Skip to main content
Current modelAlibaba Qwen

Qwen3.8-Omni-Flash benchmarks, pricing and specs

Qwen3.8-Omni-Flash is an Alibaba Qwen model released on September 20, 2026 that takes text, image, audio and video input and produces text. It costs $0.15 per 1M input tokens and $0.47 per 1M output tokens and accepts up to 1,000,000 tokens of context.

Per 1M input tokens
$0.15
Per 1M output tokens
$0.47
Context window
1M

Data verified October 12, 2026 · Dataset 2026-10-12

Share this

Key facts

Provider
Alibaba Qwen
Model family
Qwen3.8
Release date
September 20, 2026
Status
Current
Access
API only
API model id
qwen3.8-omni-flash
Context window
1,000,000 tokens
Max output
131,072 tokens
Input
text, image, audio, video
Output
text
Task families
multimodal, audio
Declared capabilities
reasoning, audio understanding, video understanding, image understanding

Qwen3.8-Omni-Flash pricing

It is the #4 cheapest of 38 current multimodal models priced per 1M input tokens.

UnitPrice (USD)TierAppliesSource
1M input tokens$0.15standardCurrent
1M output tokens$0.47standardCurrent
1M input tokens$0.016cachedCurrent

Independent benchmark results

Scores come from independent evaluators that ran the model themselves, pinned to a dated snapshot. Rank is among current models in the same snapshot. Alibaba Qwen's own launch figures are listed under strengths with the publisher named, because they are not measured the same way.

No independent benchmark has published a result for Qwen3.8-Omni-Flash yet. It is still compared below on price, context and capabilities.

Strengths, cautions and best uses

Strengths

  • An omnimodal model that reads text, images, audio and video in one 1M-token context and answers in text, with thinking, tool calling and web search
  • Alibaba positions it for agents that plan, call tools and deliver finished work from audio-visual material, including up to an hour of combined audio and video
  • Very low international API rates of $0.15 input and $0.47 output per 1M tokens

Cautions

  • Output is text only; for spoken replies Alibaba lists a separate realtime variant
  • Alibaba's comparisons with Gemini 3.8 Flash are self-reported and have not been reproduced independently here
  • The release date is the Alibaba Cloud announcement date, 2026-09-20

Best for

  • Long audio and video understanding at low cost
  • Agent pipelines that must reason over meetings, calls or recorded media

Alternatives to Qwen3.8-Omni-Flash

The closest current multimodalmodels by price. The cheapest model that scores as well or better is listed first; failing that, the cheapest one within 3% of the benchmark's scale.

Compare Qwen3.8-Omni-Flash with

Other Qwen3.8 models

Qwen3.8-Omni-Flash FAQ

How much does Qwen3.8-Omni-Flash cost?

Qwen3.8-Omni-Flash costs $0.15 per 1M input tokens and $0.47 per 1M output tokens at the standard tier. It is the #4 cheapest of 38 current multimodal models priced per 1M input tokens. Source: Alibaba Cloud, verified October 12, 2026.

What is the context window of Qwen3.8-Omni-Flash?

Qwen3.8-Omni-Flash accepts up to 1,000,000 tokens of context. It can return up to 131,072 tokens in one response. That ranks #24 of 41 current multimodal models by context length.

Is Qwen3.8-Omni-Flash open source or API only?

Qwen3.8-Omni-Flash is available through Alibaba Qwen's API only; its weights are not published.

What are the best alternatives to Qwen3.8-Omni-Flash?

The closest current alternatives by price and measured performance are GPT-5.6 Luna (33% more per 1M input tokens, $0.20 vs $0.15), Claude Haiku 5.5 (33% cheaper per 1M input tokens, $0.10 vs $0.15), GPT-4.1 nano (33% cheaper per 1M input tokens, $0.10 vs $0.15) and Gemini 3.1 Flash-Lite (67% more per 1M input tokens, $0.25 vs $0.15).

Sources

Cite this page

Van Data Team. "Qwen3.8-Omni-Flash Benchmarks, Pricing and Specs." AI Model Benchmark Dataset 2026-10-12. https://vandatateam.com/ai-model-benchmarks/qwen3-8-omni-flash

Reuse the data: JSON · CSV · or explore every model in the AI model benchmark comparison.

Production evaluation

Deciding whether Qwen3.8-Omni-Flash fits your workload?

Public benchmarks narrow the shortlist. We test the finalists on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.

  • Evaluation cases and acceptance thresholds tied to real work
  • Model routing, fallbacks and cost controls