Google's Gemini 3.1 Pro is one of the most capable models on the market — a 1M-token context window, strong coding scores, and very competitive pricing. Gemini Code Assist, the IDE companion, is bundled into the same family. The catch for Chinese developers is the same as with the rest of Google: generativelanguage.googleapis.com is not consistently reachable from Mainland networks, and Google Cloud billing requires a card it tends not to accept.
This guide walks through what actually works in 2026 — the API, Gemini Code Assist, and the specific use cases where Gemini's 1M context unlocks workflows other models cannot.
Why Gemini Is Worth the Effort
Before jumping into setup, a quick note on why this matters. Gemini 3.1 Pro pulls ahead of GPT-5.5 and Claude Opus 4.7 on three dimensions that matter for real engineering work:
- Context window. 1M tokens means you can put an entire mid-size codebase into the prompt and ask architectural questions across files. GPT-5.5 maxes out at 128K; Claude Opus 4.7 reaches 1M context; Gemini's 1M is the most reliable for cross-file reasoning at this size.
- Cost per million tokens. At standard pricing, Gemini 3.1 Pro is meaningfully cheaper than GPT-5.5 and Claude Sonnet 4.6 for the same task. The gap widens for long-context tasks because the per-token rate stays flat.
- Multimodal. Gemini natively handles video frames, audio, and PDFs in the same conversation. Useful when you're processing logs with screenshots, support tickets with attachments, or documentation with diagrams.
For a deeper Gemini-vs-others view see our LLM comparison 2026; for benchmark detail on coding specifically see DeepSeek V3 vs Claude 4 vs GPT-4.1 for coding (April 2026).
The Two Network Walls
Google's developer endpoints — generativelanguage.googleapis.com for Gemini API, aiplatform.googleapis.com for Vertex AI — are routed through global Google infrastructure. From China:
- Connectivity is intermittent. Some cities and ISPs do better than others, and the same connection fluctuates by hour.
- Even when the connection works, latency is 200-500 ms with a long tail, which kills streaming UX.
- Authentication tokens (Application Default Credentials, OAuth) periodically fail to refresh because the OAuth endpoints themselves are flaky.
The fixes everyone tries:
- VPN — works, but adds latency on every request, and you have to keep it running.
- Hosted in Hong Kong / Singapore proxy — works for one developer; doesn't scale to a production service from China.
- Vertex AI on a China-friendly region — only works for Google Cloud customers with a global billing account.
The fourth path, which is the focus of this guide, is to call Gemini through a gateway that is directly reachable from China and accepts RMB payment.
Calling Gemini Through Router One
Router One exposes Gemini 3.1 Pro behind an OpenAI-compatible endpoint that resolves directly to Mainland-friendly infrastructure. Set two environment variables and call it like any other model:
export OPENAI_BASE_URL=https://api.router.one/v1
export OPENAI_API_KEY=sk-your-router-one-key
from openai import OpenAI
client = OpenAI()
resp = client.chat.completions.create(
model="gemini-3.1-pro",
messages=[{"role": "user", "content": "Outline a query optimizer for ClickHouse"}],
)
print(resp.choices[0].message.content)
You can switch to gemini-2.5-flash for cheaper, faster responses; gemini-2.0-pro for the previous-generation flagship; or any of the other models on the platform without changing the SDK. Billing is in RMB, top-up via WeChat Pay or Alipay — full payment walkthrough in WeChat Pay / Alipay for OpenAI & Claude API.
Using the 1M Context Window
The 1M window is wasted if you do not feed it the right material. A few patterns worth knowing:
Whole-codebase questions. find . -name "*.go" | xargs cat your repo into a single prompt and ask "where would breaking up this monolith into services hurt the most?" Works for codebases up to ~150K lines of Go, ~100K of TypeScript before you start hitting the window edge.
Long document analysis. Drop a 700-page legal contract or a year of customer feedback CSVs into one prompt. The retrieval-vs-context tradeoff flips: at 1M context you often skip RAG and just let the model see everything.
Multi-doc reasoning. Architecture review across PRD + design doc + existing code + recent incidents. The model sees all four; it can spot inconsistencies a RAG pipeline would miss because the chunks were never co-occurring.
A practical caveat: streaming starts later for very long inputs because the model genuinely has to read all of it. Plan for 3-8 second time-to-first-token on near-1M-token prompts.
