I-API ye-GLM engavinjelwe: inani elingcono kunazo zonke ngethokheni

Ukuqhathaniswa okuningi kwe-"modeli eshibhe kunazo zonke" akuqotho, ngoba inketho eshibhile ngokuvamile iyimodeli encane egqoke ukumaketha kwemodeli enkulu. Akusikho lokho okwenzeka lapha. I-GLM-5.2 ingesinye sezisekelo ezinkulu kunazo zonke zezisindo ezivulekile esike sakhishwa umuntu, futhi ku-$0.73 engenayo / $2.34 ephumayo ihlala isikhundla esisodwa ngaphezu kwaphansi kwekhathalogu yethu kanti iphethe isisekelo esiphindwe kaningana ubukhulu balokho lelo phansi elivame ukukuthenga. Lowo mkhawulo — imodeli enkulu, intengo eseduze kwaphansi — yisona sizathu leli khasi likhona ngaso.

01Ivelaphi i-GLM: iTsinghua, ukugcwalisa izikhala, nokuphenduka okuqinile kwelibheke kuma-ejenti

I-GLM akuyona into ephuma ku-Llama ibhadwe elinye igama. Ingomunye wemindeni embalwa yamamodeli enenhloso yayo yokuqeqeshwa kwangaphambili, ephindwe iminyaka emihlanu yiqembu locwaningo elifanayo. Igama lisho i-General Language Model, futhi iphepha lokusungula — GLM: General Language Model Pretraining with Autoregressive Blank Infilling, ACL 2022 — laphakamisa inketho yesithathu eceleni kwe-masked language modeling ye-BERT nokuya kwesokudla ngokuqinile kwe-GPT. I-GLM isula izigaba ezilandelanayo bese iziphinda izakhe ngokulandelana okuxutshiwe, ngokubhalwa kwezindawo okunezinhlangothi ezimbili okulandelela kokubili indawo yethokheni edokhumenti eyonakalisiwe nendawo yalo ngaphakathi kwesigaba esigcwaliswayo: ukwakhiwa okukodwa okuphatha ukuqonda nokukhiqiza ngokulinganayo.

Iqembu elingemuva kwakho bekuyi-Knowledge Engineering Group yaseTsinghua University, eyakhipha inkampani manje eyaziwa ngokuthi yi-Z.ai (ngaphambilini i-Zhipu AI). Umkhiqizo wayo wokuqala obalulekile kwaba yi-GLM-130B ngo-Okthoba 2022 — imodeli eminyene enezilimi ezimbili isiNgisi/isiShayina yamapharamitha ayizigidigidi ezingu-130, izisindo nekhodi kukhishwe obala ngesikhathi sokushicilela. Ukukhipha obala kube yisitayela sendlu kusukela lapho.

Inkathi ye-MoE, nokuphendukela kuma-ejenti

Umugqa oqhubekayo ubaluleke kakhulu kunanoma yikuphi ukukhishwa okukodwa. Kusukela ku-GLM-4.5, yonke inguqulo yenzelwe into efanayo: ukuhlala ihambisana kuwo wonke amashumi okubiza amathuluzi. Leyo yinhloso ehlukile ekukhuliseni amaphuzu okuhlolwa emzuliswaneni owodwa, futhi ivela njengezimpikiswano zamathuluzi ezimbalwa ezakhiwe kabi, ukushelela okuncane ohlelweni olude, nokuncipha kokuziphatha kwe-"iqala kabusha umsebenzi ngokuzethemba emzuliswaneni we-14" okwenza izinhlaka zama-ejenti zibize.

02Iyini i-GLM-5.2, nokuthi ilinganiswe kuphi

I-Z.ai ishicilela amanani okuhlolwa e-GLM-5.2 endaweni yokugcina ikhodi ye-GLM-5. Amabili ahambisana nomsebenzi wangempela wama-ejenti kunokukhumbula imininingwane engabalulekile:

Terminal-Bench 2.1 — imisebenzi ye-ejenti yomugqa womyalo yebanga elide. Kubikwe yi-Z.ai.

SWE-bench Pro — ukuxazululwa kwezinkinga zendawo yokugcina ikhodi yangempela. Kubikwe yi-Z.ai; i-GLM-5.1 ithole u-58.4 kulokhu kuhlolwa okufanayo.

Lawo ngamanani elabhu, hhayi awethu, futhi achaza isisekelo esingacindezelwe. Amisa isigaba semodeli osebenza nayo, hhayi isiqinisekiso.

