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Google's closed-weight, reasoning-tuned flagship with a 1M-token context window, multimodal input, and native tool calling.
Overview. Gemini 2.5 Pro is Google's closed-weight, reasoning-tuned flagship model, generally available since June 2025. It pairs a 1,048,576-token context window with native multimodal input across text, image, audio, and video, and always produces a visible reasoning trace before its final answer. Developers reach for it when a task requires reasoning over very large documents or codebases alongside native tool calling, code execution, and web search grounding.
Key strengths. Gemini 2.5 Pro scores 63.8% on SWE-bench Verified and posts a coding index of 33.3, reflecting solid performance on realistic software-engineering tasks. Its Design Arena scores range from 1119 to 1241 Elo, pointing to comparatively strong front-end and data-visualization generation. Its broader benchmark profile — an intelligence index of 25.9 and agentic index of 7.2 — is consistent with its positioning as a large-context reasoning generalist rather than a narrow specialist.
When to use it. Use Gemini 2.5 Pro for document- or codebase-scale reasoning tasks that need the full 1M-token context window, or for multimodal inputs spanning image, audio, and video alongside text. It fits interactive workloads that call for native tool calling, code execution, structured or JSON-mode output, and web search grounding. Its mandatory chain-of-thought reasoning makes it a better fit for complex multi-step problems than for high-throughput agentic pipelines, where its low agentic-index score suggests a weaker fit.
Limitations. Training data runs only through January 2025, and output is text-only — there is no native audio output on this variant, unlike its Preview TTS sibling. The mandatory reasoning trace adds latency compared with non-reasoning peers.
Sovereignty note. Google is a United States-based creator, so Gemini 2.5 Pro carries CLOUD Act exposure and is served only through Google's own infrastructure — it is not available for Sovereign, self-hosted deployment. Teams with strict EU-only data-residency requirements should weigh this against Sovereign-hosted alternatives on the Platform.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €1.12 / 8.98+5 rates | Online |
24 use cases classified by the curator. The note says why.
71.0 on SWE-bench Verified plus native tool-calling and mandatory chain-of-thought reasoning make this a top-tier agentic coding model.
High SWE-bench Verified (71.0) and extended-thinking mode support multi-step root-cause reasoning across large codebases.
Chain-of-thought reasoning plus strong coding benchmarks make detailed code walkthroughs a natural fit.
1,048,576-token context window allows full-repository ingestion, combined with a 33.3 coding index and 71.0 SWE-bench Verified score.
Over 1M-token context window enables direct Q&A over full document sets without chunking.
Documented native function/tool calling makes this a strong fit for structured function-calling application backends.
Web search grounding and tool-calling support browsing workflows, though no browser-specific benchmark is cited.
Vision capability supports chart interpretation; Design Arena data-visualization score of 1289 indicates comparatively strong visual task handling.
Vision support plus a strong 1289 Design Arena data-visualization score suggests solid chart-to-data extraction capability.
Extended-thinking and tool-calling support multi-step analytical workflows, though no dedicated data-analysis benchmark is cited.
Vision capability combined with a million-token context window suits OCR over long scanned documents, though OCR-specific benchmarks are not reported.
Native vision capability supports image-based question answering, though no dedicated vision benchmark is reported.
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.
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