Qwen Models

145 modelsGeneral models from $0.0206/M inputUp to 1.05M context

Usage

Last 30 days · 2026-08-16 to 2026-09-14

Tokens

266B

Requests

7.8M

Models in use

136 of 145

Tokens per day, stacked by model

020B40B08-1608-2308-3009-0609-132026-08-16 — 2,225,669,890 tokens 89 more models: 1,052,018,715 qwen3.8-max: 482,504,605 qwen3.7-flash: 296,680,040 qwen3.6-flash: 278,053,250 qwen3.6-plus: 89,287,900 qwen3.7-plus: 20,892,935 qwen3.5-flash: 6,232,4452026-08-17 — 4,593,586,785 tokens qwen3.7-flash: 2,235,710,005 89 more models: 757,522,630 qwen3.6-flash: 572,522,565 qwen3.8-max: 506,122,370 qwen3.6-plus: 329,280,635 qwen3.7-plus: 186,973,675 qwen3.5-flash: 5,454,9052026-08-18 — 4,109,936,195 tokens qwen3.7-flash: 1,752,326,835 89 more models: 1,126,947,730 qwen3.8-max: 460,220,560 qwen3.6-plus: 379,968,650 qwen3.7-plus: 215,141,500 qwen3.6-flash: 159,462,595 qwen3.5-flash: 15,868,3252026-08-19 — 10,762,531,495 tokens qwen3.5-flash: 6,588,389,020 qwen3.7-flash: 1,332,770,300 qwen3.6-flash: 771,308,940 89 more models: 756,533,210 qwen3.6-plus: 590,422,685 qwen3.8-max: 585,842,470 qwen3.7-plus: 137,264,8702026-08-20 — 9,314,602,925 tokens qwen3.5-flash: 4,655,296,340 qwen3.7-flash: 2,881,172,015 89 more models: 680,822,890 qwen3.6-plus: 453,331,845 qwen3.7-plus: 332,098,285 qwen3.8-max: 256,483,000 qwen3.6-flash: 55,398,5502026-08-21 — 11,080,543,440 tokens qwen3.5-flash: 6,726,948,450 qwen3.7-flash: 1,568,101,055 89 more models: 1,385,135,130 qwen3.8-max: 590,218,935 qwen3.6-plus: 514,997,960 qwen3.7-plus: 280,304,030 qwen3.6-flash: 14,837,8802026-08-22 — 22,140,183,020 tokens qwen3.5-flash: 16,191,844,120 qwen3.7-flash: 3,093,141,575 89 more models: 1,153,717,275 qwen3.6-plus: 666,721,125 qwen3.6-flash: 538,527,295 qwen3.8-max: 392,218,740 qwen3.7-plus: 104,012,8902026-08-23 — 39,975,748,315 tokens qwen3.5-flash: 34,643,023,500 qwen3.7-flash: 3,123,425,320 89 more models: 1,075,355,835 qwen3.8-max: 569,024,200 qwen3.6-plus: 344,279,280 qwen3.6-flash: 136,080,335 qwen3.7-plus: 84,559,8452026-08-24 — 6,030,436,280 tokens qwen3.7-flash: 3,404,980,410 89 more models: 782,616,180 qwen3.8-max: 737,963,130 qwen3.6-plus: 541,223,325 qwen3.6-flash: 356,510,950 qwen3.5-flash: 148,699,850 qwen3.7-plus: 58,442,4352026-08-25 — 11,296,607,005 tokens qwen3.7-flash: 9,503,612,180 89 more models: 755,762,710 qwen3.8-max: 406,098,105 qwen3.5-flash: 273,007,745 qwen3.7-plus: 238,447,760 qwen3.6-plus: 115,419,265 qwen3.6-flash: 4,259,2402026-08-26 — 21,219,605,385 tokens qwen3.7-flash: 18,819,287,620 89 more models: 689,444,520 qwen3.7-plus: 459,769,510 qwen3.5-flash: 406,845,720 qwen3.6-plus: 400,715,850 qwen3.8-max: 326,822,250 