Disclosure: I work on the zltokens team. The figures below are the monthly cost calculation for this workload, not a promise of identical savings for every project.
Support classification. Field extraction. Short summaries.
The monthly calculation came to $864. My first thought was to find a lower-cost Claude API provider.
Then I looked at the tasks. These features weren't using Claude because we'd compared the options for each one. The project had connected it early, and the later features inherited that choice. It worked, so nobody changed it.
The starting calculation covered about 54,000 model requests per month, averaging 1,700 input tokens and 300 output tokens each. At the Opus 4.6 rates used in this calculation—$5 per million input tokens and $25 per million output tokens—that comes to $864, excluding caching and batch discounts.
When I came across zltokens, the price mattered. But the more useful question was how to split the work: which requests could use a more economical model, and which should stay with Claude?
I started with tasks that had clear acceptance criteria. I didn't test only the easy examples.
"My order still hasn't arrived. I don't want to wait. Please refund it."
If a classifier turns that into "shipping inquiry" and misses the refund request, the saved API cost can come straight back as support rework.
So the rule was simple: move a task only after it passes the original acceptance criteria. Keep the stronger model for difficult requests, and escalate when the first route doesn't handle the request adequately.
Using DeepSeek V4 Pro as the economical candidate, the monthly breakdown is:
• Economical model first: 40,000 calls — $69.
• Direct to Claude: 10,000 calls — $160.
• Escalation calls, already included: 4,000 calls — $65.
• Total: 54,000 model calls — $294.
The escalation cost is already in that total. Don't add it again.
$864 minus $294 is $570 a month, or about 66% less in API charges for this cost calculation.
That is not "send everything to the cheapest model." Complex work still gets the stronger model. Routine work gets tested for a less expensive option that can meet the same requirements.
Testing and configuration take time. But you don't have to replace your entire compatible tool setup. With zltokens, workloads can have separate API keys, model ranges, and quotas, with calls arranged around the agreed rules. Offers on particular models are also worth comparing.
I'd start with one small feature, not a project-wide migration. Keep its acceptance criteria, check the available models and integration path, and work out whether the change makes sense for that task.
Explore the models and API setup on zltokens:
https://www.zltokens.com/en/start?utm_source=dev&utm_medium=organic_social&utm_campaign=story2_202609&utm_content=main
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