Qwen3.5-Flash is the hosted, production-grade API version of Alibaba's Qwen3.5-35B-A3B open-weight model. While the underlying open-weight model carries 35 billion total parameters, it activates only around 3 billion per forward pass — a characteristic of its sparse Mixture-of-Experts (MoE) design that keeps inference fast and affordable without meaningful quality sacrifice.
The "Flash" name signals where this model sits in the lineup: it is optimized for throughput, low latency, and cost at scale. It ships with a 1M-token context window by default, native multimodal support for text, images, and video, built-in tool-calling, and configurable chain-of-thought reasoning. These are not experimental features — they are production defaults, ready to use from the first API call.
Qwen3.5-Flash does not follow a standard dense transformer architecture. It uses a hybrid design that combines two distinct attention mechanisms in a fixed 3:1 ratio — three linear attention layers for every one full self-attention layer.
Compresses context into fixed-size recurrent states rather than growing KV-caches. This dramatically reduces memory overhead, especially for very long sequences. Processing a 500K-token document costs roughly 3–4x more than a 50K document — not 100x as with standard attention.
Each token is routed to a small subset of specialized "expert" sub-networks within the 35B parameter space. With only 8.6% of total parameters active per forward pass, the model achieves GPT-5-mini-class reasoning at a fraction of the raw compute cost.
Text, images, and short video segments are processed in a single forward pass. There are no separate vision adapters or preprocessing pipelines — the model was trained from scratch on multimodal tokens using early fusion, allowing natural cross-modal reasoning.
Callers can dial reasoning intensity up or down per request. At low settings the model behaves as a fast instruction-follower; at higher settings it performs multi-step chain-of-thought decomposition suited for math, coding, and agentic planning tasks.
Pass entire codebases, document collections, or agent state histories in a single request — no chunking, no RAG pipeline required for most workloads.
Include short video clips, screenshots, and text in the same prompt thread. Natively understood — not converted or approximated by a separate model.
Reasoning traces persist across conversation turns, reducing redundant computation in iterative development workflows and multi-step planning tasks.
Strong multilingual coverage across 201 languages, with consistent instruction-following quality whether the user writes in English, Arabic, Japanese, or Estonian.
The model was evaluated across a range of standard benchmarks. Headline results on SWE-bench Verified place it squarely in the frontier-adjacent tier, outperforming the previous-generation Qwen3.5-397B-A17B on major coding benchmarks despite being far smaller.
| Benchmark | Score | Context |
|---|---|---|
| SWE-bench Verified | 77.2% | Autonomous coding task resolution |
| Terminal-Bench 2.0 | 59.3% | Shell and CLI reasoning tasks |
| MMLU-ProX (29 langs) | Strong | Multilingual knowledge & reasoning |
| Vals Index accuracy | 49.9% | Cross-domain agentic accuracy index |
Legal, finance, and research teams processing reports, contracts, or filings that exceed standard context windows. The 1M context eliminates most chunking pipelines entirely.
Engineers building multi-step tool-use agents that require consistent structured output, long state histories, and reliable multi-turn reasoning — without the cost of a tier-1 model.
Applications running millions of requests per month where per-token cost is the dominant budget constraint. At $0.13/M input, previously cost-prohibitive workloads become viable.
Teams that want full data control. The open-weight 35B-A3B base model runs on consumer-grade hardware (8GB+ VRAM), deployable via vLLM, SGLang, or llama.cpp with no per-token fees.
Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.
Code assistance and agentic workflows: Alibaba Cloud says Qwen3.5-Flash “shines in coding assistance” and “agentic workflows,” including fixing code autonomously and operating desktop applications.
Visual understanding: Alibaba Cloud documents Qwen3.5-Flash as a native vision-language model for visual understanding, and its model lifecycle notes mention analyzing charts and long videos.
Text generation and reasoning: Alibaba Cloud lists Qwen3.5-Flash under text generation with thinking mode and supports it for reasoning, making it suitable for general text-generation tasks that require structured output and tool use.
Multimodal document workflows: Public listings describe the model as accepting text, images, and video, and position it for multimodal document workflows that combine mixed text, image, and video inputs.
import requests r = requests.post( "https://api.aimlapi.com/v1/chat/completions", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "alibaba/qwen3.5-flash", "messages": [ { "role": "user", "content": "Hello!" } ] }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "alibaba/qwen3.5-flash", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"alibaba/qwen3.5-flash","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
Qwen3.5-Flash This page | Reasoning + agents | |||
| Reasoning + agents | ||||
| Balanced coding + agents | ||||
| Long-context, multimodal & agentic workflows | ||||
| Reasoning + agents |
Qwen3.5-Flash has a 1,000,000 tokens context window and can return up to 65,536 tokens.
Qwen3.5-Flash takes image, text as input and returns text.
Use alibaba/qwen3.5-flash as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
Qwen3.5-Flash became available on April 21, 2026.
Qwen3.5-Flash is priced at input $0.13 / 1M tokens, output $0.52 / 1M tokens.
Yes, Qwen3.5-Flash can stream responses as they are generated.
Yes, Qwen3.5-Flash accepts image input alongside text.
Qwen3.5-Flash was built by Alibaba Cloud.
Yes, it supports tool calling, including parallel tool calls.
No, its supported modalities are image and text input to text output; video is not listed.