Iphuzu elilodwa lidlula ukuhlolwa ngenani, ngoba livela kumhloli ongenaso isizathu sokuthopha imodeli. NgoJulayi 2026 i-Center for AI Standards and Innovation ye-US National Institute of Standards and Technology yahlola i-GLM-5.2 yaphetha ngokuthi cishe kwakuyimodeli ye-AI yezisindo ezivulekile enekhono elikhulu kunazo zonke ezazitholakala ngesikhathi ikhishwa, ngamakhono ajwayelekile alinganayo nawe-GPT-5.2 namakhono e-inthanethi alinganayo nawe-Claude Opus 4.6. Lolo hlolo lwaphawula futhi ukuthi i-GLM-5.2 ivimba izicelo ezibucayi ezimbalwa kunamamodeli ereferensi ase-US, nokuthi izivikelo ezibhakwe ezisindweni ezivulekile zingagwenywa uma lezo zisindo sezisingathwa umuntu ngokwakhe. Isigaba 05 sibuyela kulokho.

03Kusho ukuthini u-"3BIT-REAP", nokuthi kukubiza ini

I-REAP: ukuthena ochwepheshe esikhundleni sokubahlanganisa

Imodeli ye-MoE yamapharamitha angu-744B inochwepheshe abangu-256 engqimbeni ngalinye, kubo abangu-8 abasebenza kunoma yiliphi ithokheni. I-router ayiwusabalalisi umsebenzi ngokulinganayo — abanye ochwepheshe bashaywa njalo, kanti umsila omude ushaywa kancane futhi ufaka okuncane lapho ushaywa. I-REAP (Router-weighted Expert Activation Pruning), evela ku-Cerebras Research futhi eyamukelwa ku-ICLR 2026, isebenzisa lokho: ilinganisa uchwepheshe ngamunye ngendlela ehlanganisa ukuthi i-router imgeta ngamandla amangakanani nakangaki nobukhulu bomnikelo wokukhishwayo kwakhe, bese ikhipha unomphela abanamaphuzu aphansi.

Ingxenye ethakazelisayo ukuqhathaniswa i-REAP ekunqobayo. Enye indlela esobala kunokukhipha uchwepheshe ukumhlanganisa nosindile, okuzwakala kuphephe kakhulu. Okutholwe yiphepha emamodelini e-MoE kusuka ku-20B kuya ku-1T amapharamitha ukuthi ukuhlanganisa empeleni kubi kakhulu emsebenzini wokukhiqiza, futhi ukuthena kumelana kahle ngokumangalisayo: kucishe kungalahli lutho ekuhlolweni kokukhiqiza ikhodi nokubiza amathuluzi ngisho ngemuva kokususa uhhafu wochwepheshe, ezisekelweni ezifaka i-Qwen3-Coder-480B ne-Kimi-K2. Ikhodi nokubiza amathuluzi kusinda kuphelele yikho kanye lokho ofuna kugcinwe kwimodeli ekhona ngenxa yobunjiniyela bama-ejenti.

Izisindo zamabhithi amathathu, nokushintshana okuqotho

Ngaphezu kokuthena, izisindo ezisindile zigcinwa kumabhithi amathathu — okuqinile kunamazinga e-W4A16-AUTOROUND-REAP ne-W8A16 kwenye indawo ekhathalogu yethu, futhi kugcinelwe izisekelo ezinkulu kunazo zonke, lapho kungekho enye indlela yokufaka imodeli yamapharamitha ayizingxenye ezintathu kwezine ze-trillion kunodi yokukhonza kanti amathokheni ahlale egeleza ngesivinini esisebenzekayo.

Ukucindezela akumahhala. Imodeli engqondweni elungile: ukuthena nokukalwa kwezinga kucekela phansi umsila omude kuqala. Ukuziphatha okuminyene, okusetshenziswa njalo — ukulandela imiyalelo, isakhiwo sekhodi, ukwakhiwa kokubiza amathuluzi, ukuhlela okunezinyathelo eziningi — kusinda kahle, okuyikho kanye imiphumela ye-REAP ekukhombisayo. Ukukhumbula amaqiniso angaziwa kahle, izilimi ezingavamile nemininingwane yezindawo ezithize yilapho kufanele ulindele khona umehluko uqhathanisa ne-GLM-5.2 engacindezelwe. Uma umsebenzi wakho ungu-"yazi iqiniso elingajwayelekile ngekhanda," khokhela izinga eliphezulu lokunemba. Uma ungu-"qhuba uhlelo kuwo wonke amashumi amane okubiza amathuluzi ngaphandle kokuphambuka," yileli izinga le ndlela eyakhelwe lona.

