AIHubMixAIHubMix

Qwen Models

141 modelsGeneral models from $0.02/M inputUp to 2M context

Usage

Last 30 days · 2026-07-22 to 2026-08-20

Tokens

244B

Requests

5.4M

Models in use

131 of 141

Tokens per day, stacked by model

038.6B77.3B07-2207-2908-0508-1208-192026-07-22 — 3,594,802,455 tokens 90 more models: 1,211,091,720 qwen3.8-max-preview: 1,164,768,565 qwen3.6-flash: 474,235,470 qwen3.7-plus: 373,184,665 qwen3.6-plus: 198,716,165 qwen3.5-flash: 94,686,940 qwen-turbo: 78,118,9302026-07-23 — 5,472,829,720 tokens 90 more models: 1,914,738,725 qwen3.5-flash: 1,729,504,835 qwen3.8-max-preview: 928,297,830 qwen3.6-plus: 283,588,035 qwen3.6-flash: 280,970,430 qwen3.7-plus: 241,433,905 qwen-turbo: 94,295,9602026-07-24 — 5,615,339,185 tokens 90 more models: 3,198,630,660 qwen3.7-plus: 772,965,485 qwen3.8-max-preview: 759,611,045 qwen3.5-flash: 332,077,655 qwen3.6-flash: 254,380,080 qwen3.6-plus: 186,165,935 qwen-turbo: 111,508,3252026-07-25 — 3,836,678,275 tokens 90 more models: 1,601,011,885 qwen3.8-max-preview: 1,019,763,235 qwen3.7-plus: 704,497,060 qwen3.6-flash: 265,668,750 qwen3.6-plus: 108,880,390 qwen3.5-flash: 86,583,840 qwen-turbo: 50,273,1152026-07-26 — 3,090,499,560 tokens qwen3.8-max-preview: 1,198,887,125 qwen3.6-flash: 542,844,035 90 more models: 466,089,315 qwen3.7-plus: 450,824,260 qwen3.5-flash: 282,952,315 qwen3.6-plus: 97,863,440 qwen-turbo: 51,039,0702026-07-27 — 4,519,148,930 tokens qwen3.8-max-preview: 2,203,514,420 90 more models: 937,001,700 qwen3.6-flash: 707,736,210 qwen3.7-plus: 449,983,775 qwen3.6-plus: 121,276,075 qwen3.5-flash: 62,708,285 qwen-turbo: 36,928,4652026-07-28 — 4,549,309,250 tokens qwen3.8-max-preview: 1,963,488,165 90 more models: 1,040,247,300 qwen3.6-flash: 786,759,720 qwen3.7-plus: 380,238,445 qwen3.5-flash: 150,365,030 qwen3.6-plus: 149,669,280 qwen-turbo: 78,537,435 qwen3.7-flash: 3,8752026-07-29 — 3,810,921,310 tokens qwen3.8-max-preview: 2,389,920,945 90 more models: 853,934,675 qwen3.7-plus: 216,258,305 qwen3.5-flash: 166,864,935 qwen3.6-plus: 92,480,940 qwen-turbo: 76,337,710 qwen3.6-flash: 12,384,600 qwen3.7-flash: 2,739,2002026-07-30 — 4,044,367,870 tokens qwen3.8-max-preview: 2,207,737,960 90 more models: 812,949,530 qwen3.6-plus: 374,370,565 qwen3.7-plus: 321,360,850 qwen3.5-flash: 218,728,495 qwen-turbo: 76,390,225 qwen3.7-flash: 21,108,610 qwen3.6-flash: 11,721,6352026-07-31 — 6,050,270,600 tokens qwen3.8-max-preview: 2,347,146,935 qwen3.6-plus: 1,328,738,480 90 more models: 814,341,825 qwen3.7-plus: 759,129,045 qwen3.5-flash: 457,844,710 qwen3.7-flash: 257,636,590 qwen-turbo: 76,362,270 