Z.AI Models

71 modelsGeneral models free to startUp to 1.05M contextOfficial site ↗

Usage

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

Tokens

872B

Requests

5.2M

Models in use

59 of 71

Tokens per day, stacked by model

025.8B51.6B08-1608-2308-3009-0609-132026-08-16 — 10,858,625,150 tokens coding-glm-5.3: 6,240,755,890 41 more models: 1,820,904,955 glm-5.2: 1,277,709,835 coding-glm-5: 968,461,655 coding-glm-4.7: 550,792,8152026-08-17 — 10,885,006,880 tokens coding-glm-5.3: 4,856,441,140 glm-5.2: 3,542,775,825 41 more models: 1,098,274,490 coding-glm-5: 846,574,650 coding-glm-4.7: 534,609,745 glm-5.3: 6,331,0302026-08-18 — 12,680,941,755 tokens coding-glm-5.3: 5,588,652,325 glm-5.2: 3,295,448,645 41 more models: 1,624,162,300 coding-glm-4.7: 801,399,540 coding-glm-5: 755,660,520 glm-5.3: 615,618,4252026-08-19 — 16,957,620,515 tokens coding-glm-5.3: 10,813,449,925 glm-5.3: 2,034,350,875 41 more models: 1,354,511,255 glm-5.2: 1,311,421,615 coding-glm-4.7: 774,367,860 coding-glm-5: 669,518,9852026-08-20 — 18,954,357,385 tokens coding-glm-5.3: 10,854,316,955 glm-5.3: 3,115,697,245 glm-5.2: 1,984,767,335 41 more models: 1,697,217,510 coding-glm-5: 700,718,890 coding-glm-4.7: 601,639,4502026-08-21 — 14,319,820,885 tokens coding-glm-5.3: 6,400,303,505 glm-5.3: 3,551,356,595 41 more models: 1,865,657,570 glm-5.2: 914,098,525 coding-glm-4.7: 836,180,500 coding-glm-5: 752,224,1902026-08-22 — 11,655,525,000 tokens glm-5.3: 5,314,556,755 coding-glm-5.3: 3,263,978,225 41 more models: 1,401,273,905 coding-glm-5: 749,770,845 coding-glm-4.7: 493,553,955 glm-5.2: 432,391,3152026-08-23 — 7,972,566,555 tokens glm-5.3: 5,254,336,005 41 more models: 1,342,316,265 coding-glm-5: 729,098,185 coding-glm-4.7: 437,156,505 glm-5.2: 209,659,5952026-08-24 — 12,228,887,180 tokens glm-5.3: 7,151,130,030 41 more models: 2,393,443,970 coding-glm-5.3: 1,057,873,455 glm-5.2: 677,333,920 coding-glm-5: 638,361,775 coding-glm-4.7: 310,744,0302026-08-25 — 13,624,077,940 tokens glm-5.3: 5,884,650,075 coding-glm-5.3: 2,837,134,225 41 more models: 2,208,173,575 glm-5.2: 1,268,252,005 coding-glm-5: 922,107,850 coding-glm-4.7: 503,760,2102026-08-26 — 13,319,822,160 tokens glm-5.3: 6,189,403,720 coding-glm-5.3: 2,201,414,415 glm-5.2: 1,875,141,405 41 more models: 1,600,348,560 coding-glm-4.7: 574,062,960 coding-glm-5: 564,909,435 coding-glm-5.3-free: 307,583,775 glm-5.3-flash: 6,957,8902026-08-27 — 42,417,976,010 tokens glm-5.3-flash: 18,544,831,100 coding-glm-5.3: 8,942,687,205 glm-5.3: 5,401,025,930 41 more models: 3,510,090,825 coding-glm-5.3-free: 2,911,460,015 coding-glm-5.3-flash: 1,270,865,940 coding-glm-5: 675,621,425 coding-glm-4.7: 