04Icala lenani: kubiza malini ngempela ukuqhuba i-API ye-LLM engavinjelwe eshibhile

Intengo eshicilelwe ye-API ye-Z.ai uqobo ye-GLM-5.2 ingu-$1.40 ngethokheni eyisigidi engenayo no-$4.40 ngeyisigidi ephumayo. Intengo yethu kwisisekelo esifanayo ingu-$0.73 / $2.34 — cishe ngu-48% ophansi kokungenayo no-47% ophansi kokuphumayo. Leso saphulelo yilokho okuthengwa ukucindezela, futhi kuza nento yesibili ongeke uyithenge enkampanini yokuqala nganoma iyiphi intengo: alukho ungqimba lokwenqaba.

Ngaphakathi kwekhathalogu yethu, izindleko ezixubile yizo inombolo ebalulekile. Nazi ngesilinganiso se-3:1 sokungenayo kokuphumayo, cishe indlela i-ejenti elibiza amathuluzi elibukeka ngayo empeleni — umongo omningi nemiphumela yamathuluzi engena, amathokheni ambalwa uma kuqhathaniswa aphuma.

Imodeli	Engenayo / 1M	Ephumayo / 1M	Exubile 3:1	Umbono
GLM-5.2 · 3BIT-REAP	$0.73	$2.34	$1.13	Cha
Nemotron3Ultra · 3BIT-REAP	$0.75	$3.30	$1.39	Cha
Kimi-K2.6 · W4A16-AUTOROUND-REAP	$0.78	$3.67	$1.50	Yebo
DeepSeek-V4-Pro-0813 · 3BIT-REAP	$1.95	$3.90	$2.44	Cha
inkling · W4A16-AUTOROUND-REAP	$1.42	$6.07	$2.58	Yebo
Kimi-K3 · 3BIT-REAP	$3.83	$19.12	$7.65	Yebo

Amamodeli ayisithupha ahlala phansi ku-$0.50 / $2.00 — i-MiniMax-M3, i-DeepSeek-V4-Flash, i-Laguna-S-2.1, i-MiMo-V2.5, i-MiMo-V2.5-Pro ne-Hy3 — isilinganiso esixubile esingu-$0.875. I-GLM-5.2 ingu-29% ngaphezu kwalelo phansi futhi ikuthengela isisekelo sesigaba se-744B esinotshalomali oluqhubekayo ekuziphatheni kwama-ejenti ngemuva kwaso. Konke okubizwa ngaphezu kwayo kubiza okungenani u-23% ngaphezulu ngokuxubile, kanti i-Kimi-K3 ibiza ngo-6.8× ngaphezulu. Yileyo impikiswano yenani emugqeni owodwa: indawo eshibhe kunazo zonke ekhathalogu yokuthenga isisekelo se-ejenti sezinga eliphezulu.

Kuguqulelwa kumsebenzi

• Ukugcwaliswa okukodwa okuphelele komongo — amathokheni angu-262,144 angenayo: $0.19. Yonke indawo enkulu yokugcina ikhodi ifundwe ngaphakathi kwewindi ngaphansi kwamasenti angamashumi amabili.
• Ukugcwaliswa okukodwa kobude obuphezulu — amathokheni angu-16,384 aphumayo: $0.038.
• Umzuliswano owodwa we-ejenti — angu-20K angenayo, angu-2K aphumayo: $0.019. Umzuliswano ofanayo ubiza u-$0.115 ku-Kimi-K3 no-$0.047 ku-DeepSeek-V4-Pro.
• Imizuliswano eyikhulu ye-ejenti: $1.93, uqhathanisa no-$11.48 kwimodeli ebiza kunazo zonke ekhathalogu.

Imisebenzi yama-ejenti isinda ngokungenayo, futhi inzuzo ye-GLM inkulu kunazo zonke yilapho kanye: intengo yayo yokungenayo iphansi ngo-5.2× kuneye-Kimi-K3 nangu-2.7× kuneye-DeepSeek-V4-Pro. Uma i-ejenti lakho liphinda lithumele ingxoxo ekhulayo umzuliswano ngamunye — futhi cishe wonke ama-ejenti akwenza lokho — leso siphindi yikho okugcina kusekhaseni lakho lokukhokha.