qwen3.6-flash: 9,070,7452026-08-01 — 3,752,925,355 tokens qwen3.8-max-preview: 1,772,328,855 90 more models: 1,565,409,715 qwen3.7-plus: 172,101,330 qwen-turbo: 100,652,185 qwen3.6-plus: 72,789,450 qwen3.5-flash: 50,944,765 qwen3.7-flash: 13,554,915 qwen3.6-flash: 5,144,1402026-08-02 — 5,181,574,000 tokens qwen-turbo: 2,468,964,135 qwen3.8-max-preview: 1,661,640,115 90 more models: 450,657,245 qwen3.7-plus: 238,782,560 qwen3.6-flash: 199,662,365 qwen3.6-plus: 116,618,775 qwen3.5-flash: 36,978,710 qwen3.7-flash: 8,270,0952026-08-03 — 5,167,803,105 tokens qwen3.8-max-preview: 2,800,461,700 90 more models: 1,195,565,340 qwen3.6-flash: 344,017,520 qwen-turbo: 305,729,715 qwen3.7-plus: 231,178,755 qwen3.8-max: 110,759,875 qwen3.6-plus: 87,920,585 qwen3.5-flash: 75,352,445 qwen3.7-flash: 16,817,1702026-08-04 — 6,877,120,050 tokens qwen3.8-max: 1,698,104,195 qwen3.6-flash: 1,688,407,065 qwen3.8-max-preview: 1,507,134,015 90 more models: 899,314,560 qwen3.5-flash: 573,484,220 qwen-turbo: 222,800,550 qwen3.7-plus: 139,624,050 qwen3.6-plus: 102,481,100 qwen3.7-flash: 45,770,2952026-08-05 — 4,927,219,620 tokens qwen3.8-max-preview: 2,510,885,125 90 more models: 1,143,874,945 qwen3.8-max: 599,977,280 qwen3.5-flash: 262,388,745 qwen3.7-plus: 149,923,155 qwen-turbo: 143,808,750 qwen3.6-plus: 79,908,205 qwen3.6-flash: 24,598,950 qwen3.7-flash: 11,854,4652026-08-06 — 6,027,696,305 tokens qwen3.8-max-preview: 2,920,521,410 qwen3.8-max: 2,195,945,010 90 more models: 470,110,465 qwen3.6-plus: 120,951,140 qwen3.7-plus: 111,498,760 qwen-turbo: 106,235,550 qwen3.5-flash: 79,557,900 qwen3.7-flash: 12,935,575 qwen3.6-flash: 9,940,4952026-08-07 — 77,276,482,745 tokens qwen3.8-max: 74,548,440,955 qwen3.8-max-preview: 1,492,678,730 90 more models: 570,129,955 qwen3.6-plus: 286,990,750 qwen-turbo: 164,632,750 qwen3.5-flash: 153,032,975 qwen3.7-plus: 29,385,650 qwen3.6-flash: 15,702,885 qwen3.7-flash: 15,488,0952026-08-08 — 6,429,860,125 tokens qwen3.8-max: 4,576,704,575 qwen3.6-plus: 797,493,285 90 more models: 416,611,580 qwen-turbo: 296,514,300 qwen3.6-flash: 112,109,005 qwen3.8-max-preview: 104,128,810 qwen3.5-flash: 70,556,940 qwen3.7-plus: 30,505,550 qwen3.7-flash: 25,236,0802026-08-09 — 5,918,303,370 tokens qwen3.8-max: 4,032,315,410 90 more models: 835,059,265 qwen3.6-flash: 534,133,145 qwen3.6-plus: 193,997,155 qwen-turbo: 156,440,540 qwen3.5-flash: 110,362,140 qwen3.7-plus: 33,291,850 qwen3.7-flash: 22,703,8652026-08-10 — 11,750,787,285 tokens qwen3.5-flash: 9,281,864,995 90 more models: 1,250,328,310 qwen3.8-max: 518,681,450 qwen-turbo: 286,715,675 qwen3.6-plus: 219,831,720 