qwen3.6-flash: 116,719,9152026-08-27 — 16,929,402,775 tokens qwen3.7-flash: 14,636,196,755 89 more models: 890,369,275 qwen3.8-max: 448,246,100 qwen3.6-plus: 442,386,265 qwen3.7-plus: 375,852,805 qwen3.8-flash: 101,836,125 qwen3.5-flash: 25,039,730 qwen3.6-flash: 9,475,7202026-08-28 — 9,992,357,565 tokens qwen3.7-flash: 4,571,307,665 qwen3.8-max: 2,705,194,310 qwen3.8-flash: 1,611,022,680 89 more models: 952,811,135 qwen3.7-plus: 85,054,645 qwen3.6-plus: 50,402,340 qwen3.5-flash: 12,883,810 qwen3.6-flash: 3,680,9802026-08-29 — 3,865,700,735 tokens qwen3.8-flash: 1,304,691,330 qwen3.7-plus: 967,758,565 qwen3.7-flash: 582,730,885 89 more models: 537,920,360 qwen3.8-max: 351,256,495 qwen3.6-plus: 65,149,380 qwen3.5-flash: 54,529,180 qwen3.6-flash: 1,664,5402026-08-30 — 4,613,177,950 tokens qwen3.8-flash: 2,874,334,035 89 more models: 430,792,845 qwen3.5-flash: 418,571,330 qwen3.8-max: 362,585,665 qwen3.7-flash: 274,445,050 qwen3.7-plus: 229,640,335 qwen3.6-plus: 20,088,680 qwen3.6-flash: 2,720,0102026-08-31 — 5,175,736,935 tokens qwen3.7-flash: 2,658,587,475 qwen3.8-flash: 1,290,923,125 89 more models: 558,154,820 qwen3.8-max: 446,626,545 qwen3.7-plus: 173,928,785 qwen3.6-plus: 30,576,715 qwen3.5-flash: 13,621,680 qwen3.6-flash: 3,317,7902026-09-01 — 5,909,231,430 tokens qwen3.7-flash: 3,064,866,360 89 more models: 1,261,710,265 qwen3.8-flash: 868,273,890 qwen3.8-max: 517,189,160 qwen3.7-plus: 95,539,205 qwen3.6-plus: 44,710,070 qwen3.6-flash: 34,510,930 qwen3.5-flash: 22,431,5502026-09-02 — 2,624,590,710 tokens qwen3.8-max: 682,135,670 89 more models: 558,190,685 qwen3.7-flash: 474,126,635 qwen3.7-plus: 385,064,385 qwen3.8-flash: 321,819,470 qwen3.8-max-2026-09-02: 137,077,990 qwen3.6-plus: 31,528,105 qwen3.6-flash: 27,677,820 qwen3.5-flash: 6,969,9502026-09-03 — 5,537,612,300 tokens qwen3.8-flash: 2,739,642,180 qwen3.7-flash: 998,547,835 qwen3.8-max-2026-09-02: 906,928,385 89 more models: 510,896,825 qwen3.8-max: 279,175,850 qwen3.7-plus: 42,829,865 qwen3.6-plus: 35,487,290 qwen3.6-flash: 12,864,980 qwen3.5-flash: 11,239,0902026-09-04 — 12,488,881,260 tokens qwen3.8-flash: 9,993,403,840 89 more models: 1,004,620,870 qwen3.7-flash: 524,029,925 qwen3.8-max: 412,862,265 qwen3.8-max-2026-09-02: 368,870,925 qwen3.7-plus: 76,676,335 qwen3.6-flash: 45,467,625 qwen3.6-plus: 34,807,120 qwen3.5-flash: 28,142,3552026-09-05 — 4,022,671,485 tokens qwen3.8-flash: 2,243,267,610 qwen3.8-max: 625,610,780 qwen3.7-flash: 456,534,395 89 more models: 358,287,255 qwen3.8-max-2026-09-02: 121,533,475 qwen3.7-plus: 113,934,270 qwen3.6-plus: 46,505,190 qwen3.6-flash: 36,990,525 qwen3.5-flash: 20,007,9852026-09-06 — 2,626,546,915 tokens