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The weekly limit cut Anthropic is calling an increase

My Claude experience keeps getting less satisfactory. Every week I bang up against the Cowork weekly limit. Now that limit is about to get smaller, not bigger, whatever the headline says.

The math Anthropic already admitted

On 29 August, Anthropic’s own developer account, @ClaudeDevs, posted this on X:

“Starting September 14, we’re permanently raising standard weekly limits in Claude Code by 25% for Pro, Max, Team, and seat-based Enterprise plans. Until then, the current 50% increase will be in place.”

Read the arithmetic and the “increase” isn’t one. A temporary 50 per cent boost is live now, expiring 13 September. From the next day it’s replaced by a permanent 25 per cent rise on the old baseline. Index the baseline at 100: the boost has you at 150 today, the new “increase” lands at 125. That’s 17 per cent less than what you have right now, and Anthropic said so themselves, in a second post the same day:

“Compared to today, this works out to a 17% reduction in weekly limits on Claude Code. We’re working on exciting changes that will make it feel like you’re getting more from Claude, while having more visibility and control of your usage. Can’t wait to share them.”

Two subscriptions, one price

I run Claude Cowork alongside a similarly funded ChatGPT account, both US$20 a month. As I wrote in July, the two feel increasingly interchangeable for what I do, and the gap keeps closing. ChatGPT gets me further before I hit a limit and feels more responsive. None of that makes me want to leave Claude. It makes me want a genuine third option that doesn’t depend on either company’s quota decisions, and in a few weeks I’ll be properly offline exploring remote parts of outback Australia, so I’ve spent the past few days actually testing one.

TL;DR

What I actually tested

Before picking a model I wanted to know what I use Cowork for, not what I assume. Pulling 478 logged sessions from my own tracker, roughly half is mechanical: filing, tracking, drafting emails. The rest splits across finance, travel and analysis.

The candidate was Qwen3.6-35B-A3B, running locally via LM Studio on a Mac Powerbook M4 Max, 48GB. Six tests built to mirror those categories: tool-calling, following a skill’s exact output format, a financial projection, a long-document needle-in-haystack search, a coding task against a real script, and fact-checking a set of claims before writing up the analysis. I also wired it into aider, the open-source pair-programming CLI, to see how it handled real file edits.

Where it earns its keep

It called tools correctly and reproduced an exact required output format byte for byte. Fed a real 138,000-token slab of my own notes with a planted code buried three-quarters of the way through, it found it exactly. Given a real script to write against my tracker database, it ran first try with zero errors, a real jump from a broken May test. Wired into aider, it correctly edited a real Python file and applied the change as a clean git diff.

That’s a meaningful chunk of what I actually do with Cowork, filing, tracking, coding, digging through long documents, run offline for the price of electricity.

The wild goose chase, quantified

The costly part is the one I’d already noticed anecdotally: it burns far too many tokens chasing its own tail before answering. Qwen3.6 is a hybrid reasoning model, every response can carry a hidden “thinking” block before the visible answer, and on a one-line formatting task, 98 per cent of the token budget went to that hidden reasoning. Three times it burned its entire budget thinking and returned nothing, no error attached, the sort of failure a naive setup reads as a normal response. Even at an 8,000-token budget, a ten-year financial projection never finished, though it nailed the formula and the first year’s figure to the cent before running out of room.

Asked to fact-check a set of claims and write up the analysis, it nailed the structure and tone without much trouble, then invented specific, plausible, entirely false numbers and stated them with full confidence, nothing hedged or flagged as an estimate. For anything built on checkable claims, that’s disqualifying for unsupervised use, and a good argument for keeping fact-checking a separate step from drafting, which matters even more with a model that has no way to check itself against the live internet.

The part that surprised me

I’d assumed Hermes Agent, the free framework built to front local models, was a new tool I’d have to hand my files to on trust. Turns out I already run it. It’s the framework behind an agent I’ve had quietly running since May, with its own persona, a Todoist connector already wired to a real API key, and dozens of stored skills. Pointed at the local model, it discovered the live Todoist connector’s tools and, asked to make one read-only call, correctly reported my account structure and changed nothing. The real gaps are narrower than “all or nothing”: the local model’s personality ran chattier than the persona it was meant to run, and there’s no web search wired in yet, the same gap that blocks the fact-checked work above.

There’s a third path that looks like it solves the whole problem and doesn’t. Ollama shipped a one-toggle way to point Claude Desktop itself at a local model instead of Anthropic’s own. But the connectors and fine-grained permissions that make me trust the Claude app are wired to Anthropic’s own models, not whichever one Desktop is pointed at. Swap the backend and you keep the familiar window and lose the part that made it trustworthy, on purpose: both Anthropic and OpenAI have a real commercial interest in keeping tight control over what their frameworks can do to a user’s files. A local swap doesn’t inherit that control, it just borrows the furniture.

How much AI does the outback actually need

Realistically, how much do I need out there? I’ll pre-compute the things that matter, sunrise and sunset times, tide charts, and go from there.

On the numbers, a quarter to a third of what I actually use Cowork for is realistically offline-substitutable today. The rest isn’t blocked by the model, it’s blocked by missing internet access and connectors, a smaller, more tractable problem. I’d still like to cut my dependence on two US$20-a-month subscriptions I increasingly can’t rely on. We’ll keep chipping away at it.

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