Discounted @AskVenice API keys. Prices fall all day and expire at midnight UTC.

GPT-6 Sol & Luna are now on CheapTokens, anonymously.
GPT-6 Sol & Luna are now on Venice, anonymously.
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Long weekends are for big builds! 🤩
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Tokens are looking cheap today! 😀
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These Venice AI tokens expire at midnight UTC. That's why they're more than 60% off right now! How many tokens can you burn before then?
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Opus 5 and Fable are live and currently discounted by 75%! But remember they will expire in ~3 hours. Enjoy the deals! 😀
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Wonderful to see such talented artists creating with us!
Replying to @0xatd
I’m building Roxy Vane episode 1! piped.video/1qRlwUg7W3I?is=VBXV…
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Who is building on Sunday? 🏗️
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currently 70% off AI API inference. by the time you read this, more. get the deal before they expire!
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If your tool can point at an OpenAI-compatible endpoint, testing Venice AI should be a config change, not a migration project. CheapTokens gives builders a bounded key for real workload tests.
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Same-Day Credits are intentionally simple: buy discounted Venice API credits, run today's workload, and use them before the midnight UTC reset.
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CheapTokens is for bursty Venice AI workloads: evals, batch summaries, agent runs, media tests. Buy credits for the work you need today, then check usage from your account page. cheaptokens.ai
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Reserve credits are for recurring inference you want to keep simple. Keep one API key, top up the balance, choose the daily capacity, and track usage from the account page.
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A useful inference-credit supplier should disappear into your existing client config: base URL, key, model, usage page. CheapTokens keeps that surface explicit so experiments do not become accounting work.
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Before a batch run, do a small CheapTokens smoke test: set the base URL, use the issued key, make one request, then check usage visibility. If that loop is clear, the larger job is easier to run cleanly.
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For launch prep, separate inference supply from application logic. Use a CheapTokens key for the finite run, keep your client configuration explicit, and check usage after the job. That makes the cost surface easier to inspect.
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For model experiments, keep the inference-supply step explicit: choose daily capacity, use the issued CheapTokens key, run the test, then check remaining usage before the next reset. That makes the run easier to budget and audit.
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If you are evaluating CheapTokens, start with the integration surface: OpenAI-compatible client, CheapTokens base URL, issued key, status page for usage. No new app architecture required.
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Before you swap inference suppliers, check the boring parts: Can your existing client use the API path? Do you get a funded key immediately? Can you see remaining usage? That is the checklist CheapTokens is built around.
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For agent tests, the useful unit is not just price per token. It is whether the run has a funded key, a clear daily limit, and a place to check remaining usage. CheapTokens is built around that operating loop.
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Agent budgets work better when purchasing and usage are part of the same loop. CheapTokens is built around that: buy daily API capacity, use the issued key, then verify remaining usage before the reset window.
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