Qwen Models

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

Usage

Last 30 days · 2026-10-02 to 2026-10-31

Tokens

134B

Requests

5M

Models in use

153 of 154

Tokens per day, stacked by model

04.7B9.3B10-0210-0910-1610-2310-302026-10-02 — 5,860,575,690 tokens qwen3.8-flash: 3,905,171,175 87 more models: 569,979,755 qwen3.8-max: 550,423,985 qwen3.7-flash: 425,506,845 qwen3.8-omni-flash: 228,133,805 qwen3.8-max-2026-09-02: 91,669,880 qwen3.5-flash: 66,709,875 qwen3.5-35b-a3b: 22,758,315 qwen3-vl-plus: 222,0552026-10-03 — 4,226,544,995 tokens qwen3.8-flash: 2,228,262,785 qwen3.8-omni-flash: 713,468,610 qwen3.7-flash: 561,065,235 87 more models: 335,302,050 qwen3.8-max: 294,286,845 qwen3.5-flash: 52,800,720 qwen3.5-35b-a3b: 40,840,980 qwen3-vl-plus: 341,790 qwen3.8-max-2026-09-02: 175,9802026-10-04 — 4,016,658,765 tokens qwen3.8-flash: 2,044,922,020 qwen3.8-omni-flash: 580,886,185 qwen3.7-flash: 486,838,295 87 more models: 439,310,930 qwen3.5-flash: 169,690,110 qwen3.8-max: 160,744,910 qwen3.5-35b-a3b: 124,146,380 qwen3.8-max-2026-09-02: 9,935,590 qwen3-vl-plus: 184,3452026-10-05 — 5,321,499,485 tokens qwen3.8-flash: 2,645,542,155 qwen3.8-omni-flash: 1,228,697,370 qwen3.7-flash: 463,815,695 87 more models: 403,217,845 qwen3.8-max-2026-09-02: 274,110,140 qwen3.8-max: 157,898,475 qwen3.5-35b-a3b: 143,645,600 qwen3.5-flash: 4,375,135 qwen3-vl-plus: 197,0702026-10-06 — 3,772,301,525 tokens qwen3.8-flash: 2,044,610,845 87 more models: 537,654,495 qwen3.8-max: 416,233,610 qwen3.7-flash: 366,043,405 qwen3.8-omni-flash: 313,993,260 qwen3.5-35b-a3b: 47,576,640 qwen3.8-max-2026-09-02: 29,335,135 qwen3.5-flash: 16,606,550 qwen3-vl-plus: 247,5852026-10-07 — 4,488,586,695 tokens qwen3.8-flash: 1,836,515,610 qwen3.8-max: 859,371,825 qwen3.7-flash: 660,545,155 87 more models: 543,151,350 qwen3.8-omni-flash: 492,556,300 qwen3.5-35b-a3b: 48,564,385 qwen3.8-max-2026-09-02: 31,634,345 qwen3.5-flash: 13,882,385 qwen3-vl-plus: 2,365,3402026-10-08 — 5,147,478,365 tokens qwen3.8-flash: 2,115,667,600 qwen3.7-flash: 890,578,295 87 more models: 806,583,140 qwen3.8-max: 428,372,080 qwen3.5-flash: 395,787,080 qwen3.8-omni-flash: 339,073,780 qwen3-vl-plus: 123,603,580 qwen3.5-35b-a3b: 41,887,020 qwen3.8-max-2026-09-02: 5,925,7902026-10-09 — 7,577,614,045 tokens qwen3.8-flash: 2,452,962,435 qwen3.7-flash: 1,327,124,410 qwen3.8-omni-flash: 1,266,718,525 87 more models: 1,185,457,670 qwen3.8-max: 635,633,310 qwen3-vl-plus: 439,214,060 qwen3.8-max-2026-09-02: 119,903,405 qwen3.5-flash: 105,561,940 qwen3.5-35b-a3b: 45,038,2902026-10-10 — 4,782,052,240 tokens qwen3.8-flash: 2,066,618,175 qwen3.7-flash: 1,096,032,300 qwen3-vl-plus: 582,144,170 87 more models: 426,760,680 qwen3.5-35b-a3b: 196,873,650 qwen3.8-omni-flash: 184,900,010 qwen3.5-flash: 157,322,815 qwen3.8-max: 61,394,130 qwen3.8-max-2026-09-02: 10,006,3102026-10-11 — 9,334,828,415 tokens qwen3.8-flash: 5,805,593,090 qwen3.7-flash: 918,793,220 qwen3.5-flash: 812,118,530 qwen3-vl-plus: 684,227,580 87 more models: 330,218,785 qwen3.8-max-2026-09-02: 