reasoning with a 1,048,576-token context window, 65,536-token max output, and multimodal input across text, image, audio, video, and file formats. Developers reach for it when a task needs deliberate, multi-step reasoning over very large or mixed-format input rather than a fast single-pass answer.
Key strengths. Chain-of-thought is mandatory rather than optional on this model, with configurable reasoning effort levels (high, medium, low), which suits tasks that benefit from deeper deliberation over a quick response. Its context window is among the largest available for a hosted model, letting it hold entire document sets, long transcripts, or multi-file codebases in a single call without chunking. Prior LMSys Arena evaluation placed it solidly within frontier-class territory, though the most recent leaderboard snapshot could not be freshly re-verified this cycle.
When to use it. Use this model for long-horizon research, document analysis, or writing tasks where reasoning over very large or multi-file input matters more than raw response speed. It also accepts native image, audio, and file input, making it a fit for multimodal document and media understanding. It is not Google's dedicated choice for heavy agentic tool-calling workloads — a separate custom-tools-tuned sibling variant exists specifically for that use case.
Limitations. Chain-of-thought reasoning is always-on and cannot be disabled, which adds latency relative to non-reasoning models and makes this a poor fit for simple, low-latency lookups. Output is text-only, with no native audio or image generation. Its knowledge cutoff is January 2025, and independent third-party benchmark placements for this model have shown notable movement across recent evaluation snapshots, which we flag for transparency rather than as a settled capability signal.
Sovereignty note. Gemini 3.1 Pro Preview is built by Google, a US-headquartered creator, and served under Google's proprietary Gemini API terms. Our research did not surface an explicit training-on-input opt-out or EU data-residency guarantee for this model, so it does not carry Stav's Sovereign designation.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €1.76 / 10.56+7 rates | Online |
18 use cases classified by the curator. The note says why.
Tool-calling, extended-thinking, and huge context make it well suited to long-horizon agentic coding sessions spanning large repos.
1M-token context plus reasoning mode supports grounded answer generation over very large retrieved document sets in one call.
Massive context window allows entire document sets to be held in-context for precise Q&A rather than relying on retrieval chunking.
Explicit tool-calling capability makes this a solid fit for structured function-calling application backends.
Full-document ingestion at 1M tokens plus mandatory chain-of-thought supports cross-referencing many long papers in a single pass.
Mandatory chain-of-thought inference and tool-calling support are direct architectural fits for complex multi-step agent planning.
Tool-calling and vision support browser automation flows, but no agentic-benchmark evidence (e.g. WebArena) is available to confirm strength.
Extended-thinking mode supports deep root-cause reasoning, and large context permits ingesting full logs/codebases, though unverified by benchmarks.
Vision capability supports chart interpretation tasks, though no vision benchmark scores are documented for this variant.
1M-token context enables full-repository review, though no code-specific benchmark scores are available to claim best-in-class.
Reasoning mode plus huge context supports large dataset analysis, but no data-analysis-specific benchmarks are cited.
Vision plus 1M context allows processing of long scanned documents, though OCR-specific accuracy figures are not published.
US CLOUD Act jurisdiction exposure is a poor fit for sensitive PII-handling workflows requiring data sovereignty guarantees.
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-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-3.1-pro-preview",
"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