Free model IDs now ship with expiry dates: how to use promotional inference without building on sand

Something changed in how models get launched, and it happened fast enough that most teams have not adjusted their engineering to match.

Free inference used to be a trial: a small credit, a low rate limit, an obvious upgrade path. In August 2026 it became a distribution strategy with a calendar. Four examples from a single week:

• Ox Alpha. An anonymous listing on OpenRouter and OpenCode, free, 1M token context, multimodal. It processed over 20 trillion tokens in six days, was revealed as Z.ai GLM-5.3-Flash, and the alias was retired.
• Inkling. Thinking Machines made it free on OpenRouter for agentic harnesses only, for the next few weeks, and said openly that it would use the data, disassociated from accounts, to improve the model.
• MiniMax Week. M3 and M2.7 free on GMI Cloud from 24 August to 6 September, reachable through the provider API or a gateway, alongside their speech and music models.
• Z.ai Build Week. 100 million free tokens each to 50,000 new users, with the window extended into an ongoing series.

That is roughly a trillion tokens of promotional capacity available to any developer who wanted it, in one week, across four labs. It is genuinely useful. It is also a new class of dependency, and the failure modes are specific.

Three ways a free window fails

The ID is retired. This is what happened to Ox Alpha. One developer wrote the postmortem for everyone in a thread on r/openrouter: 350 million tokens burned across two accounts in two days, excellent output, then the listing disappeared. Their line is the one to remember: "the worst part is i started planning so much work around it not knowing that it will end in 2 days." Others found out the same way, with a thread titled simply "OX Alpha removed?" and another asking whether the free unlimited listing had just been pulled.

The promotional ID stops resolving while the paid one keeps working. This is the most insidious version, because it is a naming problem rather than a capability problem. When a promotion ends, the suffixed identifier that your code references can start returning errors while the underlying model is still there, still serving, under a different string. Nothing about the model changed. Your integration broke anyway.

The ID survives and throttles to uselessness. The quiet one. A user on r/SillyTavernAI described trying to use a free tier of a model they liked and getting rate limited even when barely using it, unable to get a single message through. From the application's point of view this is not an outage, it is elevated latency and intermittent errors, which is exactly the shape that no one alerts on.

The quota side of the same story

Free promotional capacity is one half of a bigger pattern. The other half is that paid subscription quotas have become the binding constraint for heavy users, and our corpus shows that pressure rising: comparing the week of 15 to 21 August against 22 to 28 August, rate limit chatter went from 129 mentions to 218, and provider outage mentions rose from 42 to 73.

The volume numbers underneath are extraordinary. One developer reported 6.8 billion lifetime tokens on a coding agent with a 726 million token peak day, and said they can exhaust a weekly allowance in under 24 hours of active work. Another described a harness that consumed 500 to 600 million tokens in two days through uncontrolled agent loops, mostly cache reads, and published the full support transcript after being offered only a pro rata credit.

The friction shows up in smaller ways too. A user on r/ClaudeAI reported burning 23% of a five hour limit on "server is busy" responses that produced no output. Another asked whether two 5x accounts beat one 20x account for weekly headroom. A consultant averaging three quarters of a billion tokens a month asked about migrating from a max plan to enterprise purely for limits. On the other side, OpenAI reset Codex weekly limits repeatedly through the month and signalled that the five hour limit is returning for some plans.

Put the two halves together and you get the defining operational fact of this moment: capacity is abundant, cheap and unreliable at the same time. There is more free and discounted inference available than at any point in the industry's history, and less certainty than ever about whether any specific identifier will serve your next request.

The demand side is asking for the obvious answer. A post on r/openclaw laid out four paid subscriptions plus opportunistic free models and asked whether any tool exists to manage all of them, noting that free models die, logins expire and the maintenance is becoming the work. The same question in a different accent shows up as which subscription should I keep, a post that appeared in at least five subreddits in three days.

The engineering answer

Free capacity is worth using. It should just sit in the part of your architecture designed for things that disappear.

1. Free is a lane, not a plan. Put a paid provider as the primary for anything interactive, and route free capacity to batch, background, evaluation, backfill and internal tooling where a failure is retryable and nobody is waiting. Managed policies let you express that split once, and Requesty maintains a free models surface precisely so this is a routing decision rather than a code branch.

2. Every free primary needs a paid fallback. Fallback policies turn all three failure modes above into a latency blip. The ID retires, the request completes on the next candidate. This single control would have made the Ox Alpha retirement invisible to end users. The general pattern is in why rate limits happen and how multi provider fallback fixes them, and the outage version is in how to fail over in two minutes.

3. Cap what a free lane can consume. In flight rate limits and API limits stop a throttling free provider from holding your entire concurrency budget in slow requests. This is the control that prevents a promotional endpoint from degrading traffic that was never routed to it.

4. Alert on per model error rate, not just on spend. A free tier that silently starts failing 40% of requests is invisible on a cost dashboard because it costs nothing. Alerts plus performance monitoring and error codes are what make a quiet degradation loud.

5. Never reference a promotional identifier from application code. Reference an intent. When a suffixed promotional ID stops resolving, you want to edit one policy, not grep your services. This is the same discipline the stealth launch demanded, and we made the full case in the Ox Alpha post.

6. Know your cache hit rate before you attribute any saving to the free tier. Given that 86% of agentic token burn is cached input, a free model behind a broken cache can cost more in wall clock and retries than a paid model behind a working one. Automatic caching first, then measure.

What to expect next

The promotional window is not a phase. It is what competition looks like when five labs ship comparable open weight capability in nine days and none of them can win on capability alone. Expect more free windows, more stealth aliases, more discounts with published end dates, and more model identifiers that exist for a fortnight.

That is good for cost and good for evaluation. It is only bad if your production path assumes any of it is permanent.

Build the indirection once and the churn becomes free optionality instead of an incident queue. See what is currently live and what it costs across every provider in the models catalog and the cheapest rankings, or compare the routing and reliability surface against OpenRouter, LiteLLM and Vercel AI Gateway.

Start routing on Requesty and use every free window without betting on it.