DEV Community: VirtualHotBar

Tokeness review: one API key for GPT/Claude/Gemini/Grok/DeepSeek/Kimi (with real caveats)

VirtualHotBar — Sat, 01 Aug 2026 08:49:59 +0000

TL;DR: Moved my multi-model API usage to Tokeness (tokeness.io), a third-party API gateway. One key covers GPT, Claude, Gemini, Grok, DeepSeek, GLM and Kimi; prepaid CNY billing (7:1) with business invoices; per-1M-token prices listed openly in the console; genuinely usable free tier. Main caveats: GPT/Claude are blocked for mainland China IPs (their own compliance decision, not a bug), it's a relay so your prompts pass through their servers, and prices aren't locked. Full breakdown below.

Background

I run Claude Code and Codex CLI daily, and was maintaining separate accounts for OpenAI, Anthropic and DeepSeek — three billing systems, three key dashboards, plus payment friction without an international card. A few weeks ago I consolidated onto Tokeness. Not affiliated with them; just sharing what I found, since multi-model gateways get discussed here a lot.

What it is

A relay/gateway: you point your tools at https://n.tokeness.io/v1, they forward to the official APIs. Both OpenAI-compatible and native Anthropic protocols are supported, so Claude Code, Codex CLI, Cursor, Cline, Dify, n8n etc. are a one-line base_url change (they have ~20 integration guides at docs.tokeness.io).

from openai import OpenAI

client = OpenAI(
api_key="YOUR_TOKENESS_KEY",
base_url="https://n.tokeness.io/v1",
)
resp = client.chat.completions.create(
model="deepseek-v4-flash", # copied exactly from their model marketplace
messages=[{"role": "user", "content": "hello"}],
)
The good

One key for everything. New models land fast — kimi-k3, grok-4.5, the gpt-5.6 series, gemini-3.6-flash are all live as of late July.
Pricing transparency is better than most resellers: every model shows input/output price per 1M tokens in the console. No "30% of official, do the math yourself" nonsense.
deepseek-v4-flash is updated to the official 0731 release and billed 40% below official pricing. It's been solid for agent workloads in my testing.
Free tier that actually works: tokeness/free routes to ~200B-param models (DeepSeek V4 Flash class). Enough to test your whole integration without paying.
Business invoices + per-request usage logs (status, model, cost per call). If you expense API costs, this matters.
They run an impact program — 10M to 5B free tokens/month for OSS/nonprofits, with public rules (docs link).
The bad

GPT, Claude and some others don't work from mainland China IPs. They announced this June 15 as a compliance move. Overseas networks are fine; Chinese models work from anywhere. Weirdly, publishing this restriction makes me trust them more than resellers promising "direct Claude access from China," but it's a hard limitation depending on where you are.
It's still a third-party relay. They claim they only forward and retain nothing — you can't audit that. My rule: personal projects fine, customer data/prod secrets go through official APIs only. Same rule I'd apply to any reseller.
Prices aren't locked. They state upfront that pricing follows upstream costs, FX and channel policy. gpt-5.6-luna dropped 80% recently, but cuts can presumably reverse.
"Original models, no dilution" is their claim. I ran basic sanity checks (identity questions, known-answer reasoning prompts, response field structure) and nothing looked off, but YMMV.
Who it's for

Multi-model users who want one bill and proper invoicing, and have the network situation for GPT/Claude sorted. Not for anyone whose core need is "Claude directly reachable from mainland China" — they're explicit that this isn't that.

They also have a referral program (20% of a friend's first top-up) — mentioning for completeness, deliberately not posting my link.

Anyone else using it? Curious how their stability holds under heavier agent workloads.

One API Key for GPT, Claude, Gemini, Grok, DeepSeek and Kimi — How I Simplified My AI Dev Setup

VirtualHotBar — Sat, 01 Aug 2026 08:46:59 +0000

The problem: too many accounts, too many bills

If you build with LLMs daily, you probably know the pain. I had an OpenAI account for GPT, an Anthropic account for Claude Code, another one for DeepSeek, and I wanted to try Grok and the new Kimi models too. That's four or five separate sign-ups, billing systems, and API key dashboards — plus the payment friction if you don't have an international credit card.

A few weeks ago I consolidated everything onto Tokeness (tokeness.io), a multi-model API gateway. This post is my honest experience: what it does, how to set it up, and the caveats you should know before trying it.

Full disclosure: I'm a regular user, not affiliated with Tokeness. It's a third-party relay service, not an official product of any model vendor — more on what that means below.

What it is

Tokeness is an API aggregation gateway. You point your tools at their endpoint, and they route requests to OpenAI, Anthropic, Google, DeepSeek, and others:

Your code / Claude Code / Codex CLI / Cursor
        ↓  (OpenAI-compatible or native Anthropic protocol)
n.tokeness.io  (auth → routing → forwarding)
        ↓
Official model APIs → response returned to you

The pitch: one API key for all major models — GPT, Claude, Gemini, Grok, GLM, DeepSeek, Kimi — with new models landing fast (kimi-k3, grok-4.5, the gpt-5.6 series, and gemini-3.6-flash are already live as of late July).

