Mossez-100M-Base
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From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.
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Every model measured with the SPARTA framework, together with the benchmark CSVs and the compiler reports that describe them. **Code:** https://github.com/mouad-zouhdi/sparta-edgetpu The repository README defines every column of every CSV published here. The framework measures how structured pruning behaves once a model actually runs on a Google Coral Edge TPU: latency, accuracy, and the compiler's split between parameters cached in on-chip memory and parameters streamed from the host.
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Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.