581,479,780 glm-5.2: 579,913,7902026-08-28 — 49,882,099,935 tokens glm-5.3-flash: 24,967,167,410 glm-5.3: 6,644,999,460 coding-glm-5.3: 6,473,492,100 coding-glm-5.3-flash: 4,668,933,520 41 more models: 3,783,917,150 coding-glm-5.3-free: 1,855,862,725 coding-glm-4.7: 702,247,515 coding-glm-5: 638,659,035 glm-5.2: 146,821,0202026-08-29 — 42,255,269,785 tokens glm-5.3-flash: 19,797,770,380 coding-glm-5.3: 9,379,725,515 coding-glm-5.3-flash: 4,368,748,005 glm-5.3: 3,828,966,350 41 more models: 2,612,391,720 coding-glm-5.3-free: 1,087,920,470 coding-glm-5: 546,139,410 coding-glm-4.7: 468,092,615 glm-5.2: 165,515,3202026-08-30 — 38,240,889,900 tokens glm-5.3-flash: 16,887,788,695 coding-glm-5.3-flash: 7,075,907,190 coding-glm-5.3: 6,985,081,055 glm-5.3: 2,517,170,120 41 more models: 2,374,616,490 coding-glm-5.3-free: 992,227,435 coding-glm-4.7: 657,528,045 coding-glm-5: 509,579,135 glm-5.2: 240,991,7352026-08-31 — 43,047,594,125 tokens glm-5.3-flash: 30,310,025,780 glm-5.3: 6,139,648,995 coding-glm-5.3: 2,144,963,070 coding-glm-5.3-flash: 1,676,527,185 41 more models: 1,184,430,000 coding-glm-5.3-free: 646,405,490 coding-glm-5: 393,732,375 coding-glm-4.7: 304,180,545 glm-5.2: 247,680,6852026-09-01 — 51,461,371,390 tokens glm-5.3-flash: 31,654,991,910 glm-5.3: 9,091,797,005 coding-glm-5.3-flash: 4,915,068,365 coding-glm-5.3: 1,835,721,165 41 more models: 1,661,284,495 coding-glm-5.3-free: 725,439,385 glm-5.2: 607,777,805 coding-glm-4.7: 517,627,840 coding-glm-5: 451,663,4202026-09-02 — 46,666,343,135 tokens glm-5.3-flash: 26,759,614,510 coding-glm-5.3-flash: 7,740,056,995 glm-5.3: 5,170,680,560 coding-glm-5.3: 3,393,608,975 41 more models: 1,241,211,970 coding-glm-4.7: 858,361,495 coding-glm-5.3-free: 845,181,570 coding-glm-5: 555,869,820 glm-5.2: 101,757,2402026-09-03 — 51,641,943,395 tokens glm-5.3-flash: 25,230,128,915 coding-glm-5.3-flash: 12,343,929,790 coding-glm-5.3: 6,869,311,775 glm-5.3: 3,979,112,850 41 more models: 1,125,383,820 coding-glm-5.3-free: 747,105,115 coding-glm-4.7: 649,069,910 coding-glm-5: 479,669,045 glm-5.2: 218,232,1752026-09-04 — 46,371,361,975 tokens glm-5.3-flash: 19,662,008,285 coding-glm-5.3: 9,763,910,860 glm-5.3: 9,293,907,790 coding-glm-5.3-flash: 4,901,606,785 41 more models: 933,977,925 coding-glm-5.3-free: 681,732,660 coding-glm-4.7: 602,820,105 coding-glm-5: 414,301,745 glm-5.2: 117,095,8202026-09-05 — 29,861,185,965 tokens glm-5.3-flash: 21,927,219,175 glm-5.3: 3,128,027,890 coding-glm-5.3-flash: 1,852,618,190 41 more models: 1,373,718,745 coding-glm-5.3: 685,183,950 coding-glm-4.7: 269,966,815 coding-glm-5.3-free: 238,700,495 coding-glm-5: 234,625,675 glm-5.2: 151,125,0302026-09-06 — 23,964,835,395 tokens glm-5.3-flash: 15,843,079,290 coding-glm-5.3-flash: 2,664,937,935 glm-5.3: 2,492,985,755 41 more models: 1,141,134,215 coding-glm-5.3: 1,056,058,050 coding-glm-5.3-free: 296,578,685 glm-5.2: 253,601,235 coding-glm-5: 216,045,370 coding-glm-4.7: 414,8602026-09-07 — 34,215,316,590 tokens glm-5.3-flash: 19,059,816,875 glm-5.3: 8,506,221,890 coding-glm-5.3-flash: 3,673,599,520 coding-glm-5.3: 1,706,993,930 41 more models: 727,582,595 coding-glm-5.3-free: 203,013,875 glm-5.2: 170,388,190 coding-glm-5: 167,196,945 coding-glm-4.7: 502,7702026-09-08 — 40,514,142,995 tokens glm-5.3-flash: 31,111,412,135 glm-5.3: 4,765,267,430 coding-glm-5.3-flash: 1,836,691,175 coding-glm-5.3: 1,186,262,485 41 more models: 756,530,630 coding-glm-5.3-free: 489,010,705 coding-glm-5: 287,559,585 glm-5.2: 81,151,885 coding-glm-4.7: 256,9652026-09-09 — 37,307,876,475 tokens glm-5.3-flash: 28,819,987,670 glm-5.3: 2,668,476,660 coding-glm-5.3-flash: 2,630,894,580 coding-glm-5.3: 1,787,867,080 coding-glm-5.3-free: 665,144,695 41 more models: 652,060,460 coding-glm-5: 49,938,345 glm-5.2: 33,290,630 coding-glm-4.7: 216,3552026-09-10 — 36,765,006,860 tokens glm-5.3-flash: 20,838,310,355 coding-glm-5.3: 6,256,329,915 coding-glm-5.3-flash: 4,554,588,335 glm-5.3: 3,918,748,480 41 more models: 678,068,320 coding-glm-5.3-free: 337,835,180 glm-5.2: 179,967,880 coding-glm-5: 1,102,275 coding-glm-4.7: 56,1202026-09-11 — 27,466,347,680 tokens glm-5.3-flash: 13,499,143,900 coding-glm-5.3: 6,302,106,195 coding-glm-5.3-flash: 4,176,616,120 glm-5.3: 2,423,067,985 41 more models: 482,657,985 coding-glm-5.3-free: 387,048,340 glm-5.2: 195,702,255 coding-glm-5: 4,9002026-09-12 — 28,040,223,830 tokens glm-5.3-flash: 12,697,067,645 coding-glm-5.3: 5,913,778,670 coding-glm-5.3-flash: 5,901,329,840 glm-5.3: 2,562,207,840 41 more models: 561,088,710 coding-glm-5.3-free: 349,691,025 glm-5.2: 55,059,175 coding-glm-5: 465 coding-glm-4.7: 4602026-09-13 — 28,974,673,665 tokens glm-5.3-flash: 15,594,889,085 coding-glm-5.3-flash: 7,183,268,420 coding-glm-5.3: 3,550,681,185 glm-5.3: 1,597,157,395 41 more models: 685,189,260 coding-glm-5.3-free: 270,211,240 glm-5.2: 93,276,330 coding-glm-5: 605 coding-glm-4.7: 1452026-09-14 — 29,856,072,615 tokens glm-5.3-flash: 13,233,719,365 coding-glm-5.3-flash: 7,729,760,530 coding-glm-5.3: 3,938,101,995 glm-5.3: 3,303,513,370 41 more models: 858,981,630 glm-5.2: 428,183,875 coding-glm-5.3-free: 362,332,145 coding-glm-5: 806,545 coding-glm-4.7: 673,160
  • glm-5.3-flash
  • coding-glm-5.3
  • glm-5.3
  • coding-glm-5.3-flash
  • glm-5.2
  • coding-glm-5.3-free
  • coding-glm-5
  • coding-glm-4.7
  • 41 more models