qwen3.8-flash: 783,405,980 qwen3.7-flash: 525,941,750 89 more models: 514,015,635 qwen3.8-max: 389,019,335 qwen3.8-max-2026-09-02: 213,127,295 qwen3.7-plus: 108,501,455 qwen3.6-plus: 42,937,295 qwen3.6-flash: 34,324,665 qwen3.5-flash: 15,273,5052026-09-07 — 9,239,671,715 tokens qwen3.8-flash: 6,190,494,710 qwen3.7-flash: 1,257,714,380 89 more models: 666,161,775 qwen3.8-max: 454,783,380 qwen3.7-plus: 332,139,620 qwen3.8-max-2026-09-02: 206,455,505 qwen3.6-plus: 83,869,820 qwen3.6-flash: 29,598,420 qwen3.5-flash: 18,454,1052026-09-08 — 5,829,326,175 tokens qwen3.7-flash: 2,068,261,435 qwen3.8-flash: 1,577,516,590 89 more models: 881,714,425 qwen3.8-max: 622,859,425 qwen3.7-plus: 328,252,005 qwen3.5-flash: 214,756,460 qwen3.8-max-2026-09-02: 67,832,015 qwen3.6-plus: 63,835,965 qwen3.6-flash: 4,297,8552026-09-09 — 5,185,373,400 tokens qwen3.8-flash: 1,970,807,585 89 more models: 1,289,256,770 qwen3.7-flash: 799,413,475 qwen3.8-max: 423,545,040 qwen3.7-plus: 351,726,740 qwen3.8-max-2026-09-02: 206,650,860 qwen3.6-plus: 116,561,460 qwen3.6-flash: 14,511,815 qwen3.5-flash: 12,899,6552026-09-10 — 5,191,505,810 tokens qwen3.7-flash: 1,975,783,065 89 more models: 951,147,820 qwen3.7-plus: 630,277,955 qwen3.8-flash: 626,499,440 qwen3.8-max-2026-09-02: 473,250,630 qwen3.8-max: 446,450,955 qwen3.6-plus: 70,143,335 qwen3.5-flash: 9,063,200 qwen3.6-flash: 8,889,4102026-09-11 — 8,244,349,505 tokens qwen3.7-flash: 4,781,081,620 89 more models: 1,569,953,190 qwen3.8-flash: 550,154,925 qwen3.8-max: 538,164,185 qwen3.8-max-2026-09-02: 325,398,580 qwen3.7-plus: 298,764,370 qwen3.6-plus: 166,858,875 qwen3.5-flash: 7,702,675 qwen3.6-flash: 6,271,0852026-09-12 — 3,545,208,675 tokens 89 more models: 917,223,790 qwen3.8-max: 826,539,465 qwen3.7-flash: 766,822,830 qwen3.8-max-2026-09-02: 423,241,700 qwen3.8-flash: 270,429,405 qwen3.7-plus: 186,075,810 qwen3.6-plus: 133,917,360 qwen3.6-flash: 17,263,760 qwen3.5-flash: 3,694,5552026-09-13 — 5,054,764,075 tokens qwen3.8-max: 1,247,804,290 qwen3.7-flash: 1,210,605,070 qwen3.8-flash: 821,153,755 89 more models: 744,841,890 qwen3.8-max-2026-09-02: 609,629,460 qwen3.7-plus: 243,396,330 qwen3.6-plus: 162,328,845 qwen3.5-flash: 13,399,385 qwen3.6-flash: 1,605,0502026-09-14 — 7,398,658,815 tokens qwen3.8-flash: 2,362,250,595 qwen3.7-flash: 1,747,887,370 89 more models: 1,358,138,390 qwen3.8-max: 1,127,928,580 qwen3.8-max-2026-09-02: 607,695,115 qwen3.6-plus: 164,158,535 qwen3.7-plus: 23,774,135 qwen3.5-flash: 5,519,830 qwen3.6-flash: 1,306,265
  • qwen3.7-flash
  • qwen3.5-flash
  • qwen3.8-flash
  • qwen3.8-max
  • qwen3.7-plus
  • qwen3.6-plus
  • qwen3.8-max-2026-09-02
  • qwen3.6-flash
  • 89 more models