311,938,765 qwen3.8-omni-flash: 197,479,385 qwen3.5-35b-a3b: 150,390,700 qwen3.8-max: 124,068,3602026-10-12 — 7,275,532,620 tokens qwen3.8-flash: 3,693,538,820 qwen3.7-flash: 1,355,318,745 qwen3.8-omni-flash: 1,117,968,920 qwen3.5-flash: 386,510,710 qwen3.8-max: 301,527,245 87 more models: 220,146,760 qwen3.8-max-2026-09-02: 145,997,650 qwen3.5-35b-a3b: 47,417,085 qwen3-vl-plus: 7,106,6852026-10-13 — 7,157,834,350 tokens qwen3.7-flash: 3,434,348,280 qwen3.8-flash: 2,394,278,555 qwen3.8-max: 503,569,555 87 more models: 333,295,940 qwen3-vl-plus: 263,866,010 qwen3.8-max-2026-09-02: 134,045,990 qwen3.5-35b-a3b: 45,446,465 qwen3.8-omni-flash: 35,198,445 qwen3.5-flash: 13,785,1102026-10-14 — 3,693,926,410 tokens qwen3.8-flash: 1,523,000,000 qwen3.7-flash: 1,071,472,660 qwen3.8-max: 464,008,720 87 more models: 336,276,475 qwen3.8-omni-flash: 128,893,760 qwen3.5-flash: 59,898,500 qwen3.8-max-2026-09-02: 53,206,570 qwen3.5-35b-a3b: 52,690,675 qwen3-vl-plus: 4,479,0502026-10-15 — 3,315,096,655 tokens qwen3.7-flash: 1,748,219,815 qwen3.8-flash: 544,923,040 qwen3.8-max: 481,457,010 87 more models: 295,601,865 qwen3.8-omni-flash: 110,815,065 qwen3.8-max-2026-09-02: 58,848,250 qwen3.5-35b-a3b: 41,953,250 qwen3.5-flash: 32,751,965 qwen3-vl-plus: 526,3952026-10-16 — 2,891,121,440 tokens 87 more models: 1,098,160,405 qwen3.8-flash: 646,581,630 qwen3.8-max: 398,555,440 qwen3.7-flash: 345,634,095 qwen3.8-max-2026-09-02: 290,232,530 qwen3.8-omni-flash: 37,414,040 qwen3.5-35b-a3b: 36,653,915 qwen3-vl-plus: 22,408,455 qwen3.5-flash: 15,480,9302026-10-17 — 1,625,961,395 tokens qwen3.8-max: 443,373,510 qwen3.8-flash: 362,492,450 87 more models: 333,431,175 qwen3.8-omni-flash: 144,912,425 qwen3.7-flash: 136,777,610 qwen3.8-max-2026-09-02: 136,021,065 qwen3.5-35b-a3b: 62,164,815 qwen3.5-flash: 6,644,340 qwen3-vl-plus: 144,0052026-10-18 — 2,946,415,920 tokens qwen3.8-max: 1,357,385,360 87 more models: 741,916,055 qwen3.8-flash: 590,164,680 qwen3.8-max-2026-09-02: 129,504,970 qwen3.7-flash: 70,791,805 qwen3.5-35b-a3b: 37,741,725 qwen3.5-flash: 8,869,895 qwen3.8-omni-flash: 8,777,900 qwen3-vl-plus: 1,263,5302026-10-19 — 3,982,893,850 tokens qwen3.8-max: 1,305,157,685 qwen3.8-flash: 993,501,460 87 more models: 717,930,515 qwen3.7-flash: 457,805,405 qwen3.5-35b-a3b: 185,502,065 qwen3.5-flash: 165,824,895 qwen3.8-max-2026-09-02: 104,122,490 qwen3-vl-plus: 39,554,990 qwen3.8-omni-flash: 13,494,3452026-10-20 — 3,921,547,840 tokens qwen3.7-flash: 1,316,439,000 qwen3.8-max: 999,829,165 87 more models: 699,945,475 qwen3.8-flash: 592,647,695 qwen3.8-max-2026-09-02: 235,911,905 qwen3.5-35b-a3b: 34,089,190 qwen3.5-flash: 26,480,930 qwen3.8-omni-flash: 12,883,645 qwen3-vl-plus: 3,320,8352026-10-21 — 3,958,954,575 tokens qwen3.7-flash: 1,540,856,865 qwen3.8-max: 939,579,895 qwen3.8-flash: 622,871,365 87 more models: 486,643,490 qwen3.8-max-2026-09-02: 182,985,085 qwen3.5-flash: 62,206,150 qwen3.8-omni-flash: 53,940,430 qwen3-vl-plus: 37,850,565 qwen3.5-35b-a3b: 32,020,7302026-10-22 — 7,259,240,395 tokens