Grok 4.6 pricing on 59API: rates, setup and when to use it
The problem: one model, one key, no guesswork
Choosing a model is easy. Getting one to run reliably, at a cost you can predict, is where the friction usually starts. Maybe you are juggling two providers, two API keys and two billing dashboards, or you are staring at a monthly subscription wondering what happens when a project goes quiet. The annoying part is that token pricing is rarely one number: input tokens, output tokens and cache reads are billed separately, so a headline rate rarely tells you what a request will cost.
That is the problem 59API is built to smooth out. 59API is an independent third-party AI API relay. You get one account and one API key, one API host at https://api.59api.com, and access to Claude, GPT, Gemini, DeepSeek and other models from that single setup. There is no monthly fee. You top up a prepaid balance, and every request is charged by real token usage as it happens.
Grok 4.6 is available through that same account, using the model id grok-4.6. In this guide we will walk through Grok 4.6 pricing, the exact connection settings, and the situations where this model fits your workflow better than the alternatives.
Grok 4.6 pricing on 59API, in plain numbers
Start with the numbers, because clear rates are the whole point. On 59API, Grok 4.6 is priced per 1 million tokens: input $0.5556 (¥4), output $1.67 (¥12), and cache read $0.1389 (¥1). Billing is in CNY, and the USD figures are shown at ¥7.2 = $1.
Read those three lines as a menu of where your tokens go. Input tokens are what your prompt and any context you send cost. Output tokens are what the model writes back, and they carry the higher rate here. Cache read tokens are billed at $0.1389 (¥1) per 1 million tokens, which is worth keeping in mind if your setup reuses the same context repeatedly. Nothing is hidden behind a seat fee or a minimum commitment: 59API charges per request based on real token usage, the balance never expires, and there is no monthly fee.
Money goes in before usage does. Top-ups come in ¥10, ¥50, ¥100, ¥200, ¥500 and ¥1000, paid by WeChat Pay or bank card, and invoices are available if you need one for accounting. Because Grok 4.6 has no official list price shown for comparison here, this guide does not attach a savings percentage to it. What you can do is read the three rates, estimate your own token volumes, and see whether the numbers fit your budget before you build anything.
Setup in three steps
Setup is deliberately short. First, create your 59API account and top up a prepaid balance so there is something to draw from. Then point your client at the 59API host. For Codex, OpenAI SDKs, cURL and OpenAI-compatible clients such as Cursor, Cherry Studio, Cline and OpenCode, the base_url is https://api.59api.com/v1 and the chat endpoint is https://api.59api.com/v1/chat/completions. Your key goes in the authorization field where your client expects it.
Next, select the model by its exact id: grok-4.6. Send a short test prompt, check the response, and then look at your balance to confirm that a small amount was deducted according to real token usage. That feedback loop is the fastest way to learn what your prompt shapes actually cost.
One compatibility note is worth knowing up front. The OpenAI-compatible path above covers GPT, DeepSeek and other non-Claude, non-Gemini models, which includes Grok 4.6. Claude models work only through the genuine Claude Code or Claude Desktop client, version 2.1.285 or newer, never through the OpenAI SDK, cURL or other clients. Gemini models use Google's native protocol only through Gemini CLI, and they do not work on /v1/chat/completions. Keeping those lanes straight saves an afternoon of debugging.
When to reach for Grok 4.6 (and Grok 4.7)
Now the practical question: when should you use Grok 4.6 rather than something else in the 59API catalogue? Start with cost shape rather than marketing. Grok 4.6 charges $0.5556 (¥4) per 1 million input tokens and $1.67 (¥12) per 1 million output tokens, so a workload that produces long answers costs proportionally more than one that sends short prompts and expects short replies.
If you want a second Grok option, Grok 4.7 (model id grok-4.7) is listed at the same rates: input $0.5556 (¥4), output $1.67 (¥12), cache read $0.1389 (¥1). Same prices, different model id, so you can switch and compare without moving accounts or changing keys.
For lighter budgets, MiniMax M3 (model id MiniMax-M3) is listed at input $0.1389 (¥1) and output $0.5556 (¥4), while Qwen3.7 Plus (model id qwen3.7-plus) is listed at input $0.1389 (¥1) and output $0.4167 (¥3). GLM-5.3 (model id glm-5.3) sits at input $0.5556 (¥4), output $1.67 (¥12), cache read $0.1389 (¥1) — the same figures as Grok 4.6. We do not publish benchmark verdicts here, so treat these as candidates to test with your own prompts and your own token volumes. Run a week of real traffic, then compare what you actually paid.
Running it day to day on 59API
Finally, a few habits that keep 59API predictable day to day. Because billing follows real token usage, the most useful habit is watching your own traffic rather than guessing. Start with a modest top-up, ship a small slice of work, and let the balance tell you the true rate of your requests before you commit to a larger amount. The ¥10 and ¥50 top-ups are enough for that kind of early testing; the ¥100, ¥200, ¥500 and ¥1000 steps are there when usage proves steady.
If your team needs receipts, invoices are available for top-ups paid by WeChat Pay or bank card.
One thing to keep straight: optional monthly and weekly cards are scoped to a model family. A card has its own key, can only call that family's models, and is paid only from the card — it never falls back to wallet balance, and a balance key never draws on a card. In other words, if you are running Grok 4.6, use your 59API balance key rather than assuming a Claude or GPT card will cover it. Unused card quota does not roll over.
That is the whole workflow: one key, one host, per-token billing you can read line by line. Give Grok 4.6 a small test run on 59API, measure the tokens your prompts actually consume, and scale the top-up once the numbers make sense.