Which models that traffic went to

  1. Qwen3.7 Flash34.3%91.4B
  2. Qwen3.5 Flash26.5%70.6B
  3. Qwen3.8 Flash14.5%38.5B
  4. Qwen3.8 Max6.8%18.2B
  5. Qwen3.7 Plus2.7%7.2B
  6. Qwen3.6 Plus2.3%6.2B
  7. Qwen3.8 Max 2026 09-021.8%4.7B
  8. Qwen3.6 Flash1.2%3.3B
  9. 89 more models9.8%26.2B

Share of 266B tokens. 39 models with traffic report no token counts and cannot be ranked here, including qwen-image-3.0 and Qwen/Qwen2.5-VL-72B-Instruct — they are in the request view.

The two views disagree on purpose: a model can take a large share of the calls and a small share of the tokens — many short requests — or the reverse. Which one matters depends on whether your cost is driven by call volume or by prompt length. Measured on AIHubMix over the last 30 days, counting the 145 model IDs listed on this page; traffic routed through upstream-specific IDs that are not in the public catalog is not included.

All 145 Qwen Models

Open in model list
Qwen models on AIHubMix with input and output modalities, context length, maximum output, price per million tokens including cache read and cache write rates, and measured throughput and latency.
Modalities
qwen3-coder-plusTakes text, returns text.1.05M66K$0.54$2.16/M$0.108/M—42 tok/s2.21 s
qwen3.6-plus-preview-freeTakes text, returns text.1M66KFreeFree/M————
qwen3.5-flashTakes text, vision, video, returns text.1M66K$0.0282$0.282/M$0.0028/M$0.0352/M105 tok/s1.27 s
qwen3.7-flashTakes text, vision, video, returns text.1M131K$0.0282$0.1128/M$0.0056/M$0.0352/M66 tok/s4.36 s
qwen3.5-plusTakes text, vision, video, returns text.1M66K$0.1096$0.6576/M$0.011/M$0.137/M36 tok/s2.63 s
qwen3.8-flashTakes text, vision, video, returns text.1M131K$0.1126$0.38/M$0.0141/M$0.1759/M45 tok/s2.46 s
qwen3-coder-flashTakes text, returns text.1M66K$0.136$0.544/M——110 tok/s1.85 s
qwen3.6-flashTakes text, vision, video, returns text.1M66K$0.169$1.014/M$0.0169/M$0.2112/M78 tok/s1.26 s
qwen3.6-plusTakes text, vision, video, returns text.1M66K$0.282$1.692/M$0.0282/M$0.3525/M45 tok/s2.27 s
qwen3.7-plusTakes text, vision, video, returns text.1M131K$0.282$1.128/M$0.0564/M$0.3525/M52 tok/s2.42 s
qwen3.8-max-previewTakes text, vision, video, returns text.1M131K$0.338$1.014/M$0.0676/M$0.4225/M48 tok/s2.40 s
qwen3.7-maxTakes text, returns text.1M131K$1.69$5.07/M$0.169/M$2.1125/M55 tok/s1.34 s
qwen3.8-maxTakes text, vision, video, returns text.1M131K$1.69$5.07/M$0.169/M$2.1125/M35 tok/s3.32 s
qwen3.8-max-2026-09-02Takes text, vision, video, returns text.1M131K$1.69$5.07/M$0.169/M$2.1125/M29 tok/s2.75 s
qwen3.8-2.4t-a95bTakes text, returns text.1M131K$2$6/M$0.5/M—48 tok/s1.73 s
qwen3-vl-flashTakes text, vision, video, returns text.262K33K$0.0206$0.206/M$0.0041/M—116 tok/s1.00 s
qwen3-vl-flash-2026-01-22Takes text, vision, video, returns text.262K33K$0.0206$0.206/M——5 tok/s1.06 s