qwen3.6-flash: 141,413,605 qwen3.7-plus: 30,584,085 qwen3.7-flash: 21,367,4452026-08-11 — 3,694,289,610 tokens qwen3.8-max: 1,319,453,285 qwen3.6-flash: 816,282,095 90 more models: 670,038,645 qwen3.5-flash: 354,995,350 qwen3.7-plus: 152,983,285 qwen-turbo: 140,146,000 qwen3.6-plus: 135,394,680 qwen3.7-flash: 104,996,2702026-08-12 — 8,003,519,940 tokens qwen3.8-max: 4,686,374,400 qwen3.7-flash: 850,650,565 qwen3.5-flash: 823,784,615 qwen3.6-flash: 651,941,600 90 more models: 550,154,385 qwen3.6-plus: 168,869,530 qwen3.7-plus: 156,662,610 qwen-turbo: 115,082,2352026-08-13 — 10,480,748,430 tokens qwen3.8-max: 6,802,432,565 qwen3.7-flash: 1,243,542,740 90 more models: 846,671,200 qwen3.5-flash: 645,833,790 qwen3.7-plus: 357,808,270 qwen3.6-plus: 343,986,285 qwen-turbo: 163,323,095 qwen3.6-flash: 77,150,4852026-08-14 — 9,774,872,500 tokens qwen3.8-max: 6,589,006,230 qwen3.7-flash: 1,422,951,215 90 more models: 926,703,260 qwen3.6-flash: 428,440,920 qwen3.7-plus: 195,006,400 qwen3.6-plus: 121,146,165 qwen-turbo: 66,017,835 qwen3.5-flash: 25,600,4752026-08-15 — 2,775,435,980 tokens qwen3.8-max: 1,133,576,850 qwen3.7-flash: 673,504,055 90 more models: 633,517,165 qwen-turbo: 125,681,470 qwen3.6-plus: 122,031,570 qwen3.6-flash: 62,465,335 qwen3.7-plus: 15,380,945 qwen3.5-flash: 9,278,5902026-08-16 — 2,225,669,890 tokens 90 more models: 896,610,950 qwen3.8-max: 482,504,605 qwen3.7-flash: 296,680,040 qwen3.6-flash: 278,053,250 qwen-turbo: 155,407,765 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 90 more models: 686,028,535 qwen3.6-flash: 572,522,565 qwen3.8-max: 506,122,370 qwen3.6-plus: 329,280,635 qwen3.7-plus: 186,973,675 qwen-turbo: 71,494,095 qwen3.5-flash: 5,454,9052026-08-18 — 4,109,936,195 tokens qwen3.7-flash: 1,752,326,835 90 more models: 947,157,495 qwen3.8-max: 460,220,560 qwen3.6-plus: 379,968,650 qwen3.7-plus: 215,141,500 qwen3.6-flash: 159,462,595 qwen3.8-max-preview: 133,309,415 qwen-turbo: 46,480,820 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 90 more models: 623,252,020 qwen3.6-plus: 590,422,685 qwen3.8-max: 585,842,470 qwen3.7-plus: 137,264,870 qwen-turbo: 116,610,860 qwen3.8-max-preview: 16,670,3302026-08-20 — 9,314,602,925 tokens qwen3.5-flash: 4,655,296,340 qwen3.7-flash: 2,881,172,015 90 more models: 594,273,890 qwen3.6-plus: 453,331,845 qwen3.7-plus: 332,098,285 qwen3.8-max: 256,483,000 qwen-turbo: 69,273,400 qwen3.6-flash: 55,398,550 qwen3.8-max-preview: 17,275,600
  • qwen3.8-max
  • qwen3.8-max-preview
  • qwen3.5-flash
  • qwen3.7-flash
  • qwen3.6-flash
  • qwen3.6-plus
  • qwen3.7-plus
  • qwen-turbo
  • 90 more models

