---
title: Qwen
slug: alibaba-qwen
url: "https://toolweight.com/options/alibaba-qwen"
homepage: "https://qwen.ai"
categories: llm-apis
last_verified: 2026-01-15
license: CC-BY-4.0
---

# Qwen

> Alibaba's model family, huge open-weight range, closed flagship

Qwen is the most prolific open-weight family in the category, spanning tiny edge models to mixture-of-experts systems, with the top-end Max tier held back as API-only. Served through Alibaba Cloud Model Studio, which brings real regional coverage including Singapore and Sydney, and an OpenAI-compatible endpoint that makes evaluation cheap.

## Identity

|  |  |
| --- | --- |
| Name | Qwen |
| Company | Alibaba Cloud |
| One-liner | Alibaba's model family, huge open-weight range, closed flagship |
| Site | https://qwen.ai |
| Docs | https://www.alibabacloud.com/help/en/model-studio |
| Founded | 2009 |
| Open source | Yes |
| Licence | Apache-2.0 (most open models) |
| Brand | https://toolweight.com/vendors/Alibaba Cloud |
| Compared in | 1 |

## Where it is compared

### [LLM APIs](https://toolweight.com/compare/llm-apis)
Ranked **#5 of 18** on default weights.
| Field | Value | Confidence | Verified | Source | Note |
| --- | --- | --- | --- | --- | --- |
| Flagship model | Qwen3-Max | Inferred | 2026-01-15 | - | The Max tier is API-only; the open-weight Qwen3 models sit a tier below. |
| Context window | 262,144 tokens | Inferred | 2026-01-15 | - | - |
| Max output | - | Unknown | - | - | - |
| Image input | ◐ | Inferred | 2026-01-15 | - | Via the Qwen-VL line rather than the flagship text model, which is precisely what this column calls partial, and how Cohere and Z.ai are graded for the same arrangement. The note and the grade now agree. |
| Audio in/out | ◐ | Inferred | 2026-01-15 | - | Qwen-Audio and Omni variants exist as separate models. |
| Open weights | ◐ | Vendor-claimed | 2026-01-15 | https://github.com/QwenLM/Qwen3 | The most prolific open-weight release programme in the category, but the Max flagship is closed. |
| OpenAI-compat API | ● | Vendor-claimed | 2026-01-15 | https://www.alibabacloud.com/help/en/model-studio/compatibility-of-openai-with-dashscope | - |
| TTFT p50 | - | Unknown | - | - | - |
| Output tok/s | - | Unknown | - | - | - |
| $/M input | $1.2 /M tok | Inferred | 2026-01-15 | - | Model Studio pricing is tiered by prompt length; this is the short-context rate and longer prompts cost materially more. |
| $/M output | $6 /M tok | Inferred | 2026-01-15 | - | - |
| $/M cache read | - | Unknown | - | - | - |
| Batch discount | 50 % | Inferred | 2026-01-15 | - | Model Studio offers a discounted batch mode. |
| Tool use | ● | Inferred | 2026-01-15 | - | - |
| Schema output | ◐ | Inferred | 2026-01-15 | - | - |
| Effort control | ◐ | Inferred | 2026-01-15 | - | Thinking can be toggled on the hybrid models; no graduated levels. |
| Computer use | ○ | Inferred | 2026-01-15 | - | - |
| MCP support | ◐ | Inferred | 2026-01-15 | - | - |
| Cache TTL | - | Unknown | - | - | - |
| Continuity policy | Model Studio exposes dated snapshot IDs alongside rolling aliases, so pinning is possible if you use the dated form. Deprecation notices appear in the console rather than as a public policy page, and the cadence of new Qwen releases means aliases move quickly. Open-weight tiers remove the problem entirely. | Inferred | 2026-01-15 | - | - |
| Notice period | - | Unknown | - | - | - |
| Pinnable versions | ● | Inferred | 2026-01-15 | - | Dated snapshot IDs are published for most models. |
| Zero retention | - | Unknown | - | - | - |
| Trains on your data | ◐ | Inferred | 2026-01-15 | - | - |
| AU region | ◐ | Inferred | 2026-01-15 | - | Alibaba Cloud operates a Sydney region, but Model Studio availability there should be confirmed before relying on it. |
| API since | 2023 | Inferred | - | - | - |
| Positioning | The widest open-weight family, plus a closed flagship tier | Inferred | - | - | - |

**Verdict.** The best open-weight range in the category, there is a Qwen model at nearly every size and modality, mostly Apache 2.0. The hosted Max tier is a reasonable mid-price flagship but rarely the reason to be here; most teams use the open weights through a Western host and treat Model Studio as optional.

## Alternatives

- [Amazon Bedrock](https://toolweight.com/options/amazon-bedrock), Multi-vendor model access inside your existing AWS account
- [Anthropic](https://toolweight.com/options/anthropic), Claude models, built around long agentic runs and tool use
- [Cerebras](https://toolweight.com/options/cerebras), Wafer-scale inference, the fastest tokens per second available
- [Cohere](https://toolweight.com/options/cohere), Enterprise-focused models built for RAG and private deployment
- [DeepSeek](https://toolweight.com/options/deepseek), Frontier-adjacent models at a small fraction of Western prices
- [Fireworks AI](https://toolweight.com/options/fireworks-ai), Fast open-weight inference with strong structured-output support
- [Google Gemini](https://toolweight.com/options/google-gemini), Gemini via AI Studio for prototyping or Vertex AI for production
- [Groq](https://toolweight.com/options/groq), Custom LPU silicon serving open-weight models at extreme speed
- [Meta Llama](https://toolweight.com/options/meta-llama), Open-weight Llama models, hosted almost everywhere but Meta
- [Mistral AI](https://toolweight.com/options/mistral-ai), European lab with an open-weight lineage and EU-resident hosting
- [Moonshot AI](https://toolweight.com/options/moonshot-ai), Kimi models, open-weight agentic performance at low cost
- [OpenAI](https://toolweight.com/options/openai), GPT models plus audio, images and embeddings on one bill

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