qwen
Qwen: Qwen3.5 Plus 2026-02-15
qwen/qwen3.5-plus-02-15
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
Qwen: Qwen3.5 Plus 2026-02-15 (qwen/qwen3.5-plus-02-15) is a vision-language model listed in the OpenRouter catalog, accepting text, image, and video input and producing text output, with a reported context window of about one million tokens. The catalog description attributes to it a hybrid architecture combining linear attention with sparse mixture-of-experts, aimed at higher inference efficiency. Tool calling and structured output parameters are also advertised. Only catalog data was available; no independent model card was included in this source pack.[1]
Strengths
- Reported multimodal input: the catalog lists text, image, and video inputs with text output.[1]
- A context window of roughly one million tokens is reported, which could suit long documents or multi-file inputs.[1]
- The catalog describes a hybrid linear-attention plus sparse mixture-of-experts design for more efficient inference, and lists tool calling, structured output, and logprob parameters.[1]
Limitations
- All evidence comes from the OpenRouter catalog; no independent model card, license terms, or hardware requirements were available in this source pack.[1]
- Reasoning support is unclear: the listing exposes reasoning-related parameters but reports no supported effort levels or defaults.[1]
- No independent benchmark suite is reported, and the catalog's design arena results cover only narrow creative categories; real-world performance requires workload validation.[1]
Where to use it
- May suit long-context document analysis or multimedia pipelines that combine images or video with text prompts, given the reported inputs and context window.[1]
- Could be evaluated for tool-using agents or structured-output applications, since those parameters are advertised; verify behavior before production use.[1]
Sources
- [1] OpenRouter catalog · Qwen: Qwen3.5 Plus 2026-02-15Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts models, achieving higher inference efficiency. In a variety of...
Specifications
- Model ID
- qwen/qwen3.5-plus-02-15
- Canonical ID
- qwen/qwen3.5-plus-20260216
- Creator
- qwen
- Added to OpenRouter
- 16.02.2026
- Context length
- 1,000,000
- Modality
- text+image+video->text
- Input modalities
- text, image, video
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $0.260 / out $1.56
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | No measurement | reasoning: optional · parameters: include_reasoning, reasoning |
| Coding | No measurement | — |
| Agents | No measurement | parameters: structured_outputs, tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: optional · parameters: include_reasoning, reasoning |
| Document analysis | No measurement | — |
| Vision | No measurement | input: image |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | input: video |
| Embeddings | No measurement | — |
Evaluations
| Source | Benchmark | Value | Grade | Data retrieved |
|---|---|---|---|---|
| Design Arena | models · 3dwin rate 47.7% · rank 93 | 1,102Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · asciiartwin rate 43.2% · rank 66 | 1,089Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · codecategorieswin rate 48.4% · rank 83 | 1,157Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · datavizwin rate 44.8% · rank 90 | 1,133Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · gamedevwin rate 42.6% · rank 98 | 1,096Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · svgwin rate 48.9% · rank 67 | 1,099Elo | CC — below the B band | 13.11.2026 |
| Design Arena | models · uicomponentwin rate 52.2% · rank 73 | 1,170Elo | BB — next 30% of the cohort | 13.11.2026 |
| Design Arena | models · websitewin rate 50% · rank 78 | 1,180Elo | BB — next 30% of the cohort | 13.11.2026 |
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| Alibaba | 1,000,000 | $0.260 | $1.56 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.