qwen3.7-flash: 3,016,196,995 qwen3.8-max: 2,040,288,275 qwen3.8-flash: 1,029,155,220 87 more models: 867,804,520 qwen3.8-max-2026-09-02: 161,079,505 qwen3.5-35b-a3b: 80,192,785 qwen3.8-omni-flash: 48,935,895 qwen3-vl-plus: 9,289,480 qwen3.5-flash: 6,297,7202026-10-23 — 4,628,133,845 tokens qwen3.8-max: 1,614,917,365 qwen3.7-flash: 1,062,414,945 87 more models: 680,751,215 qwen3.8-flash: 573,717,070 qwen3.8-max-2026-09-02: 350,367,505 qwen3.5-flash: 187,548,500 qwen3.8-omni-flash: 109,182,760 qwen3.5-35b-a3b: 46,762,310 qwen3-vl-plus: 2,472,1752026-10-24 — 3,208,310,730 tokens qwen3.7-flash: 1,329,084,995 qwen3.8-max: 1,058,198,455 qwen3.8-flash: 381,022,830 87 more models: 311,087,725 qwen3.5-35b-a3b: 56,290,480 qwen3.8-max-2026-09-02: 32,920,655 qwen3.8-omni-flash: 26,617,480 qwen3.5-flash: 10,074,065 qwen3-vl-plus: 3,014,0452026-10-25 — 4,938,822,005 tokens qwen3.7-flash: 2,088,296,585 qwen3.8-flash: 1,759,604,080 qwen3.8-max: 780,432,125 87 more models: 184,499,740 qwen3.5-35b-a3b: 52,140,140 qwen3.8-max-2026-09-02: 31,352,230 qwen3.5-flash: 31,151,650 qwen3.8-omni-flash: 8,756,420 qwen3-vl-plus: 2,589,0352026-10-26 — 3,529,473,935 tokens qwen3.7-flash: 1,954,945,295 qwen3.8-max: 689,679,645 87 more models: 536,273,495 qwen3.8-flash: 205,460,180 qwen3.8-omni-flash: 49,566,965 qwen3.5-35b-a3b: 45,902,465 qwen3.8-max-2026-09-02: 30,097,595 qwen3.5-flash: 8,793,790 qwen3-vl-plus: 8,754,5052026-10-27 — 5,694,516,490 tokens qwen3.8-flash: 3,132,538,790 qwen3.7-flash: 1,136,441,620 qwen3.8-max: 749,365,570 87 more models: 450,471,950 qwen3.5-flash: 88,205,785 qwen3.5-35b-a3b: 54,277,865 qwen3.8-omni-flash: 42,406,115 qwen3.8-max-2026-09-02: 24,482,175 qwen3-vl-plus: 16,326,6202026-10-28 — 4,109,183,645 tokens qwen3.7-flash: 1,429,676,055 qwen3.8-flash: 1,269,437,855 qwen3.8-max: 611,507,930 87 more models: 345,283,435 qwen3.5-flash: 219,316,065 qwen3.8-omni-flash: 122,212,900 qwen3.5-35b-a3b: 57,994,160 qwen3.8-max-2026-09-02: 33,978,820 qwen3-vl-plus: 19,776,4252026-10-29 — 2,457,725,920 tokens qwen3.8-flash: 1,211,007,975 qwen3.7-flash: 398,904,775 qwen3.8-max: 378,157,145 87 more models: 263,585,595 qwen3.8-omni-flash: 125,915,590 qwen3.5-35b-a3b: 37,819,485 qwen3.8-max-2026-09-02: 23,751,280 qwen3.5-flash: 9,651,025 qwen3-vl-plus: 8,933,0502026-10-30 — 1,439,174,675 tokens qwen3.7-flash: 427,463,885 qwen3.5-flash: 293,373,225 87 more models: 293,134,085 qwen3.8-flash: 250,589,550 qwen3.8-max: 108,638,075 qwen3.5-35b-a3b: 47,257,100 qwen3-vl-plus: 9,614,180 qwen3.8-omni-flash: 7,661,460 qwen3.8-max-2026-09-02: 1,443,1152026-10-31 — 1,104,234,745 tokens 87 more models: 442,100,535 qwen3.7-flash: 236,639,630 qwen3.8-flash: 215,228,820 qwen3.8-max: 105,236,795 qwen3.5-35b-a3b: 54,914,545 qwen3.5-flash: 30,607,795 qwen3.8-omni-flash: 13,341,710 qwen3.8-max-2026-09-02: 4,442,460 qwen3-vl-plus: 1,722,455
  • qwen3.8-flash
  • qwen3.7-flash
  • qwen3.8-max
  • qwen3.8-omni-flash
  • qwen3.5-flash
  • qwen3.8-max-2026-09-02
  • qwen3-vl-plus
  • qwen3.5-35b-a3b
  • 87 more models