Which models that traffic went to

  1. Qwen3.8 Max45.6%111B
  2. Qwen3.8 Max Preview12.8%31.1B
  3. Qwen3.5 Flash11.2%27.4B
  4. Qwen3.7 Flash5.4%13.3B
  5. Qwen3.6 Flash4.2%10.3B
  6. Qwen3.6 Plus3.2%7.8B
  7. Qwen3.7 Plus3.1%7.6B
  8. Qwen Turbo2.5%6.1B
  9. 90 more models11.9%29B

Share of 244B tokens. 33 models with traffic report no token counts and cannot be ranked here, including Qwen/Qwen2.5-VL-72B-Instruct and Qwen/Qwen3-32B — 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 141 model IDs listed on this page; traffic routed through upstream-specific IDs that are not in the public catalog is not included.

All 141 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-nextTakes text, returns text.2M64K$0.14$0.55/M——16 tok/s0.51 s
qwen3-coder-30b-a3b-instructTakes text, returns text.2M262K$0.20$0.80/M——115 tok/s0.95 s
qwen3-coder-plusTakes text, returns text.1.05M66K$0.54$2.16/M$0.11/M—83 tok/s1.63 s
qwen3.6-plus-preview-freeTakes text, returns text.1M66KFreeFree/M————
qwen3.7-flashTakes text, returns text.1M64K$0.03$0.11/M$0.0056/M$0.04/M109 tok/s0.92 s
qwen3.7-plusTakes text, returns text.1M64K$0.28$1.13/M$0.06/M$0.35/M40 tok/s3.21 s
qwen3.7-maxTakes text, returns text.1M64K$1.69$5.07/M$0.17/M$2.11/M49 tok/s1.82 s
qwen3.8-2.4t-a95bTakes text, vision, returns text.1M—$2.00$6.00/M$0.50/M—48 tok/s2.40 s
qwen3.5-flashTakes text, vision, video, returns text.991K64K$0.03$0.28/M$0.0028/M$0.04/M50 tok/s2.12 s
qwen3.5-35b-a3bTakes text, vision, video, returns text.991K64K$0.06$0.45/M——71 tok/s1.27 s
qwen3.5-27bTakes text, vision, video, returns text.991K64K$0.08$0.68/M——34 tok/s4.18 s
qwen3.5-plusTakes text, vision, video, returns text.991K64K$0.11$0.66/M$0.01/M$0.14/M40 tok/s1.51 s
qwen3.5-122b-a10bTakes text, vision, video, returns text.991K64K$0.11$0.90/M——41 tok/s1.40 s
qwen3.5-397b-a17bTakes text, vision, video, returns text.991K64K$0.16$0.99/M——8 tok/s1.85 s
qwen3.6-flashTakes text, vision, video, returns text.991K64K$0.17$1.01/M$0.02/M$0.21/M95 tok/s1.67 s
qwen3.6-plusTakes text, vision, video, returns text.991K64K$0.28$1.69/M$0.03/M$0.35/M41 tok/s1.60 s
qwen3.8-max-previewTakes text, vision, returns text.984K131K$0.34$1.01/M$0.07/M$0.42/M48 tok/s2.40 s
qwen3.8-maxTakes text, returns text.984K128K$1.69$5.07/M$0.17/M$2.11/M33 tok/s2.87 s
qwen3.6-35b-a3bTakes text, vision, video, returns text.262K64K$0.25$1.52/M——212 tok/s1.16 s
qwen3-235b-a22b-instruct-2507Takes text, vision, returns text.262K—$0.28$1.12/M——96 tok/s1.03 s
qwen3-235b-a22b-thinking-2507Takes text, vision, returns text.262K—$0.28$2.80/M——87 tok/s0.27 s
qwen3.6-27bTakes text, vision, video, returns text.262K64K$0.42$2.53/M——43 tok/s1.61 s
qwen3-maxTakes text, vision, returns text.262K66K$0.45$1.80/M$0.09/M$0.56/M45 tok/s1.76 s
qwen3-coder-480b-a35b-instructTakes text, returns text.262K—$0.82$3.28/M——1655 tok/s0.92 s
qwen3-coder-flashTakes text, returns text.256K66K$0.14$0.54/M——110 tok/s1.85 s
qwen3-vl-plusTakes text, vision, video, returns text.256K32K$0.14$1.37/M$0.03/M—67 tok/s2.32 s
qwen3-next-80b-a3b-instructTakes text, vision, returns text.256K—$0.14$0.55/M——150 tok/s0.10 s
qwen3-next-80b-a3b-thinkingTakes text, vision, returns text.256K—$0.14$1.42/M——196 tok/s1.00 s
qwen3-vl-flashTakes text, vision, video, returns text.254K32K$0.02$0.21/M$0.0041/M———
