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Qwen: Qwen Image 2.1 Turbo

qwen/qwen-image-2.1-turbo

基准测试比较

Qwen Image 2.1 Turbo is the fast, lower-cost tier of the Qwen Image 2.1 series from Qwen, for image generation and editing where throughput and price matter more than peak fidelity. See Qwen Image 2.1 Pro for the higher-quality tier.

模态
价格
from US$0.0184/image
上下文
66K
发布时间
2026年10月16日
基准测试比较
试验场基准测试提供商定价性能正常运行时间应用活动常见问题探索

Example outputs summary for Qwen: Qwen Image 2.1 Turbo

  • 文生图: Editorial architectural photograph of a contemporary neighborhood storefront at blue hour, glowing warmly against the dusk. Above the entrance, a warm-white neon sign in flowing hand-bent cursive reads exactly "OpenRouter", the only text in the scene. Large windows reveal a cozy, lived-in interior: wood shelves styled with books and ceramics, lush trailing plants, and soft pendant lighting. Potted plants and a bicycle rest by the entrance, and golden light spills across the wet pavement in gentle reflections. Straight-on composition, realistic materials, quiet street with no people. — 使用 Qwen: Qwen Image 2.1 Turbo 生成

试验场

基准测试

标准化评测得分,以及此模型针对共用基准测试提示词的实际输出。百分比越高越好,每项排名显示此模型在该来源所评测的模型中的位置。

Benchmark outputs summary for Qwen: Qwen Image 2.1 Turbo

  • Full wine glass: A wine glass filled to the brim with white wine, the surface level with the rim of the glass, standing upright on a plain wooden table. Straight-on eye-level shot, glass upright and not tilted, no other objects in the frame. (在此提示词中并列第 第 10 名,共 60 名)
  • Closed umbrellas: Wide street-level photograph of about twelve people crossing a city street in heavy rain. Every person is carrying a closed, furled umbrella held down at their side. Not a single umbrella is open. Each umbrella is clearly visible. They are all getting soaked. No other people in the frame. (在此提示词中并列第 第 17 名,共 60 名)
  • Three fingers: Close straight-on photograph of an open right hand raised palm towards the camera, with exactly three fingers extended straight up and the other two folded down into the palm. Plain background, nothing else in the frame. (在此提示词中并列第 第 1 名,共 60 名)
  • Cards & dice: Exactly four playing cards face up in a single straight row on a polished marble counter, evenly spaced, all the same size, showing the seven of hearts, the eight of spades, the nine of diamonds and the ten of clubs from left to right. No other objects in the frame. Straight-on eye-level shot, soft even light. (在此提示词中并列第 第 17 名,共 60 名)
  • Long exact string: A white A4 poster on a brick wall reading exactly "SPRING CLEARANCE - EVERYTHING 40% OFF - SATURDAY 14 JUNE, 9AM TO 6PM". No other text in the frame. (在此提示词中并列第 第 1 名,共 60 名)
  • Multiple languages: Straight-on photograph of a white overhead directional sign in an international airport terminal, with a black arrow pointing right at the left end. The sign carries the same word in four scripts on four stacked lines, in this order from top to bottom: "DEPARTURES", then the Japanese 出発, then the Korean 출발, then the Arabic المغادرة. Clean sans-serif lettering, sharp focus, no other text anywhere in the frame. (在此提示词中并列第 第 1 名,共 60 名)
  • Occlusion: Straight-on eye-level photograph of a plain wooden desk. On the left, a closed hardcover book with a dark red cover stands upright, its front cover facing the camera. A blue ceramic mug stands on the desk to the right of the book and clearly further back, about 20 cm behind it, positioned so that the right edge of the book overlaps and hides the left third of the mug. The mug's handle points to the right and the whole rim of the mug is visible. Bare desk surface is visible between the book and the mug. Nothing else on the desk. Plain wall behind, soft even light. (在此提示词中并列第 第 39 名,共 60 名)
  • Mirror: Straight-on eye-level photograph of a bathroom vanity. A large rectangular mirror covers the wall behind the counter and its bottom edge meets the countertop, so both the objects and their reflections are visible. On the counter, a tall red toothbrush cup on the left and a tall green glass bottle on the right, both roughly 20 cm tall and well apart from each other. The mirror reflects the counter and the objects on it accurately. Nothing else in the frame. (在此提示词中并列第 第 40 名,共 60 名)