Which models that traffic went to

  1. Qwen3.8 Flash36.8%49.1B
  2. Qwen3.7 Flash23.8%31.8B
  3. Qwen3.8 Max14.2%19B
  4. Qwen3.8 Omni Flash5.8%7.8B
  5. Qwen3.5 Flash2.6%3.5B
  6. Qwen3.8 Max 2026 09-022.3%3B
  7. Qwen3 VL Plus1.7%2.3B
  8. Qwen3.5 35B A3B1.5%2B
  9. 87 more models11.4%15.2B

Share of 134B tokens. 58 models with traffic report no token counts and cannot be ranked here, including qwen-image-3.0 and qwen-image-3.0-pro — 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 154 model IDs listed on this page; traffic routed through upstream-specific IDs that are not in the public catalog is not included.

All 154 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—83 tok/s1.63 s
qwen3.6-plus-preview-freeTakes text, returns text.1M66KFreeFree/M————
qwen3.8-flash-freeTakes text, vision, video, returns text.1M131KFreeFree/M————
qwen3.8-max-freeTakes text, vision, video, returns text.1M131KFreeFree/M————
qwen3.5-flashTakes text, vision, video, returns text.1M66K$0.0282$0.282/M$0.0028/M$0.0352/M44 tok/s0.87 s
qwen3.7-flashTakes text, vision, video, returns text.1M131K$0.0282$0.1128/M$0.0056/M$0.0352/M81 tok/s0.71 s
qwen3.5-plusTakes text, vision, video, returns text.1M66K$0.1096$0.6576/M$0.011/M$0.137/M51 tok/s1.18 s
qwen3.8-flashTakes text, vision, video, returns text.1M131K$0.1126$0.38/M$0.0141/M$0.1759/M33 tok/s64.57 s
qwen3.8-omni-flashTakes text, vision, audio, video, returns text.1M131K$0.1126$0.38/M$0.0141/M$0.1759/M4 tok/s32.33 s
qwen3-coder-flashTakes text, returns text.1M66K$0.136$0.544/M——57 tok/s2.62 s
qwen3.6-flashTakes text, vision, video, returns text.1M66K$0.169$1.014/M$0.0169/M$0.2112/M29 tok/s124.96 s
qwen3.8-27bTakes text, vision, video, returns text.1M131K$0.2$2.5/M$0.05/M—55 tok/s1.35 s
qwen3.6-plusTakes text, vision, video, returns text.1M66K$0.282$1.692/M$0.0282/M$0.3525/M45 tok/s6.05 s
qwen3.7-plusTakes text, vision, video, returns text.1M131K$0.282$1.128/M$0.0564/M$0.3525/M17 tok/s29.69 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/M35 tok/s6.03 s
qwen3.8-maxTakes text, vision, video, returns text.1M131K$1.69$5.07/M$0.169/M$2.1125/M31 tok/s35.98 s
qwen3.8-max-2026-09-02Takes text, vision, video, returns text.1M131K$1.69$5.07/M$0.169/M$2.1125/M22 tok/s8.48 s
qwen3.8-2.4t-a95bTakes text, returns text.1M131K$2$6/M$0.5/M—48 tok/s2.40 s
qwen3-vl-flashTakes text, vision, video, returns text.262K33K$0.0206$0.206/M$0.0041/M—5 tok/s0.34 s
qwen3-vl-flash-2026-01-22Takes text, vision, video, returns text.262K33K$0.0206$0.206/M——2 tok/s1.04 s
qwen3.5-35b-a3bTakes text, vision, video, returns text.262K66K$0.0564$0.4512/M——61 tok/s3.70 s
qwen3.5-27bTakes text, vision, video, returns text.262K66K$0.0846$0.6768/M——47 tok/s1.11 s
