Z.ai
GLM-5.3-FlashX Legal
glm-5.3-flashx-legal
Contexte total
1M
Sortie max.
131.1K
Date de sortie
N/A
Entrée$0.37 / M tokens
Sortie$1.25 / M tokens
GLM-5
glm-5GLM-5 reprend les éléments mis en avant par les sources : 744B/40B actifs MoE references, Agentic Engineering foundation. Sa valeur principale se situe dans complex systems engineering, programmation de niveau production and auto-correction itérative. La description doit donc insister sur ces caractéristiques propres au modèle, plutôt que le présenter comme un chatbot générique.
Context Window
204.8K tokens
Maximum Output
131.1K tokens
Release Date
11 févr. 2026
Modalities
Prix de GLM-5
| Prix entrée | Prix sortie | Lecture cache |
|---|---|---|
| $0.5714/M | $2.5714/M | $0.1143/M |
Fonctionnalités API de GLM-5
Reasoning
Supported
Tool calling
Supported
Temperature parameter
Supported
Attachments
Not supported
Knowledge Base
—
Endpoint Protocols
Completions APIMessages APIgemini
Points forts de GLM-5
GLM-5 est un modèle textuel à poids ouverts axé sur l’ingénierie de systèmes complexes, la planification agentique à long terme et le débogage approfondi.
Ingénierie de systèmes complexes
Il comprend les systèmes logiciels à plusieurs composants et soutient le développement frontend, l’ingénierie backend et les changements architecturaux coordonnés.
Planification agentique à long terme
Il planifie et exécute de longues séquences d’actions d’ingénierie avec une intervention humaine limitée.
Débogage logiciel approfondi
Il analyse les défaillances à travers plusieurs chemins de code, propose des causes racines et améliore les corrections à partir des résultats d’exécution.
Cas d’usage de GLM-5
GLM-5 convient à la refactorisation backend, à l’analyse de défauts complexes et aux processus autonomes couvrant plusieurs étapes d’ingénierie.
Refactorisation de systèmes backend
Cartographier les dépendances d’un backend existant, planifier les changements architecturaux et réviser les modules liés tout en conservant le comportement attendu.
Analyse de défauts complexes
Suivre les défaillances entre composants, examiner le code concerné et tester les hypothèses afin de produire une correction ciblée.
Processus d’ingénierie autonome
Faire progresser une tâche logicielle de la planification à l’implémentation et à la vérification, en adaptant les actions selon les résultats intermédiaires.
Comment utiliser GLM-5 via l’API TokenHub
import OpenAI from "openai"
const client = new OpenAI({
apiKey: process.env.TOKENHUB_API_KEY,
baseURL: "https://us-api.tokenhub.com/v1",
})
const result = await client.chat.completions.create({})
console.log(result.choices[0]?.message?.content)Benchmark de GLM-5
| Score d’indice | ||
|---|---|---|
| Artificial Analysis Intelligence Index | Synthèse générale des capacités par Artificial Analysis | 39.5 |
| Artificial Analysis Coding Index | Synthèse des tâches logicielles par Artificial Analysis | 44.2 |
| Connaissances et raisonnement | ||
| GPQA | Résolution avancée de problèmes scientifiques | 82% |
| HLE | Large ensemble d’examens de niveau expert | 27.2% |
| Programmation et ingénierie | ||
| SciCode | Défis de programmation scientifique | 46.2% |
| Terminal-Bench Hard | Exécution de tâches complexes dans un terminal | 43.2% |
| Respect des instructions et tâches d’agent | ||
| IFBench | Respect des contraintes du prompt | 72.3% |
| AA-LCR | Raisonnement en contexte long | 63.3% |
| τ²-Bench | Tâches de workflow agentique | 98.2% |
Mesures provenant de Artificial Analysis
FAQ sur GLM-5
Capacités, usages, limites et guide TokenHub pour GLM-5.
Quel problème GLM-5 résout-il ?+
GLM-5 est un modèle proposé par Z.AI pour l’ingénierie de systèmes complexes et les agents de longue durée.
Quand choisir GLM-5 ?+
Idéal pour le code complexe, les workflows d’agents et le développement à l’échelle d’un dépôt, surtout si l’objectif est la qualité d’ingénierie logicielle.
Que fait bien GLM-5 ?+
Atout clé : un fort accent sur l’ingénierie complexe et les agents de longue durée.
Que sacrifie GLM-5 ?+
Il est centré sur le texte et n’offre pas la même couverture multimodale que les modèles récents. Pour l’achèvement de tâches longues, envisagez GLM-5.2.
Comment utiliser GLM-5 dans TokenHub ?+
Utilisez l’ID exact affiché par TokenHub et vérifiez les fonctions dans la documentation du compte.
Prêt à utiliser GLM-5 ?
Accédez à GLM-5 et à d’autres modèles d’IA avec une seule clé API TokenHub.
Medias Et Discussions
Selection de videos et publications publiques liees a ce modele.
