Alibaba (Qwen) · language model
Qwen3.8 Omni Flash: pricing, context and benchmarks
Qwen3.8 Omni Flash is a language model from Alibaba (Qwen) released on September 21, 2026. It costs $0.15 per million input tokens and $0.47 per million output tokens, with a 1M-token context window. No independent score has been published for it so far.
| Price | 23/30 | |
|---|---|---|
| Context | 12/15 | |
| Features | 11/15 |
What you need to know about Qwen3.8 Omni Flash
Qwen3.8 Omni Flash was released by Alibaba (Qwen) (China) on September 21, 2026. It accepts text, images, audio, and video as input and produces text. Its context window reaches 1,000,000 tokens, larger than 77 % of the models in the registry, and it can write up to 131,072 tokens in one go.
It is a recent model: real-world feedback and independent scores are still scarce.
Qwen3.8 Omni Flash pricing
On OpenRouter, Qwen3.8 Omni Flash costs $0.15 per million input tokens and $0.47 per million output tokens. Counting three tokens read for every token written, its average price is $0.23 per million, more expensive than 23 % of paid models (3.3 times cheaper than the median of $0.76).
A concrete example: a task that sends 2,000 tokens (about 1,500 words) and receives a 500-token answer costs $0.0005. At 1,000 tasks a day, budget $16 a month. The price calculator redoes this with your own volumes.
Tokens already sent and cached are billed at $0.016 per million.
Performance and benchmarks
Epoch AI does not yet publish a capabilities index for Qwen3.8 Omni Flash. We therefore show no made-up performance rating: only price, context and features count towards its score.
Features, privacy and local use
- It supports tool calling, so it can drive an AI agent, call APIs or MCP servers.
- It can answer in an enforced JSON format (structured outputs).
- It is a reasoning model: it "thinks" before answering, which helps on hard tasks but uses more output tokens.
Its weights are not published: it can only be used through the Alibaba (Qwen) API or a broker such as OpenRouter, which means sending your data off your machines.
Other Alibaba (Qwen) models
| Model | Released | Avg. price | ECI |
|---|---|---|---|
| Qwen3.8 Max Prime | Sep 23, 2026 | $6 | — |
| Qwen3.8 Max (0902) | Sep 3, 2026 | $3 | 155.1 |
| Qwen3.8 Flash | Aug 26, 2026 | $0.23 | — |
| Qwen3.8 27B | Aug 14, 2026 | $0.78 | 151.7 |
| Qwen3.8 2.4T A95B | Aug 12, 2026 | $3 | — |
| Qwen3.7 Flash | Jul 27, 2026 | $0.06 | 144.1 |
| Qwen3.7 Plus | Jun 3, 2026 | $0.56 | 147 |
| Qwen3.7 Max | May 21, 2026 | $2.21 | 154 |
Verdict
Qwen3.8 Omni Flash scores 77/100 on our grid, which only uses published figures.
Strengths
- Low price: cheaper than 77 % of paid models.
- Very large context (1M tokens): long documents or a whole codebase fit in one request.
- Agent-ready: tool calling and structured JSON output.
Limitations
- No independent score published: performance cannot be verified.
Avoid if…
- your data must not leave your servers.
Alternatives
- Claude Haiku 5.5$0.2
- Mercury 3$0.16
- GPT-4.1 Nano$0.18
- Solar Pro 4$0.26
- DeepSeek V4 Pro 0423$0.26
- GLM 5.3 Flash$0.24
Comparisons and tools
Learn more
Frequently asked questions
How much does Qwen3.8 Omni Flash cost?
$0.15 per million input tokens and $0.47 per million output tokens on OpenRouter (checked October 25, 2026). A task with 2,000 tokens read and 500 written costs $0.0005.
How big is Qwen3.8 Omni Flash's context window?
1,000,000 tokens, roughly 750,000 words, with a maximum of 131,072 output tokens.
Can Qwen3.8 Omni Flash run locally?
No, Alibaba (Qwen) does not publish its weights: it is API-only.
Is Qwen3.8 Omni Flash suitable for an AI agent?
Technically yes: it supports tool calling and structured outputs. Then check its cost for your volume.
What are the alternatives to Qwen3.8 Omni Flash?
The closest models on price and performance are Claude Haiku 5.5, Mercury 3, and GPT-4.1 Nano.
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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score