  • Plain zebra: Photograph of a zebra standing side-on in short grass. Its coat is a uniform solid grey with no stripes, no bands and no markings of any kind anywhere on its body. The whole animal is visible. Sharp focus. (在此提示词中并列第 第 19 名,共 60 名)
  • Times Square: Wide street-level photograph of Times Square in New York at night, wet asphalt reflecting the lights. Every billboard, screen, storefront window, awning, taxi door and road marking is completely blank — lit with plain colour fields and soft gradients only. No letters, no words, no numbers, no logos, no lettering of any kind anywhere in the frame. Sharp focus, straight-on shot. (在此提示词中并列第 第 11 名,共 60 名)
  • Minimal diff: From the reference photo, change only the sign text to "ROUTE 7 ESPRESSO - OPEN TILL 8". Keep the sign, wall, lighting and framing identical. (在此提示词中并列第 第 5 名,共 60 名)
  • Object removal: Remove only the second cup from the left in the reference photo. Leave the other four cups exactly as they are, in the same positions, and leave the shelf, towel, wall, plant and lighting untouched. (在此提示词中并列第 第 1 名,共 60 名)
  • Person removal: In the reference photo, remove only the person in the yellow coat. Their shadow on the pavement and their reflection in the shop window must be removed as well. Reconstruct the pavement and the window glass behind them. The person in the red coat and the person in the green coat must stay completely unchanged, including their own shadows and their own reflections. Change nothing else in the frame. (在此提示词中并列第 第 31 名,共 60 名)
  • Product placement: Product photograph of the ceramic mug from the reference image, standing upright on a café table. Show the same side of the mug, from the same side-on eye-level angle as the reference, with the handle on the right. The shape, proportions, colours, moulded relief and every painted detail must match the reference exactly. Only the setting and lighting change: soft daylight in a café interior, shallow depth of field. (在此提示词中并列第 第 1 名,共 60 名)
  • Four references: Place the person from the portrait reference at the desk in the scene reference, wearing the rust orange canvas jacket from its reference and holding the navy blue travel mug from its reference in one hand. Keep the face, the jacket and the mug exactly as they appear in the references — same colours, same details, same proportions. Do not change the room. (在此提示词中并列第 第 14 名,共 60 名)
  • Portraits: Studio headshot of a woman in a charcoal blazer against a mid-grey seamless backdrop, lit by a large softbox slightly above and to the left with a soft fill from the right, catchlights in both eyes. An 85mm look with shallow depth of field, skin texture and flyaway hairs kept rather than smoothed.
  • People in scenes: Photograph of four cooks working the pass in a restaurant kitchen at service, plating three dishes at once, several hands in frame and some of them overlapping. One cook partly hidden behind another, steam rising into hard overhead light, each face clearly a different person.
  • Animals: Photograph of a tabby cat asleep on a sunlit windowsill, fur lit from behind so the individual hairs glow at the edges, one ear turned toward the camera. Shallow depth of field, a houseplant soft in the background, quiet morning light.
  • Landscape and nature: Photograph of an old-growth temperate rainforest after rain, moss on every trunk and fallen log, ferns dense across the floor, thin fog between the far trees. Overcast light with no direct sun, everything from the nearest fern to the last trunk in focus.

提供商

此模型由一家提供商托管。OpenRouter 将每个请求直接转发给该提供商,无需做出路由决策。

定价

客户为此模型实际支付的平均价格,与提供商公布的价格并列展示。由于缓存和折扣,实际支付的价格往往远低于标价。

性能

吞吐量是模型生成内容的速度(每秒 token 数,越高越好)。延迟是完整的往返时间(越低越好)。TTFT 是首个 token 的响应时间,即您看到内容出现前需要等待的时间(越低越好)。

正常运行时间

正常运行时间是指过去 3 天中至少有一家提供商响应请求的时间百分比。可用性是指成功提供推理服务的时间百分比。OpenRouter 会持续监控,并在某家提供商返回错误时使用次优提供商。

应用

向此模型发送最多流量的公开应用。这能很好地反映实际生产工作负载的情况,也能提示此模型最适合哪些用例。

活动

此模型的 token 用量和请求流量随时间的变化。

API

可直接用于调用此模型的代码。OpenRouter 的 API 与 OpenAI 兼容——大多数 SDK 只需替换基础 URL 即可使用。切换模型时,唯一需要更改的是下方的模型标识符。

目前无法通过 EU 区域内路由使用。 区域内路由文档

探索更多模型

具备视觉能力的 AI 模型:用于图像理解的多模态大语言模型集合图像生成模型集合图像模型排行榜排名
端到端延迟
免费US$0.018417.39s
100.00%
端到端延迟
17.39秒