Which models that traffic went to

  1. GLM 5.3 Flash46.6%406B
  2. Coding GLM 5.316.3%142B
  3. GLM 5.314.5%127B
  4. Coding GLM 5.3 Flash10.4%91.2B
  5. GLM 5.22.4%20.9B
  6. Coding GLM 5.3 (free)1.7%14.4B
  7. Coding GLM 51.6%13.9B
  8. Coding GLM 4.71.4%12B
  9. 41 more models5.1%44.8B

Share of 872B tokens. 10 models with traffic report no token counts and cannot be ranked here, including coding-glm-5-turbo-free and glm-image — 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 71 model IDs listed on this page; traffic routed through upstream-specific IDs that are not in the public catalog is not included.

All 71 Z.AI Models

Open in model list
Z.AI 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
coding-glm-5.3-freeTakes text, returns text.1.05M131KFreeFree/M—33 tok/s6.44 s
ox-alphaTakes text, vision, video, returns text.1.05M131KFreeFree/M———
coding-glm-5.3Takes text, returns text.1.05M131K$0.06$0.22/M$0.015/M182 tok/s4.16 s
glm-5.3-flashTakes text, vision, video, returns text.1.05M131K$0.1127$0.3944/M$0.0282/M133 tok/s5.65 s
glm-5.3Takes text, returns text.1.05M131K$1.1268$3.9438/M$0.2817/M36 tok/s1.54 s
coding-glm-5.2-freeTakes text, returns text.1M131KFreeFree/M—39 tok/s8.84 s
coding-glm-5.3-flash-freeTakes text, vision, video. Output modality not published.1M131KFreeFree/M—82 tok/s5.60 s
coding-glm-5.3-flashTakes text, vision, video. Output modality not published.1M131K$0.0282$0.0986/M$0.007/M223 tok/s6.00 s
coding-glm-5.2Takes text, returns text.1M131K$0.06$0.22/M—29 tok/s7.80 s
glm-5.2Takes text, returns text.1M131K$1.1268$3.9438/M$0.2817/M36 tok/s1.04 s
cloudflare-glm-5.2Takes text, returns text.1M131K$1.4$4.4002/M$0.2604/M——
glm-5.2-fast-previewTakes text, returns text.1M131K$2.254$7.889/M$0.5635/M38 tok/s2.00 s
coding-glm-5-turbo-freeTakes text, returns text.205K131KFreeFree/M———
cc-glm-5-turboTakes text, returns text.205K131K$0.06$0.22/M—26 tok/s2.23 s
coding-glm-5-turboTakes text, returns text.205K131K$0.06$0.22/M———
glm-5-turboTakes text, returns text.205K131K$1.2$3.9996/M$0.24/M10 tok/s4.66 s
zai-glm-5-turboTakes text, returns text.205K131K$1.2$3.9996/M$0.24/M10 tok/s4.66 s
coding-glm-4.6-freeTakes text, returns text.200K131KFreeFree/M—30 tok/s3.10 s
coding-glm-4.7-freeTakes text, returns text.200K131KFreeFree/M—37 tok/s2.39 s
coding-glm-5-freeTakes text, returns text.200K131KFreeFree/M—31 tok/s5.24 s
coding-glm-5.1-freeTakes text, returns text.200K131KFreeFree/M—31 tok/s5.74 s
glm-4.6Takes text, returns text.200K131KFreeFree/MFree/M20 tok/s4.65 s
glm-4.7-flash-freeTakes text, returns text.200K131KFreeFree/M—29 tok/s14.20 s
glm-5Takes text, returns text.200K131KFreeFree/MFree/M67 tok/s0.88 s
cc-glm-5Takes text, returns text.200K131K$0.06$0.22/M—28 tok/s4.05 s
cc-glm-5.1Takes text, returns text.200K131K$0.06$0.22/M———
coding-glm-4.6Takes text, returns text.200K131K$0.06$0.22/M$0.011/M32 tok/s3.69 s
coding-glm-4.7Takes text, returns text.200K131K$0.06$0.22/M$0.011/M27 tok/s1.39 s
coding-glm-5Takes text, returns text.200K131K$0.06$0.22/M—50 tok/s1.66 s
coding-glm-5.1Takes text, returns text.200K131K$0.06$0.22/M—37 tok/s9.60 s