qwen3.5-35b-a3bTakes text, vision, video, returns text.262K66K$0.0564$0.4512/M——66 tok/s1.62 s
qwen3.5-27bTakes text, vision, video, returns text.262K66K$0.0846$0.6768/M——71 tok/s1.27 s
qwen3.5-122b-a10bTakes text, vision, video, returns text.262K66K$0.1126$0.9008/M——52 tok/s0.76 s
qwen3-coder-nextTakes text, returns text.262K66K$0.137$0.548/M——16 tok/s0.51 s
qwen3-vl-plusTakes text, vision, video, returns text.262K33K$0.137$1.37/M$0.0274/M—67 tok/s2.32 s
qwen3.5-397b-a17bTakes text, vision, video, returns text.262K66K$0.1644$0.9864/M——5 tok/s1.35 s
qwen3-coder-30b-a3b-instructTakes text, returns text.262K262K$0.2$0.8/M——115 tok/s0.95 s
qwen3.6-35b-a3bTakes text, vision, video, returns text.262K66K$0.254$1.524/M——33 tok/s2.62 s
qwen3-235b-a22b-instruct-2507Takes text, vision, returns text.262K16K$0.28$1.12/M——96 tok/s1.54 s
qwen3-235b-a22b-thinking-2507Takes text, vision, returns text.262K131K$0.28$2.8/M——87 tok/s0.27 s
qwen3.6-27bTakes text, vision, video, returns text.262K66K$0.422$2.532/M——81 tok/s1.84 s
qwen3-maxTakes text, returns text.262K66K$0.4508$1.8032/M$0.0902/M$0.5635/M56 tok/s0.81 s
qwen3-max-2026-01-23Takes text, returns text.262K66K$0.4508$1.8032/M$0.0902/M$0.5635/M15 tok/s0.51 s
qwen3.6-max-previewTakes text, returns text.262K66K$1.268$7.608/M$0.1268/M$1.585/M185 tok/s0.65 s
qwen3-coder-480b-a35b-instructTakes text, returns text.262K66K$0.82$3.28/M——1655 tok/s0.92 s
qwen3-next-80b-a3b-instructTakes text, vision, returns text.256K33K$0.138$0.552/M——150 tok/s0.10 s
qwen3-next-80b-a3b-thinkingTakes text, vision, returns text.256K33K$0.142$1.42/M——234 tok/s0.42 s
qwen3-235b-a22bTakes text, returns text.131K128K$0.28$1.12/M——81 tok/s0.63 s
qwen3-vl-30b-a3b-instructTakes text, vision, video, returns text.131K33K$0.1028$0.4112/M——50 tok/s1.07 s
qwen3-vl-30b-a3b-thinkingTakes text, vision, video, returns text.131K33K$0.1028$1.028/M——42 tok/s1.49 s
qwen3-vl-235b-a22b-instructTakes text, vision, video, returns text.131K33K$0.274$1.096/M——57 tok/s1.13 s
qwen3-vl-235b-a22b-thinkingTakes text, vision, video, returns text.131K33K$0.274$2.74/M——58 tok/s2.22 s
qwen-3.8-27bTakes text, vision, video, returns text.131K—$1.1$1.65/M——373 tok/s0.29 s
bai-qwen3-vl-235b-a22b-instructTakes , returns text.131K—$0.274$1.096/M——9 tok/s0.47 s
kat-devTakes text, returns text.128K—$0.137$0.548/M————
Qwen/Qwen2.5-VL-72B-InstructTakes text, vision, video, returns text.128K—$0.5$0.5/M————
qwen3-coder-plus-2025-07-22Takes text, returns text.128K66K$0.54$2.16/M$0.108/M—83 tok/s1.63 s
qwen3-reranker-0.6bTakes text, vision. Output modality not published.16K8K$0.11$0.11/M————
qwen-mt-turboTakes text, returns text.16K8K$0.192$0.5349/M——15 tok/s0.35 s
qwen-mt-plusTakes text, returns text.16K8K$0.492$1.476/M——40 tok/s0.83 s
qwen-audio-3.0-tts-flashTakes text, returns audio.——Free$0.141/M————