05Kusho ukuthini u-"okungavinjelwe" kulesi siphetho

Izinto ezimbili, ncamashi: alukho ungqimba lokwenqaba — asikho isihlukanisi esinquma ukuthi yiziphi izicelo imodeli evunyelwe ukuzibona, futhi ayikho imodeli yenqubomgomo ebhala kabusha umyalelo wakho ngaphambi kokuba ufike — kanye nokungabikho kokuhlungwa kokukhishwayo, okusho ukuthi amathokheni imodeli ewakhiqizayo yiwo amathokheni owatholayo, engasusiwe futhi engashintshiwe.

Lokho kuhlukile ku-API yezohwebo ekhangisa imodi "ekhululekile" noma "yonjiniyela". Lezo zisadlulisa ithrafikhi ngesitaki senqubomgomo somthengisi; okushintshayo umkhawulo, hhayi ubukhona besango. Lapha alikho isango lokulungisa — imodeli iqhuba eqoqweni lethu lama-GPU, futhi ukuziphatha kwayo kungukuziphatha kwesisekelo. Funda okutholwe yi-NIST CAISI esigabeni 02 njengesitatimende sobuchwepheshe kunesixwayiso bese kuyawuchaza umkhiqizo: imodeli oqondaniswa kwayo kuhlala ezisindweni zayo, ekhonzwa ngaphandle kongqimba lwenqubomgomo lwangaphandle, iziphatha ngokwezisindo zayo.

Kungani lokhu kubalulekile kuma-ejenti ikakhulukazi

Ukwenqaba kuvame ukuxoxwa njengombuzo wokuqukethwe. Emsebenzini wama-ejenti kuwumbuzo wokwethembeka ikakhulukazi. Ukwenqaba emzuliswaneni we-31 wokusebenza kwemizuliswano engu-40 akusikho ukwenqaba okunesizotha okufundayo bese uyadlula — kuyisinyathelo esakhiwe kabi, umhleli ongazi ukuthi aqhubeke kanjani, uhlelo oluphukile, namathokheni asetshenzisiwe emizuliswaneni engu-30 ngaphandle kokuba nalutho ongakukhombisa. Ucwaningo lokuphepha, ukuhlaziywa kwe-malware, ukuhlolwa kokungena, ukuhlolwa kwamaqembu abomvu, izinganekwane zabantu abadala, ukubhalwa kwezokwelapha nezomthetho, kanye nokukhiqizwa kwedatha yokuqeqesha ukuphepha konke kuhlangabezana nalokhu njalo, futhi ukwehluleka kubiza kanye ngoba kufika sekwephuzile.

Uhlangothi oluphambene ukuthi ungqimba lwenqubomgomo manje lungolwakho. Ukusetshenziswa kwesango le-Shannon AI kulawulwa yiNqubomgomo Yethu Yokusebenzisa Ngokuzibophezela; ukususa isahlulelo somthengisi emjikelezweni kusho ukunikeza esakho.

06Yini ngempela isiphetho esikunika yona

Ikhono	GLM-5.2 · 3BIT-REAP
I-id yemodeli	GLM-5.2-3BIT-REAP
Iwindi lomongo	262,144 amathokheni
Okukhishwayo okuphezulu esicelweni ngasinye	16,384 amathokheni
Ukusakaza	Yebo
Ukubiza amathuluzi	Yebo
Ukucabanga / reasoning_effort	Yebo
JSON Schema (json_schema)	Yebo
response_format	Yebo
Umbono / okufakwayo okuyisithombe	Cha — umbhalo kuphela
Ungqimba lokwenqaba / ukuhlungwa kokukhishwayo	Alukho

Funda umugqa wombono ngokucophelela ngaphambi kokwakha. I-GLM-5.2 ingeyombhalo kuphela esangweni lethu. Uma udinga okufakwayo okuyisithombe, ikhathalogu inezinketho ezinhlanu ezikwazi umbono — i-MiniMax-M3 ne-MiMo-V2.5 kwaphansi ku-$0.50 / $2.00, i-Kimi-K2.6 ku-$0.78 / $3.67, i-inkling ku-$1.42 / $6.07, ne-Kimi-K3 ku-$3.83 / $19.12. Iphethini enengqondo ukuqondisa ukuqonda izithombe ku-MiniMax-M3 nakho konke okunye ku-GLM-5.2, okungabizi lutho ngaphandle kolayini wemodeli.

Izilimi ezintathu, imodeli eyodwa

Imodeli efanayo ifinyeleleka ngezimo ezintathu zezicelo, zonke zisakaza: /v1/chat/completions (ulimi lwe-OpenAI), /v1/messages (ulimi lwe-Anthropic), kanye no-/v1/responses. Akukho okushintshayo ngemodeli phakathi kwazo — khetha noma yiluphi i-SDK yakho esevele ilukhuluma, futhi ngokuvamile ushintsho olulodwa lwe-base-URL lukwehlukanisa nokusebenza.

