Gemini 3 Field Notes: Inside the gemini3api Playbook

Every time we brief customers on gemini3api, we end up repeating the same story: why Gemini 3.0 Pro is the “do everything” flagship, how EvoLink routes traffic without credential sprawl, and which quickstart gets them shipping fastest. Consider this the canonical field note you can read without digging through repo docs.

1. Why gemini3api exists

• Gemini 3.0 Pro is the “do everything” flagship – million-token context, chart-topping ARC-AGI-2 and GPQA scores, best-in-class UI/video perception on ScreenSpot/Video-MMMU, and tool-aware planning for agents.
• EvoLink is the switchboard – one key, one dashboard, OpenAI-style or Google-native request formats, and async jobs that work the same for text, image, or video models.
• gemini3api.com is the front door – a landing page that keeps Gemini 3’s positioning clear, links straight to docs/keys, and now hosts a blog that mirrors the marketing story.

If you need a model that decomposes messy tickets, reasons over screenshots, or orchestrates tools without flaking out, Gemini 3 is the default benchmark we use internally—and this playbook is the battle card we hand to partners.

2. Capability cheatsheet

• Reasoning: Gemini 3.0 Pro leads on Humanity’s Last Exam, ARC-AGI-2, GPQA Diamond, and Vending-Bench style agent evaluations. It pushes more “reasoning tokens” (see API responses) so you can see when the model went deep.
• Multimodal: It ingests text, PDFs, UI captures, video snippets, and audio simultaneously. ScreenSpot-Pro and MMMU-Pro numbers matter because they translate to “the model actually understands your dashboards”.
• Context: ≈1M tokens in, tens of thousands out. Enough for repo-scale refactors or multi-hour meeting transcripts without chunking gymnastics.
• Agent-ready: Tool planning, memory diffs, and long-horizon execution. We’ve watched it keep state across Evolink Workflows and Canvas blocks without collapsing.

3. How it stacks up

Bottom line: we hold Gemini 3 as the reference for multimodal reasoning, but we still expose GPT-5/Claude/Qwen via EvoLink so you can A/B latencies, costs, and task fit.

4. Why route through EvoLink

• One key, many models – https://api.evolink.ai fronts Gemini, GPT, Claude, Qwen, Veo, Seedream, and more. Billing, auth, and analytics stay unified.
• Dual request formats – stick with the OpenAI SDK (/v1/chat/completions, model: "gemini-3-pro-preview") or call the Google-native endpoints (/v1beta/models/gemini-3-pro-preview:generateContent). We normalize auth headers either way.
• Async jobs – any heavy run can set X-Async-Mode: true. We return a task ID, you poll GET /v1/tasks/{id} across text, image, or video workloads. Status enums are consistent (pending, running, completed, failed).
• Observability – responses expose reasoning_tokens, task costs, and streaming metadata so you can actually tell why a run was slow or expensive.

5. Quickstart (OpenAI SDK format)

1. Grab a key – head to EvoLink, open Dashboard → API Keys, and copy the token you want to use for Gemini 3.
2. Fire a request:

curl --request POST \
  --url https://api.evolink.ai/v1/chat/completions \
  --header "Authorization: Bearer YOUR_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "gemini-3-pro-preview",
    "messages": [
      {
        "role": "user",
        "content": "Introduce yourself in three sentences and list one killer use case."
      }
    ]
  }'

3. Reuse existing code – in Node/Python you can point the official openai SDK to EvoLink by overriding baseURL. We keep both language snippets updated in the internal playbook.

6. Quickstart (Google native format)

Prefer the Vertex-style payloads? Switch the endpoint and payload structure:

curl --request POST \
  --url https://api.evolink.ai/v1beta/models/gemini-3-pro-preview:generateContent \
  --header "Authorization: Bearer YOUR_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "contents": [
      {
        "role": "user",
        "parts": [
          {"text": "Summarize today’s Gemini 3 shipping log in 4 bullet points."}
        ]
      }
    ]
  }'

Streaming works the same (streamGenerateContent), and the async task headers carry over.

7. Async jobs & production hygiene

• Add X-Async-Mode: true to offload giant multimodal prompts; the response gives you a task_id.
• Poll GET /v1/tasks/{task_id}—we return status, timestamps, output payloads, and cost estimates when it finishes.
• Use X-Max-Stale-Seconds or cache-control headers if you’re fanning out inference-heavy UIs and want consistent latencies across reruns.

8. Best practices from the playbook

1. Know when to switch models – if you need plugin support or enterprise controls, GPT-5/Claude still matter. Use EvoLink routing to fail over by tag.
2. Log reasoning tokens – they’re the leading indicator that Gemini 3 is going “deep”. If a request spikes, review prompts or downgrade to Flash for CRUD steps.
3. Stay multimodal – we saw the biggest wins when we included screenshots/video context rather than forcing text-only prompts.
4. Layer agents – Evolink Workflows + Gemini 3 + Canvas let you keep scratch pads, policy lookups, and external API calls inside the same plan JSON.

9. What’s next

This playbook will keep evolving with new benchmarks, pricing notes, and deployment guides (Cloudflare Workers via OpenNext is already live). Turning it into a blog post means anyone landing on /blog can catch up without cloning the repo.

Bookmark this version, keep an eye on the repo for updates, and if there’s something missing—open an issue or drop it in support so the guide (and now this post) stays the most honest summary of Gemini 3 we can provide.