qwen3.5-122b-a10bTakes text, vision, video, returns text.262K66K$0.1126$0.9008/M——98 tok/s15.36 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——7 tok/s24.92 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——110 tok/s3.10 s
qwen3-235b-a22b-instruct-2507Takes text, vision, returns text.262K16K$0.28$1.12/M——96 tok/s1.03 s
qwen3-235b-a22b-thinking-2507Takes text, vision, returns text.262K131K$0.28$2.8/M——87 tok/s0.27 s
qwen3-235b-a22b-2507Takes text, returns text.262K—$0.35$1.4/M$0.07/M———
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/M36 tok/s2.41 s
qwen3-max-2026-01-23Takes text, returns text.262K66K$0.4508$1.8032/M$0.0902/M$0.5635/M——
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——109 tok/s0.55 s
qwen3-vl-30b-a3b-instructTakes text, vision, video, returns text.131K33K$0.1028$0.4112/M——42 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——576 tok/s0.43 s
bai-qwen3-vl-235b-a22b-instructTakes , returns text.131K—$0.274$1.096/M——5 tok/s0.39 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
decision-model-previewTakes text. Output modality not published.64K—FreeFree/M————
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——1 tok/s1.10 s
qwen-mt-plusTakes text, returns text.16K8K$0.492$1.476/M——32 tok/s0.34 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————
wan3.0-video-freeTakes text, vision, audio, video, PDF, returns video.——FreeFree/M————
wan3.0-video-prime-freeTakes text, vision, audio, video, PDF, returns video.——FreeFree/M————
qwen-flashTakes , returns text.——$0.02$0.2/M——8 tok/s0.36 s
qwen-flash-2025-07-28Takes , returns text.——$0.02$0.2/M——120 tok/s0.53 s
qwen-turboTakes text, returns text.——$0.046$0.092/M$0.0092/M—23 tok/s0.84 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-InstructTakes , returns text.——$0.08$0.08/M————
qwen3-8bTakes , returns text.——$0.08$0.8/M——62 tok/s0.39 s
text-embedding-v4Takes text. Output modality not published.——$0.08$0.08/M————
qwen-longTakes , returns text.——$0.1$0.4/M——24 tok/s0.62 s
qwen3-30b-a3b-instruct-2507Takes , returns text.——$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-plusTakes , returns text.——$0.1126$1.126/M$0.0225/M$0.1407/M19 tok/s1.36 s
qwen-plus-2025-04-28Takes , returns text.——$0.1126$1.126/M$0.0225/M$0.1407/M2 tok/s0.42 s
qwen-plus-2025-07-28Takes , returns text.——$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/M36 tok/s0.50 s
qwen3-30b-a3bTakes , returns text.——$0.12$1.2/M————
qwen3-30b-a3b-thinking-2507Takes , returns text.——$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-InstructTakes , returns text.——$0.16$0.16/M————
Qwen/QwQ-32B-PreviewTakes , returns text.——$0.16$0.16/M————
qwen3-14bTakes , returns text.——$0.16$1.6/M——59 tok/s0.31 s
qwen3-32bTakes , returns text.——$0.16$0.64/M————