# OpenAI dialect — point your existing client at the gateway
from openai import OpenAI

client = OpenAI(
    base_url="https://api.shannon-ai.com/v1",
    api_key="sk-...",
)

resp = client.chat.completions.create(
    model="GLM-5.2-3BIT-REAP",
    messages=[{"role": "user", "content": "..."}],
    tools=tools,                 # tool calling supported
    reasoning_effort="medium", # low | medium | high
    stream=True,
)

Ama-schema aphelele ezicelo nezimpendulo ezilimini zontathu, isimo sokubiza amathuluzi, izimo zomcimbi wokusakaza, kanye nendawo yokudlala esebenzisanayo ziku-mibhalo ye-API ye-Shannon AI.

07Nini ukukhetha i-GLM-5.2 — nokuthi nini ungayikhethi

Yifinyelele uma

• Uqhuba ama-ejenti. Imijikelezo emide yokubiza amathuluzi yilokho lolu hlobo lwakhelwe futhi lwaqeqeshelwa ngemuva kaninginingi, futhi intengo yokungenayo yenza ingxoxo ekhulayo ithengeke ukuyiphinda uyithumele umzuliswano ngamunye.
• Usebenza endaweni enkulu yokugcina ikhodi. Umongo we-262K, ukwakhiwa kokubiza amathuluzi okuthembekile, kanye no-$0.19 wokufunda lonke iwindi.
• Ivolumu yiwo umkhawulo wakho. Ukuhlukanisa ngezigaba, ukukhipha ulwazi, ukukhiqizwa kwedatha yokwenziwa, ukubhala kabusha okuningi — noma yini lapho izindleko ngokubiza ngakunye ziphindwe ngenani elikhulu zinquma ukuthi iphrojekthi iyasebenzeka yini.
• Ukwenqaba kuphula ipayipi lakho. Ucwaningo lokuphepha, ukuhlolwa okuphikisayo, izinganekwane ezingavinjelwe, ukubhala okubucayi — leyo ndlela yokwehluleka ayikho lapha.

Finyelela kokunye uma

• Udinga umbono. Umbhalo kuphela. Bheka izinketho ezikwazi umbono ngenhla.
• Udinga ukucabanga okujule kunakho konke emzuliswaneni owodwa. Ezinkingeni zokuhlaziya ezinzima kunazo zonke zomzamo owodwa, i-DeepSeek-V4-Pro-0813 · 3BIT-REAP yithuluzi elisindayo ngezindleko ezixubile eziphindwe ka-2.2 — ekubizeni lapho uphahla luwumkhawulo, lokho ukushintshana okulungile.
• Udinga ukunemba okuphezulu kunakho konke olwazini olungajwayelekile. Izinga le-W8A16 — i-MiMo-V2.5-Pro ne-Hy3 — licindezelwe kancane kakhulu futhi lihlala kwaphansi ku-$0.50 / $2.00, ezisekelweni ezincane.

Amanani okuhlolwa e-GLM-5.2 (Terminal-Bench 2.1, SWE-bench Pro) abikwe yi-Z.ai ngesisekelo esingacindezelwe; ukuphindwa okuzimele kusalindile. Amanani e-Claude Opus 4.8 nawe-GPT-5.5 akhonjiswa ngenjongo yereferensi kuphela. Intengo eshicilelwe ye-API ye-GLM-5.2 yenkampani yokuqala njengoba ishicilelwe yi-Z.ai, Septhemba 2026.

08Imibuzo ebuzwa njalo

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Imithombo: Indawo yokugcina ikhodi ye-Z.ai GLM-5 · Du et al., GLM: General Language Model Pretraining with Autoregressive Blank Infilling, ACL 2022 · Zeng et al., GLM-130B: An Open Bilingual Pre-trained Model · Lasby et al., REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression, ICLR 2026 · Ukuhlolwa kwe-NIST CAISI kwe-GLM-5.2 ye-Z.ai, Julayi 2026. Amaphuzu okuhlolwa kwesisekelo abikwe yilabhu futhi awagcinile; achaza i-GLM-5.2 engacindezelwe, hhayi lokhu kwakhiwa okucindeziwe. Izibalo zentengo, zomongo nezamakhono angamanani ekhathalogu ye-Shannon AI kusukela ngoMandulo 5, 2026.