Gemini Code Assist
Gemini Code Assist is Google's IDE plugin (VS Code, JetBrains, Android Studio). It uses the same models but with Google-managed endpoints by default. From China, the plugin's authentication flow tends to fail at the OAuth callback step.
Two viable paths today:
- Use the API directly through Router One with a thin VS Code extension wrapper, or your own integration with Continue.dev. Gives you Gemini's quality with a network you control.
- Wait for plugin BYO-endpoint support. As of 2026 H1, Gemini Code Assist's settings expose a custom API endpoint flag for Vertex AI users only. If/when this opens to general API users, plug in Router One's URL and you're done.
For now, most China-based teams using Gemini for coding do it through the API plus a tool like Continue, Cursor (with custom endpoint), or Claude Code (which can be pointed at any OpenAI-compatible URL — see the Claude Code setup guide).
Pricing Snapshot
Pricing on Router One mirrors the upstream rates with a small markup that goes to keeping the gateway running and the network to China stable:
| Model | Input ($/M tokens) | Output ($/M tokens) | Best for |
|---|---|---|---|
| Gemini 3.1 Pro (≤200K context) | $2.00 | $12.00 | Long-context analysis, hard reasoning |
| Gemini 3.1 Pro (>200K context) | $4.00 | $18.00 | Whole-codebase / 1M+ token prompts |
| Gemini 2.5 Flash | ~$0.30 | ~$1.20 | High-volume tasks, cheap fallback |
| Gemini 3 (Jan 2026) | $1.25 | $5.00 | Legacy; only for prompts validated against it |
For comparison, GPT-5.5 sits at $5 / $30, Claude Sonnet 4.6 at $3 / $15, Claude Opus 4.7 at $5 / $25, and DeepSeek V4-Pro at $0.145 / $1.74. Gemini 3.1 Pro is competitive at frontier quality with the longest context window (2M tokens).
When to Pick Gemini Specifically
Not every task is best on Gemini. Rough guide:
- Pick Gemini 3.1 Pro when context size matters: cross-file refactoring, long-document QA, multimodal pipelines, anything that would otherwise need RAG.
- Pick Claude Opus 4.7 when you want the strongest agentic loop in production: Claude Code, multi-tool agents, long-horizon planning. See DeepSeek V3 vs Claude 4 vs GPT-4.1 for coding (April 2026).
- Pick GPT-5.5 when you need precise instruction-following in tight, well-specified prompts, and when latency matters more than depth.
- Pick Gemini 2.5 Flash for high-volume, cost-sensitive tasks where 90% of the quality at 25% of the price is the right tradeoff.
FAQ
Does Router One support Vertex AI features like context caching? The Gemini API path through Router One supports prompt caching where Gemini exposes it. Vertex-specific extensions (deployed model endpoints, batch prediction) are not exposed via the OpenAI-compatible interface today. If you need Vertex specifically, run Vertex on a Google Cloud project with a billing account that accepts your card, and use Router One for everything else.
Can I use Gemini's native SDK (google-generativeai) with Router One?
Today the cleanest path is the OpenAI-compatible interface. The native Google SDK uses Google-specific auth that does not pass through cleanly. Most teams either use the OpenAI SDK with model="gemini-3.1-pro" or use a thin wrapper that targets Router One's chat-completions endpoint.
What about embeddings?
text-embedding-004 and the latest Gemini embedding models are exposed through the standard OpenAI /v1/embeddings endpoint on Router One. Same key, same SDK calls.
Does this work for Gemini's image generation models?
Image generation (Imagen 3) is part of the Vertex AI surface, billed and served separately. Router One exposes select image models through the OpenAI /v1/images endpoint; check the model list at router.one/models for current coverage.
Is there a free tier? Google offers a small free tier for Gemini API direct, with the network caveats above. Router One's billing is pay-per-use after a small free credit on signup. There is no monthly subscription.
How do I keep my code portable to direct Google Cloud access later?
Use the OpenAI SDK against OPENAI_BASE_URL. If you ever switch to direct Vertex/Gemini API, you change the base URL and possibly the SDK call shape. Keep your prompts and tools in their own modules so the swap is mechanical.
Conclusion
Gemini 3.1 Pro's 1M context, multimodal handling, and aggressive pricing make it the right model for many tasks — especially when you have a lot of input to feed it. Direct access from China is unreliable; through Router One it works the same as any other OpenAI-compatible endpoint, with WeChat Pay billing.
For the broader story of routing across providers see AI model routing explained, and for an end-to-end view of running multi-model agents in production see How to run AI agents in production.