qwen3-vl-flash-2026-01-22Takes text, vision, video, returns text.254K32K$0.02$0.21/M————
qwen3-max-2026-01-23Takes text, returns text.252K32K$0.45$1.80/M$0.09/M$0.56/M38 tok/s0.54 s
qwen3.6-max-previewTakes text, returns text.240K64K$1.27$7.61/M$0.13/M$1.58/M42 tok/s8.46 s
qwen3-235b-a22bTakes text, returns text.131K128K$0.28$1.12/M——81 tok/s0.63 s
bai-qwen3-vl-235b-a22b-instructTakes , returns text.131K—$0.27$1.10/M——42 tok/s1.07 s
qwen3-vl-235b-a22b-instructTakes text, vision, video, returns text.131K33K$0.27$1.10/M——57 tok/s1.13 s
qwen3-vl-235b-a22b-thinkingTakes text, vision, video, returns text.131K33K$0.27$2.74/M——58 tok/s2.22 s
qwen3-vl-30b-a3b-instructTakes text, vision, video, returns text.128K32K$0.10$0.41/M——42 tok/s1.07 s
qwen3-vl-30b-a3b-thinkingTakes text, vision, video, returns text.128K32K$0.10$1.03/M——42 tok/s1.49 s
kat-devTakes text, returns text.128K—$0.14$0.55/M————
Qwen/Qwen2.5-VL-72B-InstructTakes text, vision, video, returns text.128K—$0.50$0.50/M————
qwen3-coder-plus-2025-07-22Takes text, returns text.128K66K$0.54$2.16/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$0.19$0.53/M——18 tok/s0.73 s
qwen-mt-plusTakes text, returns text.16K8K$0.49$1.48/M——13 tok/s0.41 s
qwen-audio-3.0-tts-flashTakes text, returns audio.——Free$0.14/M————
qwen-audio-3.0-tts-plusTakes text, returns audio.——Free$0.20/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$0.20/M————
qwen-flash-2025-07-28——$0.02$0.20/M————
qwen-turboTakes text, returns text.——$0.05$0.09/M$0.0092/M—94 tok/s0.66 s
qwen-turbo-2024-11-01Takes text, returns text.——$0.05$0.09/M——94 tok/s0.66 s
qwen-turbo-2025-04-28Takes , returns text.——$0.05$0.09/M————
qwen-turbo-latestTakes , returns text.——$0.05$0.09/M$0.0092/M———
qwen3-0.6bTakes , returns text.——$0.05$0.46/M————
qwen3-1.7bTakes , returns text.——$0.05$0.46/M————
qwen3-4bTakes , returns text.——$0.05$0.46/M————
bce-reranker-baseTakes text, vision. Output modality not published.——$0.07$0.07/M————
qwen3-embedding-0.6bTakes text. Output modality not published.——$0.07$0.07/M————
qwen3-embedding-4bTakes text. Output modality not published.——$0.07$0.07/M————
qwen3-embedding-8bTakes text. Output modality not published.——$0.07$0.07/M————
Qwen/Qwen2-7B-Instruct——$0.08$0.08/M————
qwen3-8bTakes , returns text.——$0.08$0.80/M————
text-embedding-v4Takes text. Output modality not published.——$0.08$0.08/M————
qwen-long——$0.10$0.40/M————
qwen3-30b-a3b-instruct-2507——$0.10$0.41/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——$0.11$1.13/M$0.02/M$0.14/M——
qwen-plus-2025-04-28Takes , returns text.——$0.11$1.13/M$0.02/M$0.14/M——
qwen-plus-2025-07-28——$0.11$1.13/M$0.02/M$0.14/M——
qwen-plus-latestTakes , returns text.——$0.11$1.13/M$0.02/M$0.14/M——
qwen3-30b-a3b——$0.12$1.20/M————
qwen3-30b-a3b-thinking-2507——$0.12$1.20/M————
gme-qwen2-vl-2b-instructTakes text, vision, video. Output modality not published.——$0.14$0.14/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$1.60/M————
qwen3-32b——$0.16$0.64/M——117 tok/s1.31 s
Qwen/Qwen2-1.5B-Instruct——$0.20$0.20/M————
Qwen/Qwen3-8B——$0.20$0.20/M————
qwen2.5-coder-1.5b-instruct——$0.20$0.40/M————
qwen2.5-coder-7b-instruct——$0.20$0.40/M————
qwen2.5-math-1.5b-instruct——$0.20$0.20/M————
qwen2.5-math-7b-instruct——$0.20$0.40/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$1.40/M————
