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Model comparison - Same provider, successor

Qwen3.7-Flash vs Qwen3.8-Flash

Qwen3.7-Flash and Qwen3.8-Flash are both Alibaba Cloud (Qwen) models. On list price, Qwen3.8-Flash charges 5x Qwen3.7-Flash's input price ($0.15 vs $0.03 per 1M tokens), and Qwen3.8-Flash charges 3.6x Qwen3.7-Flash's output price ($0.47 vs $0.13 per 1M tokens). Qwen3.7-Flash and Qwen3.8-Flash both have a 1,000,000-token context window, and both return up to 131,072 output tokens per response. Qwen3.7-Flash was released on July 21, 2026 and Qwen3.8-Flash on August 26, 2026. Both are closed-weights models, available only through an API.

Last verified from each provider's pricing page. All sources

Alibaba Cloud (Qwen) - Closed

Qwen3.7-Flash
Input / 1M
$0.03
Output / 1M
$0.13
Context
1M

Alibaba Cloud (Qwen) - Closed

Qwen3.8-Flash
Input / 1M
$0.15
Output / 1M
$0.47
Context
1M

Side-by-side specs

SpecQwen3.7-FlashQwen3.8-Flash
ProviderAlibaba Cloud (Qwen)Alibaba Cloud (Qwen)
Input / 1M tokens$0.03$0.15
Output / 1M tokens$0.13$0.47
Cache read / 1M$0.003$0.016
Cache write / 1M$0.038$0.20
Long-context rates above32,000 tokens-
Context window1,000,0001,000,000
Max output131,072131,072
Release date
WeightsClosedClosed
API model idqwen3.7-flashqwen3.8-flash
AI Gateway idalibaba/qwen3.7-flashalibaba/qwen3.8-flash
Last verified

Prices in USD per 1M tokens at each provider's standard first-party rate. A dash means the provider does not bill that item separately or no primary page states it.

What each costs in practice

The same three workloads every model page uses, worked from each model's standard list rates. Swap in your own token counts with the math on each model's page.

WorkloadQwen3.7-FlashQwen3.8-Flash
One agent session1M input tokens and 100K output tokens, no caching.$0.043$0.197
A chat app10,000 requests a day at 2K input and 500 output tokens each.$1.25$37.50 per 30-day month$5.35$160.50 per 30-day month
The same session at a 90% cache hit90% of the input is read from the prompt cache. The one-time cache write premium is not included.$0.0187$0.0764
Show the math

Qwen3.7-Flash

  • One agent sessionInput: 1M x $0.03 = $0.03Output: 0.1M x $0.13 = $0.013
  • A chat appInput: 20M x $0.03 = $0.60Output: 5M x $0.13 = $0.65
  • The same session at a 90% cache hitCached input: 0.9M x $0.003 = $0.0027Uncached input: 0.1M x $0.03 = $0.003Output: 0.1M x $0.13 = $0.013

Qwen3.8-Flash

  • One agent sessionInput: 1M x $0.15 = $0.15Output: 0.1M x $0.47 = $0.047
  • A chat appInput: 20M x $0.15 = $3.00Output: 5M x $0.47 = $2.35
  • The same session at a 90% cache hitCached input: 0.9M x $0.016 = $0.0144Uncached input: 0.1M x $0.15 = $0.015Output: 0.1M x $0.47 = $0.047

Benchmarks under the same setting

No board in the benchmark directory lists both Qwen3.7-Flash and Qwen3.8-Flash under the same setting, so there is no like-for-like score to show here.

Set them up in your tools

Step-by-step guides whose official docs list Qwen3.7-Flash or Qwen3.8-Flash by model id.

Model pages

FAQ

How do Qwen3.7-Flash and Qwen3.8-Flash compare on price?

On list price, Qwen3.8-Flash charges 5x Qwen3.7-Flash's input price ($0.15 vs $0.03 per 1M tokens), and Qwen3.8-Flash charges 3.6x Qwen3.7-Flash's output price ($0.47 vs $0.13 per 1M tokens). For one agent session of 1M input and 100K output tokens with no caching, that is $0.043 on Qwen3.7-Flash and $0.197 on Qwen3.8-Flash. Prices are each provider's first-party list rates, verified October 17, 2026 and October 17, 2026.

What are the context windows of Qwen3.7-Flash and Qwen3.8-Flash?

Qwen3.7-Flash and Qwen3.8-Flash both have a 1,000,000-token context window, and both return up to 131,072 output tokens per response.

Are Qwen3.7-Flash and Qwen3.8-Flash open weights?

Qwen3.7-Flash is closed-weights and is called through Alibaba Cloud (Qwen)'s API as qwen3.7-flash. Qwen3.8-Flash is closed-weights and is called through Alibaba Cloud (Qwen)'s API as qwen3.8-flash.

More comparisons

Sources

Qwen3.7-Flash verified from alibabacloud.com; Qwen3.8-Flash verified from alibabacloud.com. This page is as current as the older of the two: .

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