Gemini 2.5 Pro for long-context reasoning and tool use

Gemini 2.5 Pro is strongest when you need deep reasoning across long context, such as multi-document reviews, complex code analysis, or planning that spans many constraints. Because the model accepts large prompts, you can keep policies, specs, and historical context together and ask for a single, coherent response. It is also well-suited to multimodal workflows where text needs to be combined with images, audio, video, or PDFs. For production apps, structured outputs help keep results consistent.

Gemini 2.5 Pro supports an input token limit up to 1,048,576 tokens and an output limit up to 65,536 tokens. In practice, that means it can take very large documents, long chat histories, or combined media inputs in a single request. If you push the maximum, plan for larger response times and higher costs. For everyday work, many teams stay below the limit and use the extra headroom to reduce chunking and preserve continuity.

Gemini 2.5 Pro accepts text, images, audio, video, and PDF inputs and returns text output. This makes the model practical for workflows like summarizing a PDF, extracting insights from a meeting recording, or explaining a video clip in plain language. Because output is text-only, it is easy to store, search, or send downstream to analytics and business systems. If you need multimodal outputs, you can pair it with specialized media models in EvoLink.

Yes. Gemini 2.5 Pro supports function calling and structured outputs, which lets you request JSON-shaped responses for consistent data extraction and routing. This is useful when you want Gemini 2.5 Pro to populate a form, classify tickets, or produce structured summaries for dashboards. You can define the fields you need, validate output more easily, and reduce manual cleanup. For high-stakes workflows, combine structured outputs with grounding to improve reliability.

Gemini 2.5 Pro supports URL context and file search, plus grounding options such as search or maps grounding. This means Gemini 2.5 Pro can reference specific sources, link to pages, and anchor answers in retrievable material. When you need trustworthy results, provide the sources you care about, ask the model to cite them, and keep prompts focused. Grounding is especially helpful for policy, compliance, and customer-support scenarios where accuracy matters.

EvoLink provides two paths: an OpenAI SDK compatible endpoint at /v1/chat/completions and a native Gemini endpoint at /v1beta/models/gemini-2.5-pro:{method}. Gemini 2.5 Pro works with either option, so you can keep existing OpenAI-style tooling or use the native format for Gemini-specific features. Both flows use Bearer token authentication and can stream responses; the native endpoint also supports async mode with an X-Async-Mode header.

Google publishes official Gemini 2.5 Pro pricing for its API, with standard paid tiers that vary by prompt size. As listed by Google, prompts up to 200k tokens are priced at $1.25 per 1M input tokens and $10 per 1M output tokens, while larger prompts cost more; caching and storage have separate rates. EvoLink usage depends on your routing and plan, so check your dashboard for the most accurate pricing and cost controls.

Gemini 2.5 Pro lists a January 2025 knowledge cutoff, so it may not know very recent events or changes. When freshness matters, use Gemini 2.5 Pro with URL context, file uploads, or grounding so the model can rely on current sources you provide. You can also prompt it to separate cited facts from assumptions, which helps reviewers verify accuracy. This approach keeps responses useful while still benefiting from the model's long-context reasoning.