Astra, Grok and Claude: how to choose between them

Update, October 2026. GPT-6 Astra (chatgpt-astra) and Grok 4.6 are currently unavailable in Zukko. The current model list is on the Models page↗ or at GET https://api.zukko.pro/v1/models. Closest replacement: gpt-5.6-sol.

Comparing models by public leaderboards is a dubious exercise: they measure an average over someone else's work, and models are deliberately tuned for the popular ones. Below is a comparison of what can be verified objectively, and a method for everything else.

What can be compared objectively

The current list is at GET https://api.zukko.pro/v1/models; prices and context are at GET /v1/models/info.

The first thing visible: an almost eightfold price spread — from $0.04 to $0.3 per million — and the window is not the same everywhere: GLM models have 128K and no image support. That is the real entry point to the discussion — the difference has to pay for itself on your particular task.

The second: every rate here is flat, input and output cost the same. For agent workloads that matters, because input tokens outnumber output several times over, and an asymmetric rate would hit exactly there.

What a table cannot compare

Answer quality on your code, behaviour on long context, stability across multi-step chains. No leaderboard shows this, because your tasks were not in it.

A million tokens in the specification, incidentally, does not mean the model is equally attentive across the whole span. How far quality drops toward the end of the window is a property of the specific model, and it only shows on your own data.

The testing method

It takes half an hour and answers about you rather than about an average.

Take five to seven tasks from the past week — ones where you know the right answer. Run each on your candidates.

Count three quantities. How many attempts to a usable result. How many tokens went in total — the number comes back in the usage field of every response. And which types of task the answers diverged on.

The final metric is not which model is smarter but the cost of one solved task. An expensive model that got it right first time is often cheaper than a cheap one that needed four passes.

A sensible arrangement

One model for everything is nearly always either overpaying or underserving. A working arrangement looks like this.

A cheap model for volume: classification, extraction, drafts, single-file edits from a clear description.

A mid model for everyday development: ordinary code, refactoring, tests.

An expensive one deliberately: where a mistake is costly or the task needs many relations held at once.

Switching costs one line — the model field in code, a slot in Claude Code settings, the picker in Cursor.

Why this is cheap to test

Every model in the catalogue is available through one key and one balance, so comparing does not require accounts with each provider and separate invoices. The current model list comes from /v1/models, prices and context from /v1/models/info:

curl https://api.zukko.pro/v1/models
curl https://api.zukko.pro/v1/models/info

There is no starting balance, but a top-up from $1 is enough for this kind of check. On top of that, $0.50 is credited once for subscribing to the @zukkopro↗ channel (link Telegram in the dashboard).