$6.00 → $0.60 per 1M tokens: a routing scenario, not a guaranteed saving.
Send the easy 80% of requests to a small model and the blended cost drops 10×. Your result depends on your traffic mix and both models' prices.
Exactly! The interesting question is how much further costs can drop with open models while maintaining the same classification accuracy.
Do you have a concrete pricing example for this kind of workload on Batchrate? We’d love to compare it on Obolith.
If a job doesn't need an answer in seconds, why pay full price per token?
OpenAI, Anthropic and Google all run a batch API at 50% off.
The trade: no low-latency response. Completion is usually within 24 hours, often much sooner.
Example workload: 2M classification calls a month, 600 in + 20 out tokens each, on the batch API (−50%).
Full breakdown in the guide: obolith.com/guides/batch-a…
5/5 So recheck your model. When did you last check your model's output rate?
Output price per 1M tokens for each model, collected daily: obolith.com/llm-api-pricin…
4/5 The counterpoint: Qwen3 14B output went $0.91 → $0.22 per 1M tokens (-76%).
Prices move both ways. Even inside one family, the direction differs by model.
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+20% in 7 days: RunPod A40 and L4 on-demand went from $0.49/h to $0.59/h (2026-10-03 → 2026-10-10).
For the A40, Vast.ai (marketplace) is at $0.469/h.
How often do you recheck your GPU hourly rate?
4/4 B200 192GB: Hyperstack $6.00/h to RunPod $7.99/h. That's 1.3×.
Cheapest tracked provider vs priciest: 25% less on B200, 96% less on V100.
What would justify paying more for the same GPU?
Full provider-spread breakdown: obolith.com/guides/same-gp…
1/4 V100: 28× between the cheapest and priciest tracked providers. B200: 1.3×.
Same GPU, same day, very different invoice.
1 GPU on-demand, prices as of 2026-10-10.
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