🤗 Hugging Face    |   🤖 ModelScope    |   🐙 ling.tbox.cn    |    Tech Report Today, we announce the official open-source release of **Ling-2.6-flash**, an **instruct model** with **104B total parameters** and **7.4B active parameters**. As agent capabilities mature, skyrocketing **token consumption** has become a primary barrier to deployment. Unlike standard chat, agent workflows involve massive inputs and complex, multi-step execution, driving up both compute demand and user costs. While the industry is pivoting toward "long-reasoning" to push performance ceilings, a critical question remains: Are these excessive reasoning tokens truly necessary for high-frequency, everyday agent use cases?

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Ling-3.0-flash-base

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Ling-3.0-flash-dspark

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Ling-3.0-flash-Fin

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Ling-3.0-flash-Fin-fp4

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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.

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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.

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