Qwen3.6 35B-A3B Puts a 3.9-Day Timer on Your Claude Bill

Show Notes

Watch the video: https://www.youtube.com/watch?v=uCg14nY3Jhk

A Reddit post just clocked Qwen3.6 35B-A3B at 600 tokens per second on one laptop, for free - and the payback on the machine that runs it lands under four days against a Claude Sonnet 5 output bill.
Playbooks + my answers, free - Church of SEO Jesus: https://www.skool.com/church-of-seo-jesus

Playbooks + my answers, free - Church of SEO Jesus: https://www.skool.com/church-of-seo-jesus

What this video pays off:
- The exact per-token math: 1.55B output tokens a month, $23,328 at Sonnet pricing, $3,000 workstation, 3.9-day payback
- Why a 35B mixture-of-experts model runs like a 3B model on a laptop (Apache 2.0, commercial use, no permission needed)
- Which of your LLM calls to move off the API this week (classification, extraction, first-draft) and which to keep on Claude or OpenAI (customer-facing, hard reasoning)
- The three-line code change that points your existing app at localhost instead of api.openai.com - your app does not even know it moved
- The compliance unlock: regulated buyers (health, legal, finance, government) who stalled at 'we send your data to OpenAI' now sign

Prove it yourself: open your OpenAI or Anthropic billing page right now, take last month's output token count, multiply by their current price, then compare to a one-time $3,000 workstation. That is the proof, not me telling you.

Sources:
- r/LocalLLaMA benchmark thread: https://www.reddit.com/r/LocalLLaMA/comments/1wja9ml/
- Qwen3.6 35B-A3B model card: search 'Qwen3.6 35B-A3B' on huggingface.co
- Claude Sonnet 5 and GPT output pricing: https://openrouter.ai/models

Chapters:
00:00 Hook: 600 tok/s on one laptop, for free
00:34 What Qwen3.6 35B-A3B is (35B total, 3B active per token)
01:08 The r/LocalLLaMA benchmark + Apache 2.0
01:45 Claude Sonnet 5 pricing vs Qwen ($23,328/mo)
02:11 3.9 days to payback on a $3,000 workstation
02:52 The math live in PowerShell
03:23 What to keep on the API (customer-facing, hard reasoning)
03:37 Local 80% / API 20% split
04:23 Monday morning: the three-line code change
05:12 Compliance: regulated industries unlock
06:14 Verify against your own OpenAI or Anthropic bill
06:44 Own the compute - the new lever
07:07 Free Church walkthrough: workload split + hardware list
07:24 Next up: what happens to frontier lab margins

Audio edition of the Stewart Vickers AI YouTube video of the same name. Full transcript on this episode page.

What is Stewart Vickers AI?

The audio edition of the Stewart Vickers AI YouTube channel. Each episode takes one AI release - a model, a tool, a price cut - and translates it into money for business owners: what got cheaper, who got undercut, and what to do about it this week. Operator credibility over theory, specific numbers over vibes. Every episode links the original video and the free Church of SEO Jesus community where the prompts and workflows live.

600 tokens PR second on one laptop for free. 6 on consumer hardware. Not a data center, not a $50,000 rig, a machine you could carry to a coffee shop. If you pay an OpenAI bill or an Anthropic bill that grows every month, your ceiling just moved. The old ceiling was whatever your vendor charged, the new ceiling is the desk your laptop sits on, and the payback math on a $3,000 box comes in at under four days. I am going to show you the exact PR token pricing, the exact throughput number, and the exact workloads that flip from paid API to your own machine starting Monday.

635 BA3B is an open waiter Modal, 35 billion parameters total, 3 billion actually firing PR token, which is the lets a laptop keep up with a data center. Stay for the crossover math at the three minute mark, you will not want to guess this one on your P&L. Here is what happened. Someone Post-it a benchmark on r slash local LLMA showing 600 tokens PR second on a single request. Faster than most hosted APs will send you back a response. 0 licensed, which means you can put it in a paid product tomorrow with nobody's permission.

The reason a 35 billion parameter Modal runs like a 3 billion parameter Modal is a mixture of experts design. The full brain sits on disk, only the specialist you need wakes up for each token. Big memory, small compute, great for a laptop. So what does the vendor bill actually look like if you replicate this? I pulled live pricing off open router this morning. Claude Sonnet 5 charges $10 PR million output tokens.

Claude Opus 1250 GPG class models sit in the same band. 55 billion output tokens at Sonnet pricing $23,328 every month. 9 days, not months, days. One number carries the whole video and it is a number your CFO has not been told. Quick housekeeping. My channel covers every open weight Modal that undercuts an API bill on the day it ships.

So the exact commands, the Modal card link and the pricing page I just read are in the pinned comment below. And the church of SEO Jesus, my free school community is where the full playbook lives and where I answer when your setup breaks. Link below. 9 days straight arithmetic. And the workstation figure is the top of the range by used by last generation and the payback window compresses to under two days. Now the honest part, you are not going to run every LLM call on this box and you should not frontier models still win on complex reasoning, long tool chains and the edge cases that only show up in production.

Keep those, keep the customer facing conversation flows too. If a response is going straight to a paying customer, you want the best Modal money buys and it is worth the $10 a million. Most of your bill is not that most of your bill is high volume, predictable, boring work, classification, extraction, first draft copy, categorizing inbound tickets, rewriting product descriptions, tagging leads. Every one of those has a right answer that a smaller Modal can reduce for a 10th of the cost, or in this case, zero split your calls, root the boring 80% to a local Qwen box. Keep the IPI for the messy 20%. Your bill drops by roughly 80% overnight.

Here is the Monday morning instruction. Ask your developer or your agency this exact sentence. What percentage of our current LLM spend is going to classification extraction or templated generation? com. Same API surface, same JSON in, same JSON out. Your app does not even know it moved.

The runtime is public. The weights are public. The wrapper libraries speak the same syntax your app already uses. There is a second thing that flips this week and nobody is talking about it. Compliance. Every regulated customer you have ever lost because your AI feature sent their data to a third party.

The objection dies the moment your Modal runs on hardware you own. Health, legal, finance, government. The room you never got into is now a room you can walk into. The industries writing the biggest PR seat checks for software right now are the ones with the strictest data rules. Medical records, financial audits, government contracts. Every one of them stalls at the words we send your data to open AI.

Every one of them signs the second you can say the Modal runs on a box in your building. An agency I know just closed a six month contract with a small law firm because they moved from an open AI wrapper to a local Modal. Same product. The only thing that changed was where the data lived. Do not take my word for any of this. Open your open AI or anthropic billing page yourself right now.

Look at last month's output token count. Multiply by their current price. Then compare that to a one-time $3,000 workstation. That is the proof, not me telling you. Which brings US back to 600 tokens PR second. The number the whole thread hangs on and here is why it matters more than the throughput itself.

Two years ago a founder had one lever on their AI bill. Negotiate down, beg for credits, move to a cheaper tier. Now a founder has a second lever. Own the compute. 9 days. The little guy just got the same cost structure a fortune 500 fought a to build.

If you want the workload by workload split, which calls to move to your own box first, the hardware shopping list I would order today, and the three line code change that flips a live app off the IPI, that whole walkthrough is inside the church of SEO Jesus, my free school community. Link in the description. The next video I am dropping breaks down what actually happens to big frontier labs when 80% of their revenue base decides to self-host. The answer is uglier than they are telling investors. Their margins were built on you having no alternative. You have won now.