Setup (takes ~5 minutes)

Sign up at tokeness.io and top up a small balance (prepaid, billed in CNY at 7:1 vs USD).
Create an API key under API Keys. Split keys by project — small quota for dev, scoped model access for prod.
Copy a model name from the Model Marketplace (exact casing matters).
Point your client at the gateway:

import OpenAI from 'openai'

const client = new OpenAI({
  apiKey: 'YOUR_TOKENESS_API_KEY',
  baseURL: 'https://n.tokeness.io/v1'
})

const response = await client.chat.completions.create({
  model: 'YOUR_MODEL_NAME',
  messages: [{ role: 'user', content: 'Introduce Tokeness in one sentence.' }]
})

console.log(response.choices[0]?.message?.content)

For Claude Code and Codex CLI, there are dedicated integration guides in their docs (docs.tokeness.io) — along with 20+ other tools: Cursor, Cline, Roo Code, Dify, n8n, Open WebUI, LiteLLM, OpenCode, and more. Both OpenAI-compatible and native Anthropic protocols are supported, so most setups are a one-line base_url change.

What I like

One key, everything. Claude Code and Codex CLI now share a single key and a single bill. Model Marketplace updates show up within days of official releases.
Transparent pricing. The console lists input/output prices per 1M tokens for every model — no vague "30% of official price" marketing math. Current channel tables convert at 1 USD = 7 CNY, and business invoices are available, which matters if you expense this stuff.
A real free tier. tokeness/free is a free model route backed by ~200B-parameter models (e.g., DeepSeek V4 Flash). Genuinely usable for lightweight tasks and for testing your integration before paying.
Aggressive pricing on Chinese models. deepseek-v4-flash was updated to the official 0731 release with noticeably stronger agent capabilities — at 40% below official pricing.
An impact program with actual rules. Open-source projects, nonprofits, and social enterprises can apply for 10M–5B free tokens per month (details). The requirements are public: a sponsorship link in your README, monthly usage feedback, and a real review process.
Usage logs per request. Status, model, and cost for every call, plus a data dashboard — no more "where did my balance go."

The honest caveats

This part matters, so I'm not burying it:

GPT, Claude, and some other models are not available from mainland China IPs. Tokeness announced this on June 15 as a compliance decision — overseas networks work fine. Counterintuitively, I see this as a trust signal: they're stating their limits publicly instead of quietly operating in a gray zone. Chinese models (DeepSeek, GLM, Kimi) work normally either way.
It's a third-party relay. Your prompts technically pass through their servers. They state they only forward requests and retain no conversation data — but you can't independently audit that. My rule, same as with any relay: personal projects and normal code are fine; customer PII and production secrets go through official APIs only.
Prices aren't locked. They explicitly say pricing follows upstream costs, exchange rates, and channel policies. Check the console before big batch jobs.
"Original models, no dilution" is their claim. Standard self-checks apply: ask the model for its identity/cutoff, run a benchmark prompt with a known answer, and verify response fields (usage, stop_reason) match official SDK behavior.

There's also a referral program (20% back on a friend's first top-up, no cap) — I'm mentioning it for completeness, not including a link.

Verdict

If you want one key for multiple model providers, transparent per-token pricing, and proper invoicing — and you have the network situation sorted for GPT/Claude — Tokeness is worth a small top-up. Start with the tokeness/free route to test your integration, split keys per project, and set quota limits from day one.

If your only requirement is "Claude reachable directly from mainland China," this isn't the tool for that, and they're upfront about it.

Links: tokeness.io · docs.tokeness.io · API base URL: https://n.tokeness.io/v1

I built an AI model tier list website: AITier

VirtualHotBar — Sun, 10 May 2026 09:08:35 +0000

AI models are moving fast. OpenAI, Anthropic, Google, DeepSeek, Qwen, Meta and many other teams keep releasing new models, while the number of API providers is also growing quickly. Whenever I need to choose a model, I often have to jump between benchmark leaderboards, pricing pages, provider docs and scattered model information.

So I built AITier:

https://aitier.net

The goal is simple: make it easier to find the right AI model or API provider for your use case.

AITier provides visual AI model tier charts and category rankings. You can compare models across areas such as general capability, coding, math reasoning, science, multimodal tasks, agents and tools, image generation, video generation and audio. Besides rankings, it also shows useful model information such as pricing, speed, context length, parameter size, open-weight status and available providers.

If you just want a quick overview of which models are currently strong, the tier chart is a good starting point. If you want a more detailed comparison, you can use the ranking tables. For a specific model, the detail page includes capability radar charts, benchmark scores, pricing, speed metrics and provider information.

AITier collects and organizes data from public sources such as Artificial Analysis, LLM Stats, models.dev, LMArena, SWE-bench, MATH, MMMU and LiveBench. Scores are normalized and organized so models can be compared more easily within the same category.

AITier is not meant to be an absolute authority on model rankings. Model performance depends heavily on the task, and any leaderboard should be treated as a reference rather than a final answer. The site is intended to be a practical starting point: when you want to know whether a model is strong, expensive, suitable for a certain scenario, or available through a certain provider, you can check AITier first.

The site is still evolving, and I’ll continue improving data coverage, pricing information and comparison features.

Feedback is welcome — especially if you notice missing models, inaccurate prices, questionable rankings, or features you’d like to see.