Intelligence
19 scoreAlibaba · Flagships Analysis
Qwen3.5 Omni Flash
Intelligence, Performance & Price Analysis
Canonical slug: qwen3-5-omni-flash · Canonical model registry at build time
Speed
262.8 output tokens/secLatency
1.90s TTFTInput Price
$0.10 / 1M tokensOutput Price
$0.80 / 1M tokensExecutive Assessment
Routing Verdict & Tradeoffs
Ethen Routing Verdict
Capable everyday model
Qwen3.5 Omni Flash scores 19 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other non-reasoning models in a similar price tier (median: 11). Qwen3.5 Omni Flash generates output at 262.8 tokens per second (based on Alibaba's API), which is well above average compared to other non-reasoning models in a similar price tier (median: 95.5 t/s). Qwen3.5 Omni Flash costs $0.10 per 1M input tokens (very competitive, median: $0.20) and $0.80 per 1M output tokens (somewhat higher than average, median: $0.70), based on Alibaba's API.
Good for routine tasks; route complex reasoning and premium workloads to stronger models.
Task Fit Assessment
| Workload | Rating | Notes |
|---|---|---|
| Complex reasoning & agentic workflows | viable | Intelligence score 19 handles routine reasoning but may struggle with open-ended agentic tasks. |
| High-volume chat & customer-facing | optimal | Output speed 262.8 tokens/sec and capable intelligence make this suitable for real-time chat at scale. |
| Latency-sensitive applications | optimal | TTFT 1.90s — adequate latency for most interactive use cases. |
| Cost-sensitive pipelines | optimal | Output pricing at $0.80 is reasonable for moderate volume. |
Cost Pressure Analysis
Low
Pricing is competitive — input $0.10, output $0.80. Suitable for sustained production use.
Technical Specifications
Architecture and Limits
| Specification | Value | Validation Authority |
|---|---|---|
| Model type | Proprietary | inferred |
| Reasoning | No | faq |
| Input modalities | Qwen3.5 Omni Flash supports text, image, speech, and video input. | faq |
| Output modalities | Qwen3.5 Omni Flash supports text and speech output. | faq |
| Context window | 260k tokens | faq |
| Open weights / source | No, Qwen3.5 Omni Flash is proprietary. The model weights are not publicly available. | faq |
| Parameters | Qwen3.5 Omni Flash is a proprietary model and Alibaba has not disclosed the model size or parameter count. | faq |
| API availability | Yes, Qwen3.5 Omni Flash is available via API through 1 provider. | faq |
Evidence charts
Profile visualizations
Charts are the same canonical evidence cards previously published for this slug, contained inside the D18D page grammar.
AA-Omniscience Index
AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct. · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Intelligence
Artificial Analysis Intelligence Index · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Output Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
End-to-End Response Time
Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Latency: Time To First Answer Token
Seconds to first answer token received · Accounts for reasoning model 'thinking' time · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Latency includes thinking / reasoning time when applicable. Lower is better for interactive use cases.
Context Window
Context window: tokens limit · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Larger context windows enable complex multi-turn tasks but may increase latency and cost for long inputs.
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens) · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Time per Intelligence Index Task
Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Methodology
Methodology & Provenance
This page is rendered from the normalized profile and page JSON for Qwen3.5 Omni Flash.
Benchmark values are preserved as normalized; only layout, disclosure ordering, and typography are adjusted for readability.
Frequently Asked Questions
Model FAQs & Technical Disclosures
Qwen3.5 Omni Flash was released on March 30, 2026.
Qwen3.5 Omni Flash was created by Alibaba.
Qwen3.5 Omni Flash scores 19 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other non-reasoning models in a similar price tier (median: 11).
Qwen3.5 Omni Flash generates output at 262.8 tokens per second (based on Alibaba's API), which is well above average compared to other non-reasoning models in a similar price tier (median: 95.5 t/s).
Qwen3.5 Omni Flash has a time to first token (TTFT) of 1.90s (based on Alibaba's API), which is somewhat higher than average compared to other non-reasoning models in a similar price tier (median: 1.53s).
Qwen3.5 Omni Flash costs $0.10 per 1M input tokens (very competitive, median: $0.20) and $0.80 per 1M output tokens (somewhat higher than average, median: $0.70), based on Alibaba's API.
Qwen3.5 Omni Flash costs $0.10 per 1M input tokens and $0.80 per 1M output tokens (based on Alibaba's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.17 per 1M tokens. Pricing may vary by provider.
No, Qwen3.5 Omni Flash is not a reasoning model. It provides direct responses without extended chain-of-thought reasoning.
Qwen3.5 Omni Flash supports text, image, speech, and video input.
Qwen3.5 Omni Flash supports text and speech output.
Yes, Qwen3.5 Omni Flash supports image input and can analyze, describe, and answer questions about images.
Yes, Qwen3.5 Omni Flash is multimodal. It can process text, image, speech, and video input and generate text and speech output.
Qwen3.5 Omni Flash has a context window of 260k tokens. This determines how much text and conversation history the model can process in a single request.
No, Qwen3.5 Omni Flash is proprietary. The model weights are not publicly available.
Qwen3.5 Omni Flash is a proprietary model and Alibaba has not disclosed the model size or parameter count.
Qwen3.5 Omni Flash achieves a score of 19 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Yes, Qwen3.5 Omni Flash is available via API through 1 provider.
Qwen3.5 Omni Flash is available through 1 API provider.