P50,最佳提供商

正常运行时间(3 天)模型可访问。请求已路由至提供商。
100.00%
可用性(3 天)模型通过任意提供商返回了推理结果。错误和空响应均计为失败。
99.89%

过去 3 天的可用性

最近 72 小时
可用性 99.89%
3 天前2 天前昨天现在

过去 24 小时的可用性

OpenRouter Availability
99.89%

当上游提供商发生错误时,如果您的请求筛选条件允许,我们可以通过路由到另一家健康的提供商来恢复服务。您可以通过以下接口以编程方式访问各提供商的正常运行时间数据: 端点 API。了解更多有关我们的 负载均衡和提供商路由选项。

1.
https://oceanapi.dev/ 的网站图标
OceanAPI
新增
5.61Mtoken
2.
https://www.henhideaways.com/ 的网站图标
Hen Hideaways Social
新增
5.33Mtoken
3.
https://caravansanat.com/ 的网站图标
content-engine
新增
5.21Mtoken
4.
https://github.com/local/AI_video_zavod 的网站图标
Kurpatov Video Zavod
新增
3.04Mtoken
5.
https://clash-of-light.local/ 的网站图标
Clash of Light atelier
新增
1.6Mtoken

常见问题

Qwen Image 2.1 Turbo is the fast, lower-cost tier of the Qwen Image 2.1 series from Qwen, for image generation and editing where throughput and price matter more than peak fidelity. See Qwen Image 2.1 Pro for the higher-quality tier.

Qwen Image 2.1 Turbo 的费用为 1K 档位的价格为 US$0.0184/image。

Qwen Image 2.1 Turbo 以 1K和2K 分辨率渲染、支持 1:1, 1:2, 1:4, 2:1, 2:3, 3:2 及另外 7 个 宽高比和每次请求最多可生成 6 张图像.

Qwen Image 2.1 Turbo 接受 文本和图像 作为输入,并返回 图像。每个图像编辑请求最多可附加 4 张参考图像。

Qwen Image 2.1 Pro、Qwen Image 3 Pro和Qwen Image 3 是 Qwen 的其他 图像 模型。

Qwen Image 2.1 Turbo 于 2026年10月16日 发布。

更多模型,来自 Qwen

Qwen Image 2.1 Turbo

Qwen Image 2.1 Turbo is the fast, lower-cost tier of the Qwen Image 2.1 series from Qwen, for image generation and editing where throughput and price matter more than peak fidelity. See Qwen Image 2.1 Pro for the higher-quality tier.

图像from US$0.0184/image
Qwen Image 2.1 Pro

Qwen Image 2.1 Pro is an image generation and editing model from Qwen, the higher-quality tier of the Qwen Image 2.1 series. Choose it over Qwen Image 2.1 Turbo when output fidelity matters more than cost or speed.

图像from US$0.046/image
Qwen3.8 Max Prime

Qwen3.8 Max Prime is a higher-throughput variant of Qwen3.8 Max from Alibaba's Qwen team, served as a separate SKU at a higher price point. It accepts text, image, and video input and returns text, with a 1M-token context window and reasoning enabled by default. Tool calling, structured outputs, and configurable reasoning effort are supported, matching Qwen3.8 Max.

文本1M 上下文US$4 / US$12
Qwen3.8 Omni Flash

Qwen3.8 Omni Flash is an omni-modal reasoning model from Alibaba, the first Qwen model built around agentic capabilities with native audio-video understanding. It is suited for audio-video analysis and summarization, video editing and production workflows, audio-video dialogue, coding, knowledge work, and GUI interaction, and it is particularly strong at long-form multimedia tasks that combine speech, sound, and visual context. It also supports two-channel and four-channel spatial audio understanding.

文本1M 上下文US$0.15 / US$0.47
Qwen3.8 Max

Qwen3.8 Max 0902 is an updated snapshot of Qwen3.8 Max from Alibaba's Qwen team. It is a 2.4-trillion-parameter mixture-of-experts model that accepts text, image, and video input and returns text, with a 1M-token context window and reasoning enabled by default.

This snapshot is post-trained for coding and agentic work, including multi-step software projects, multi-tool orchestration, and long-horizon task execution. It also targets chart reasoning, document parsing, and multimodal understanding over long documents and extended video. Tool calling, structured outputs, and configurable reasoning effort are supported.

文本1M 上下文US$2 / US$6
Qwen3.8 Flash

Qwen3.8 Flash is a multimodal reasoning model from Alibaba. It is suited for coding assistance, agentic workflows, visual understanding, document and codebase analysis, desktop interaction, chart analysis, and long-video analysis.