glm-4.7Takes text, returns text.200K131K$0.274$1.0959/M$0.0548/M33 tok/s19.14 s
glm-5v-turboTakes text, vision, video, returns text.200K131K$0.7042$3.0985/M$0.169/M28 tok/s5.32 s
glm-5.1Takes text, returns text.200K131K$0.845$3.38/M$0.1831/M22 tok/s1.14 s
coding-glm-4.5-airTakes text. Output modality not published.131K—$0.014$0.084/M—26 tok/s4.22 s
glm-4.6vTakes text, vision, video, returns text.131K33K$0.137$0.411/M$0.0274/M——
glm-4.5-airTakes text. Output modality not published.131K98K$0.14$0.84/M—91 tok/s1.42 s
glm-4.5Takes text. Output modality not published.131K98K$0.4$1.6/M—107 tok/s0.46 s
glm-4.5vTakes text, vision, video, returns text.66K16K$0.274$0.822/M—81 tok/s8.45 s
glm-ocrTakes vision, returns text.32K—$0.0282$0.0282/M———
embedding-2Takes text. Output modality not published.8K—$0.0686$0.0686/M———
embedding-3Takes text. Output modality not published.8K—$0.0686$0.0686/M———
glm-imageTakes text, returns vision.——FreeFree/M———
Pro/THUDM/GLM-4.1V-9B-Thinking——$0.04$0.16/M———
THUDM/GLM-4-9B-0414——$0.05$0.05/M———
THUDM/GLM-Z1-9B-0414——$0.05$0.05/M———
cc-glm-4.6——$0.06$0.22/M———
cc-glm-4.7——$0.06$0.22/M———
THUDM/GLM-4-32B-0414——$0.08$0.08/M———
THUDM/GLM-Z1-32B-0414——$0.08$0.08/M———
glm-4-flash——$0.1$0.1/M———
THUDM/GLM-4.1V-9B-Thinking——$0.1$0.1/M———
doubao-1-5-pro-32k-250115——$0.108$0.27/M———
chatglm_lite——$0.2858$0.2858/M———
alicloud-glm-4.7——$0.411$1.9178/M$0.411/M44 tok/s1.01 s
alicloud-glm-5——$0.5634$2.5353/M$0.1127/M67 tok/s0.88 s
doubao-1-5-pro-256k-250115——$0.684$1.2312/M———
glm-3-turbo——$0.71$0.71/M———
chatglm_std——$0.7144$0.7144/M———
chatglm_turbo——$0.7144$0.7144/M———
glm-4.5-airxTakes text. Output modality not published.——$1.1$4.51/M$0.22/M——
chatglm_pro——$1.4286$1.4286/M———
glm-4v-plus——$2$2/M———
glm-zero-preview——$2$2/M———
glm-4.5-xTakes text. Output modality not published.——$2.2$8.91/M$0.44/M1 tok/s0.59 s
cbs-glm-4.7——$2.25$2.75/M———
glm-4-plus——$8$8/M———
cogview-3-plus——$10$10/M———
glm-4——$14.2$14.2/M———
glm-4v——$14.2$14.2/M———
code-davinci-edit-001——$20$20/M———
cogview-3——$35.5$35.5/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

cc-glm-5 vs cc-glm-5-turbocc-glm-5 vs cc-glm-5.1cc-glm-5 vs cloudflare-glm-5.2cc-glm-5 vs qwen3.8-max-previewcc-glm-5 vs kimi-k3

Z.AI on AIHubMix

Which Z.AI model should I start with?

coding-glm-4.6-free is free on input — the cheapest entry here that declares tool calling, and it carries a 200K context. Move up to cogview-3 when answer quality matters more than cost, or to coding-glm-5.3-free for long-form reasoning.

Which of these models reason before answering?

30 of the 71 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 (THUDM/…), 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 coding-glm-4.6 bills cache hits at 18.33% of the input rate and coding-glm-4.7 bills cache hits at 18.33% 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 Z.AI 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 →
Qwen
145 modelsOpenAI
137 modelsGoogle
85 modelsLlama
53 modelsByteDance
46 models

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

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