qwen-audio-3.0-tts-plusTakes text, returns audio.——Free$0.197/M————
qwen-image-3.0Takes text, vision, returns vision.——FreeFree/M————
qwen-image-3.0-proTakes text, vision, returns vision.——FreeFree/M————
qwen-flash——$0.02$0.2/M——30 tok/s0.99 s
qwen-flash-2025-07-28——$0.02$0.2/M————
qwen-turboTakes text, returns text.——$0.046$0.092/M$0.0092/M—109 tok/s0.49 s
qwen-turbo-2024-11-01Takes text, returns text.——$0.046$0.092/M——94 tok/s0.66 s
qwen-turbo-2025-04-28Takes , returns text.——$0.046$0.092/M————
qwen-turbo-latestTakes , returns text.——$0.046$0.092/M$0.0092/M———
qwen3-0.6bTakes , returns text.——$0.046$0.46/M————
qwen3-1.7bTakes , returns text.——$0.046$0.46/M————
qwen3-4bTakes , returns text.——$0.046$0.46/M————
bce-reranker-baseTakes text, vision. Output modality not published.——$0.068$0.068/M————
qwen3-embedding-0.6bTakes text. Output modality not published.——$0.068$0.068/M————
qwen3-embedding-4bTakes text. Output modality not published.——$0.068$0.068/M————
qwen3-embedding-8bTakes text. Output modality not published.——$0.068$0.068/M————
Qwen/Qwen2-7B-Instruct——$0.08$0.08/M————
qwen3-8bTakes , returns text.——$0.08$0.8/M——80 tok/s12.40 s
text-embedding-v4Takes text. Output modality not published.——$0.08$0.08/M————
qwen-long——$0.1$0.4/M——33 tok/s22.65 s
qwen3-30b-a3b-instruct-2507——$0.1028$0.4112/M————
gte-rerank-v2Takes text, vision. Output modality not published.——$0.11$0.11/M————
qwen3-reranker-4bTakes text, vision. Output modality not published.——$0.11$0.11/M————
qwen3-reranker-8bTakes text, vision. Output modality not published.——$0.11$0.11/M————
qwen-plus——$0.1126$1.126/M$0.0225/M$0.1407/M26 tok/s0.74 s
qwen-plus-2025-04-28Takes , returns text.——$0.1126$1.126/M$0.0225/M$0.1407/M11 tok/s0.36 s
qwen-plus-2025-07-28——$0.1126$1.126/M$0.0225/M$0.1407/M——
qwen-plus-latestTakes , returns text.——$0.1126$1.126/M$0.0225/M$0.1407/M39 tok/s0.85 s
qwen3-30b-a3b——$0.12$1.2/M————
qwen3-30b-a3b-thinking-2507——$0.12$1.2/M————
gme-qwen2-vl-2b-instructTakes text, vision, video. Output modality not published.——$0.138$0.138/M————
Qwen/QwQ-32BTakes , returns text.——$0.14$0.56/M————
Qwen/Qwen2.5-Coder-32B-Instruct——$0.16$0.16/M————
Qwen/QwQ-32B-Preview——$0.16$0.16/M————
qwen3-14bTakes , returns text.——$0.16$1.6/M——48 tok/s0.34 s
qwen3-32b——$0.16$0.64/M——96 tok/s1.36 s
Qwen/Qwen2-1.5B-Instruct——$0.2$0.2/M————
Qwen/Qwen3-8B——$0.2$0.2/M——11 tok/s3.31 s
qwen2.5-coder-1.5b-instruct——$0.2$0.4/M————
qwen2.5-coder-7b-instruct——$0.2$0.4/M————
qwen2.5-math-1.5b-instruct——$0.2$0.2/M————
qwen2.5-math-7b-instruct——$0.2$0.4/M————
Qwen/Qwen2-57B-A14B-Instruct——$0.24$0.24/M————
Qwen/Qwen2.5-VL-32B-InstructTakes text, vision, video, returns text.——$0.24$0.24/M————
qwen-3-235b-a22b-instruct-2507——$0.28$1.4/M————
qwen-3-235b-a22b-thinking-2507——$0.28$2.8/M————
Qwen2-VL-7B-InstructTakes text, vision, video. Output modality not published.——$0.28$0.7/M————
