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TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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**Keep as much model quality as possible while making Qwen3.8-Flash-Next practical on a system with around 64 GiB of RAM.** Instead of using very low-bit quantization on the main model, this build saves a large amount of space by pruning the huge PLE n-gram table. The saved space is then used to keep the main model weights at much higher precision. The much larger stored parameter count does not mean all of those parameters are calculated for every token.

Text generation
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
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1 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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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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