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llama-160m

JackFram

This is a LLaMA-like model with only 160M parameters trained on Wikipedia and part of the C4-en and C4-realnewslike datasets. The model is mainly developed as a base Small Speculative Model in the SpecInfer paper.

Text generation

Input Text→ Output Text

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llama-68m

JackFram

This is a LLaMA-like model with only 68M parameters trained on Wikipedia and part of the C4-en and C4-realnewslike datasets. The model is mainly developed as a base Small Speculative Model in the SpecInfer paper. > **Notes on Failed Tasks:** The `Arithmetic` and `SocialIQA` benchmarks failed during execution due to runtime pipeline incompatibilities, yielding no score. Total evaluation runtime was **44.74 minutes**.

Text generation

Input Text→ Output Text

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COST EXPLORER

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Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

MODEL HANDBOOK

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Selection guide & FAQ

How should API prices be compared?+

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.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.