Qwen3-235B-A22B-Thinking-2507——$0.28$2.8/M——87 tok/s0.27 s
qwen-max——$0.38$1.52/M——24 tok/s0.50 s
qwen-max-0125——$0.38$1.52/M————
qwen-3-32b——$0.4$1.6/M————
qwen-qwq-32b——$0.4$0.8/M————
Qwen/Qwen2.5-7B-Instruct——$0.4$0.4/M————
Qwen/Qwen3-32B——$0.4$0.8/M————
qwen2.5-14b-instruct——$0.4$1.2/M————
qwen2.5-3b-instruct——$0.4$0.8/M————
qwen2.5-7b-instruct——$0.4$0.8/M————
Qwen/Qwen3-14B——$0.5$0.5/M————
Qwen/Qwen2.5-32B-Instruct——$0.6$0.6/M——24 tok/s1.76 s
qwen2.5-32b-instruct——$0.6$1.2/M————
Qwen/Qwen2-72B-Instruct——$0.8$0.8/M————
Qwen/Qwen2.5-72B-Instruct——$0.8$0.8/M————
Qwen/Qwen2.5-72B-Instruct-128K——$0.8$0.8/M————
qwen2.5-72b-instruct——$0.8$2.4/M————
qwen2.5-math-72b-instruct——$0.8$2.4/M————
qwen3-max-previewTakes text, vision, returns text.——$0.846$3.384/M$0.1692/M—9 tok/s0.47 s
Qwen/Qwen3-30B-A3B——$1$1/M————
Qwen/QVQ-72B-Preview——$1.2$1.2/M————
qwen-imageTakes text, vision, returns vision.——$2$2/M————
qwen-image-2.0Takes text, vision, returns vision.——$2$2/M————
qwen-image-2.0-proTakes text, vision, returns vision.——$2$2/M————
qwen-image-editTakes text, vision, returns vision.——$2$2/M————
qwen-image-maxTakes text, vision, returns vision.——$2$2/M————
wan2.7-imageTakes text, vision, returns vision.——$2$2/M————
wan2.7-image-proTakes text, vision, returns vision.——$2$2/M————
Qwen2-VL-72B-InstructTakes text, vision, video. Output modality not published.——$2.18$6.54/M————
qwen2.5-vl-72b-instructTakes text, vision, returns text.——$2.4$7.2/M————
qwen-max-longcontext——$7$21/M————
wan2.2-i2v-plusTakes text, vision, returns video.——$480$480/M————
wan2.5-i2v-previewTakes text, vision, returns video.——$480$480/M————
wan2.5-t2v-previewTakes text, returns video.——$480$480/M————
wan3.0-videoTakes text, vision, audio, video, returns video.——$480$480/M————
wan3.0-video-primeTakes text, vision, audio, video, returns video.——$480$480/M————
happyhorse-1.0-i2vTakes text, vision, returns video.——$720$720/M————
happyhorse-1.0-r2vTakes text, vision, returns video.——$720$720/M————
happyhorse-1.0-t2vTakes text, returns video.——$720$720/M————
happyhorse-1.0-video-editTakes text, vision, video, returns video.——$720$720/M————
happyhorse-1.1-i2vTakes text, vision, returns video.——$720$720/M————
happyhorse-1.1-r2vTakes text, vision, returns video.——$720$720/M————
happyhorse-1.1-t2vTakes text, returns video.——$720$720/M————
wan2.6-i2vTakes text, vision, returns video.——$720$720/M————
wan2.6-t2iTakes text, vision, returns vision.——$720$720/M————
wan2.6-t2vTakes text, returns video.——$720$720/M————
wan2.7-i2vTakes text, vision, audio, returns video.——$720$720/M————
wan2.7-r2vTakes text, vision, audio, video, returns video.——$720$720/M————
wan2.7-t2vTakes text, audio, returns video.——$720$720/M————
wan2.7-videoeditTakes text, vision, video, returns video.——$720$720/M————