Qwen/Qwen2-1.5B-InstructTakes , returns text.——$0.2$0.2/M————
Qwen/Qwen3-8BTakes , returns text.——$0.2$0.2/M——11 tok/s3.31 s
qwen2.5-coder-1.5b-instructTakes , returns text.——$0.2$0.4/M————
qwen2.5-coder-7b-instructTakes , returns text.——$0.2$0.4/M————
qwen2.5-math-1.5b-instructTakes , returns text.——$0.2$0.2/M————
qwen2.5-math-7b-instructTakes , returns text.——$0.2$0.4/M————
Qwen/Qwen2-57B-A14B-InstructTakes , returns text.——$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-2507Takes , returns text.——$0.28$1.4/M————
qwen-3-235b-a22b-thinking-2507Takes , returns text.——$0.28$2.8/M——116 tok/s0.16 s
Qwen2-VL-7B-InstructTakes text, vision, video, returns text.——$0.28$0.7/M————
Qwen3-235B-A22B-Thinking-2507Takes , returns text.——$0.28$2.8/M——87 tok/s0.27 s
qwen-maxTakes , returns text.——$0.38$1.52/M——19 tok/s0.41 s
qwen-max-0125Takes , returns text.——$0.38$1.52/M————
qwen-3-32bTakes , returns text.——$0.4$1.6/M————
qwen-qwq-32bTakes , returns text.——$0.4$0.8/M————
Qwen/Qwen2.5-7B-InstructTakes , returns text.——$0.4$0.4/M————
Qwen/Qwen3-32BTakes , returns text.——$0.4$0.8/M————
qwen2.5-14b-instructTakes , returns text.——$0.4$1.2/M————
qwen2.5-3b-instructTakes , returns text.——$0.4$0.8/M————
qwen2.5-7b-instructTakes , returns text.——$0.4$0.8/M————
Qwen/Qwen3-14BTakes , returns text.——$0.5$0.5/M————
Qwen/Qwen2.5-32B-InstructTakes , returns text.——$0.6$0.6/M——24 tok/s1.62 s
qwen2.5-32b-instructTakes , returns text.——$0.6$1.2/M————
Qwen/Qwen2-72B-InstructTakes , returns text.——$0.8$0.8/M————
Qwen/Qwen2.5-72B-InstructTakes , returns text.——$0.8$0.8/M————
Qwen/Qwen2.5-72B-Instruct-128KTakes , returns text.——$0.8$0.8/M————
qwen2.5-72b-instructTakes , returns text.——$0.8$2.4/M————
qwen2.5-math-72b-instructTakes , returns text.——$0.8$2.4/M————
qwen3-max-previewTakes text, vision, returns text.——$0.846$3.384/M$0.1692/M—2 tok/s0.58 s
Qwen/Qwen3-30B-A3BTakes , returns text.——$1$1/M————
Qwen/QVQ-72B-PreviewTakes , returns text.——$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-2.1-proTakes text, vision, returns vision.——$2$2/M————
qwen-image-2.1-turboTakes 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, returns text.——$2.18$6.54/M————
qwen2.5-vl-72b-instructTakes text, vision, returns text.——$2.4$7.2/M————
qwen-max-longcontextTakes , returns text.——$7$21/M——17 tok/s5.61 s
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/QVQ-72B-Preview vs Qwen/QwQ-32BQwen/QVQ-72B-Preview vs Qwen/QwQ-32B-PreviewQwen/QVQ-72B-Preview vs Qwen/Qwen2-1.5B-InstructQwen/QVQ-72B-Preview vs kimi-k3Qwen/QVQ-72B-Preview 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 154 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.

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