qwen-3-235b-a22b-thinking-2507——$0.28$2.80/M————
Qwen2-VL-7B-InstructTakes text, vision, video. Output modality not published.——$0.28$0.70/M————
Qwen3-235B-A22B-Thinking-2507——$0.28$2.80/M——87 tok/s0.27 s
qwen-max——$0.38$1.52/M————
qwen-max-0125——$0.38$1.52/M————
qwen-3-32b——$0.40$1.60/M————
qwen-qwq-32b——$0.40$0.80/M————
Qwen/Qwen2.5-7B-Instruct——$0.40$0.40/M————
Qwen/Qwen3-32B——$0.40$0.80/M————
qwen2.5-14b-instruct——$0.40$1.20/M————
qwen2.5-3b-instruct——$0.40$0.80/M————
qwen2.5-7b-instruct——$0.40$0.80/M————
Qwen/Qwen3-14B——$0.50$0.50/M————
Qwen/Qwen2.5-32B-Instruct——$0.60$0.60/M————
qwen2.5-32b-instruct——$0.60$1.20/M————
Qwen2.5-VL-72B-InstructTakes text, vision, video. Output modality not published.——$0.62$0.62/M————
Qwen/Qwen2-72B-Instruct——$0.80$0.80/M————
Qwen/Qwen2.5-72B-Instruct——$0.80$0.80/M————
Qwen/Qwen2.5-72B-Instruct-128K——$0.80$0.80/M————
qwen2.5-72b-instruct——$0.80$2.40/M————
qwen2.5-math-72b-instruct——$0.80$2.40/M————
qwen3-max-previewTakes text, vision, returns text.——$0.85$3.38/M$0.17/M—8 tok/s0.62 s
Qwen/Qwen3-30B-A3B——$1.00$1.00/M————
Qwen/QVQ-72B-Preview——$1.20$1.20/M————
qwen-imageTakes text, vision, returns vision.——$2.00$2.00/M————
qwen-image-2.0Takes text, vision, returns vision.——$2.00$2.00/M————
qwen-image-2.0-proTakes text, vision, returns vision.——$2.00$2.00/M————
qwen-image-editTakes text, vision, returns vision.——$2.00$2.00/M————
qwen-image-maxTakes text, vision, returns vision.——$2.00$2.00/M————
wan2.7-imageTakes text, vision, returns vision.——$2.00$2.00/M————
wan2.7-image-proTakes text, vision, returns vision.——$2.00$2.00/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.——$2.40$7.20/M————
qwen-max-longcontext——$7.00$21.00/M————
wan2.2-i2v-plusTakes text, vision, returns video.——$480.00$480.00/M————
wan2.5-i2v-previewTakes text, vision, returns video.——$480.00$480.00/M————
wan2.5-t2v-previewTakes text, returns video.——$480.00$480.00/M————
happyhorse-1.0-i2vTakes text, vision, video, returns video.——$720.00$720.00/M————
happyhorse-1.0-r2vTakes text, video, returns video.——$720.00$720.00/M————
happyhorse-1.0-t2vTakes text, video, returns video.——$720.00$720.00/M————
happyhorse-1.0-video-editTakes text, vision, video, returns video.——$720.00$720.00/M————
happyhorse-1.1-i2vTakes text, vision, video, returns video.——$720.00$720.00/M————
happyhorse-1.1-r2vTakes text, video, returns video.——$720.00$720.00/M————
happyhorse-1.1-t2vTakes text, video, returns video.——$720.00$720.00/M————
wan2.6-i2vTakes text, vision, returns video.——$720.00$720.00/M————
wan2.6-t2iTakes text, vision, returns vision.——$720.00$720.00/M————
wan2.6-t2vTakes text, returns video.——$720.00$720.00/M————
wan2.7-i2vTakes text, vision, returns video.——$720.00$720.00/M————
wan2.7-r2vTakes text, video, returns video.——$720.00$720.00/M————
wan2.7-t2vTakes text, returns video.——$720.00$720.00/M————
wan2.7-videoeditTakes text, video, returns video.——$720.00$720.00/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 $0.02/M input — the cheapest entry here that declares tool calling, and it carries a 254K 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?

23 of the 141 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

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