Prices are USD per million tokens; cache read and cache write are the rates for prompt-cache hits and for writing a prompt into the cache. Throughput and latency are measured on AIHubMix — the same figures the model detail page shows — not vendor claims. A dash means the catalog does not publish that field for that model, which is not the same as the model not supporting it.

Common Comparisons

Qwen/QwQ-32B vs Qwen/Qwen2.5-VL-32B-InstructQwen/QwQ-32B vs Qwen/Qwen2.5-VL-72B-InstructQwen/QwQ-32B vs bai-qwen3-vl-235b-a22b-instructQwen/QwQ-32B vs kimi-k3Qwen/QwQ-32B vs glm-5.2

Qwen on AIHubMix

Which Qwen model should I start with?

qwen3-vl-flash at $0.0206/M input — the cheapest entry here that declares tool calling, and it carries a 262K context. Move up to happyhorse-1.0-i2v when answer quality matters more than cost, or to qwen3.6-plus-preview-free for long-form reasoning.

Which of these models reason before answering?

25 of the 145 models here declare a reasoning phase — they work through the problem before producing an answer, which helps on multi-step problems at the cost of extra output tokens. Use the Reasoning filter above the table to see them. The catalog does not record anything further about how they differ, so this page does not sort them into families.

Why are there several entries for the same model?

Because each row is a route you can call, not a model release. Some IDs name an upstream (azure-, alicloud-, cc-), some are the open-weight repository form (Qwen/…), and some differ only in capitalisation, kept so older integrations keep working.

The catalog does not carry a field saying which of those a given row is, so this page does not sort them into buckets it would have to invent. Every row shows that route’s own price, context and speed — compare those directly, and open a model to see the upstreams that serve it.

How is cached input billed?

The Cache read column is the rate for input tokens served from the prompt cache — for example qwen3.5-flash bills cache hits at 10% of the input rate and qwen3.5-plus bills cache hits at 10% of the input rate. Cache write is the surcharge for putting a prompt into the cache in the first place, and only a few upstreams bill it separately. A dash in either column means the catalog carries no cache rate for that model, so plan on paying the full input rate.

Do I need a separate Qwen account?

No. One AIHubMix key covers every model on this page, and switching between them is a change to the model string — billing, rate limits, and logs stay in one place.

Other Model Authors

See all model authors →
OpenAI
137 modelsGoogle
85 modelsZ.AI
71 modelsLlama
53 modelsByteDance
46 models

Start calling Qwen in one line

One key, one endpoint, 883 models across 39 model authors.

Get API keyRead the docs

© 2023 - 2026 AIHubMix, LLC

Product

Developer

Connect

Terms & Policies