文本1M 上下文US$0.15 / US$0.47
Qwen3 Reranker 0.6B

Qwen3 Reranker 0.6B is the smallest text reranking model from Alibaba Cloud in the Qwen3 Reranker series. Built on the Qwen3 architecture, it evaluates query-document pairs to produce relevance scores for retrieval and RAG pipelines. Supports 100+ languages with instruction-aware reranking. Designed for low-latency, resource-constrained deployments.

重排序
Qwen3 Reranker 4B

Qwen3 Reranker 4B is a text reranking model from Alibaba Cloud built on the Qwen3 architecture. It evaluates query-document pairs to produce relevance scores for use in retrieval and RAG pipelines. Supports 100+ languages and programming languages, with instruction-aware reranking that allows customizing scoring criteria per task. A compact 4B alternative balancing performance and inference cost.

重排序
Qwen3.8 27B

Qwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be enabled or disabled.

文本1.0M 上下文US$0.005 / US$0.274
Qwen3 Reranker 8B

Qwen3 Reranker 8B is a text reranking model from Alibaba Cloud built on the Qwen3 architecture. It evaluates query-document pairs to produce relevance scores for use in retrieval and RAG pipelines. Supports 100+ languages and programming languages, with instruction-aware reranking that allows customizing scoring criteria per task. Offers strong performance on multilingual benchmarks including MTEB, CMTEB, and MMTEB.

重排序US$0.20/M tokens
Qwen3 ASR 1.7B

Qwen3 ASR 1.7B is an automatic speech recognition model from Qwen. It supports multilingual language identification and transcription across 30 languages and 22 Chinese dialects, with streaming and offline inference plus segment-level and word-level timestamps.

转录US$0.000008/second
Qwen3 ASR 0.6B

Qwen3 ASR 0.6B is a compact automatic speech recognition model from Qwen. It supports multilingual language identification and transcription across 30 languages and 22 Chinese dialects, with streaming and offline inference plus segment-level and word-level timestamps.

转录US$0.000003/second
Qwen3.8 2.4T A95B

Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of Qwen3.8 Max, with 95 billion active parameters out of 2.4 trillion total. It is suited for coding, research, complex reasoning, and agentic workflows.

文本1.0M 上下文US$2 / US$6
Qwen Image 3 Pro

Qwen Image 3 Pro is an image generation and editing model from Qwen. It supports precise rendering of text and details as small as 10px, along with richer world knowledge compared to previous generations.

图像from US$0.04/image
Qwen Image 3

Qwen Image 3 is a unified image generation and editing model from Qwen. It supports precise rendering of text and details as small as 10px, along with a richer world knowledge base than previous generations.

图像from US$0.03/image
Qwen3.8 Max

Qwen3.8 Max (0803) is the August 3, 2026 checkpoint of Qwen3.8 Max, the flagship model in Alibaba's Qwen3.8 series and the general-availability successor to the Qwen3.8 Max Preview. It is a multimodal reasoning model intended for complex reasoning, visual understanding, coding, and agentic workflows. This checkpoint was superseded by Qwen3.8 Max (0902) on September 5, 2026.

文本1M 上下文
Qwen3.7 Flash

Qwen3.7 Flash is a vision-language reasoning model from Alibaba. It is suited for multimodal agents, visual coding, search, and computer interaction, with strengths in object recognition, spatial understanding, and real-world visual perception.

文本1M 上下文US$0.03 / US$0.13
Qwen-Audio-3.0-TTS Flash

Qwen-Audio-3.0-TTS Flash is Alibaba's fast, cost-efficient text-to-speech model, generating spoken audio from text via the DashScope Speech Synthesizer API.

语音US$15/M characters
Qwen-Audio-3.0-TTS Plus

Qwen-Audio-3.0-TTS Plus is Alibaba's higher-quality text-to-speech model, generating spoken audio from text via the DashScope Speech Synthesizer API.

语音US$20/M characters
Qwen3.7 Plus

Qwen3.7-Plus is a cost-effective model in Alibaba's Qwen3.7 series. It supports text and image input with text output, building on the series' text capabilities with a comprehensive upgrade to its vision-language abilities while retaining full-stack, agent-level intelligence for coding, tool use, and productivity workflows. Its distinguishing trait is multi-modal interactive hybrid agent capability: it can perceive real-world scenes, read screens and interact with GUIs, generate code from visual references, and perform end-to-end navigation within mobile apps.

文本1M 上下文US$0.32 / US$1.28