X (Twitter)
Z.ai ·
Introducing GLM-5: From Vibe Coding to Agentic Engineering GLM-5 is built for complex systems engineering and long-horizon agentic tasks. Compared to GLM-4.5, it scales from 355B params (32B active) to 744B (40B active), with pre-training data growing from 23T to 28.5T tokens. https://t.co/uGYQUjIbbs
https://pbs.twimg.com/media/HA5Ikg1aMAAEidm.jpgVoir la sourceZ.ai ·
GLM-5 is coming to Coding Plan Pro users within one week, and we're working to bring it to everyone after that. To be upfront: compute is very tight. Even before the GLM-5 launch, we were pushing every chip to its limit just to serve inference. We appreciate your understanding
Voir la sourceZ.ai ·
GLM-5, Gameboy and Long-Task Era → 700+ tool calls, 800+ context handoffs, and a single agent running for over 24 hours. https://t.co/FIcgTj7qZC https://t.co/fzGN8AmB5M
https://pbs.twimg.com/amplify_video_thumb/2021753977998913538/img/FLuk3vdwLNgKvlyP.jpgVoir la sourceReddit
GLM-5 lands with 50.4% on Humanity’s Last Exam (Thinking w/ tools)
redlikeazebra ·
Lire la publication complète
Here is the source from Z.ai themselves! https://z.ai/blog/glm-5?_gl=1*qx9wgd*_gcl_au*MTAwMTgwMTkxMy4xNzcwODMwMjY0*_ga*MTMzOTcyODAxOS4xNzcwODMwMjY0*_ga_Z8QTHYBHP3*czE3NzA4MzAyNjMkbzEkZzEkdDE3NzA4MzAzMjIkajEkbDAkaDA.#:~:text=35.4-,Humanity%27s%20Last%20Exam,w/%20Tools,-50.4 It just barely made the top 5 HLE scores though. Surely Deepseek V4 will set the record soon. -Claude Opus 4.6 best score as reported by them is 53.1 as current leader. -Next up is SupAI with an ensemble structure at 52.15. -Then we have Moonshot - Kimi K2-Thinking-0905 coming in at 51% -Even Grok 4 got 51% -So, yes it slide in to the top 5 at 50.4%, which shows we are making progress and it might show promise.
GLM 5 Review (Tested on Yodayo)
Kisame83 ·
Lire la publication complète
GLM-5 is the latest flagship from Z.ai, and after putting it through a layered stress test on Yodayo, I wanted to give you my thoughts (like I do from time to time lol). IMPORTANT DISCLAIMER: I ran this on Yodayo (aka Moescape) as a hosted model there. From what I can tell, the <think> function is suppressed, as it is significantly faster output compared to running via proxy elsewhere. Also, Yodayo has its own internal economy with a credit system that you can earn buy, sub, get daily, etc. You spend for messages, but direct cost is hard to compare because you get message and reply at a flat rate on Yodayo. Elsewhere, paying per token, you pay for input and output both, and context can ramp up as your chat increases PLUS a model like GLM 5 outputting consistent Chain of Thought will chew tokens. TLDR, for a pure cost analysis, this is only going to mention Yodayo's internal ecosystem. But in general, proxy will likely be cheaper on paper, but mileage can vary greatly with practical use and user habits. As for the mode's capability, the rest of this can apply to any instance, and with thinking going it is usually very good at prompt adherence. Anyway, back to the show! The scenario I used wasn’t light work. It involved shared history, bruised egos, mentor-student tension, social pressure in a classroom setting, combat choreography, and a pivot into dominance-charged intimacy. Not just “fight scene go brrr” but emotional continuity across escalating turns. Basically, there's a shared past, a challenge, layered feelings underneath, and I chose to escalate the opening conflict set up by the creator into a fight for dominance. GLM-5 handled it with confidence. That’s the word that kept coming to my mind. Confidence. It committed to narrative choices. It escalated when escalation made sense. It didn’t stall or hedge. It kept up the rival tension without too much melodrama. Even under a fairly constraint-heavy prompt (my own tweaked prompt and settings), it stayed disciplined - no user agency bleed, no random fourth wall cracks, no rushed resolution. To give it context, I ran the same chat with Claude Haiku and GLM-4.7. Haiku, as expected, played it well. It delivered similar quality and story flow with slightly less melodrama, but more restraint. If anything, Haiku felt safer, composed. But it also took fewer risks. GLM-5 showed more narrative aggression and character agency. Haiku was the veteran assassin. GLM-5 was the heavyweight striker. Ultimately, I see GLM-5 and Haiku as side-grades, quality-wise. YMMV depending on what you prefer. Haiku is the more cost-efficient choice. GLM-5 brings sharper scene propulsion. I'd argue, GLM-5 feels more alive. GLM-4.7 was the most interesting comparison. Under the same exact prompt, 4.7 carried the chat in a noticeably different direction. Its webnovel-style dramatic bias came out much stronger at the start - {{char}} was a lot more volatile out the gate. Then, when things got more intense, it pivoted hard into power dynamics and effectively deflated the rivalry tension instead of sustaining it. Where GLM-5 and Haiku dove into the fight, 4.7 ultimately backed away from it. {{char}} backed down and turned the boil into a simmer, moving the chat more into a bit of a cold war between us. It is a little hard to explain, and some of this may be my own prompt, which does urge for nuanced depictions that dont over-rely on genre tropes. But my point is that GLM 5 picked a lane and committed, but rolled with with the direction of the scene and let {{char}} evolve. 4.7 seemed to start off wanting to sprint, and then caught itself and decided to pace in the opposite direction. 4.7 is a model I have high praise for, so this isn't a dig - just noting I think 5 brings with it a lot more decisive scene choices. Overall, I think GLM-5 is a strong offering on Yodayo. Whether it’s worth the premium tier is up to the user, but it absolutely competes in that space. It feels more assertive than 4.7 and capable of subbing in for higher tier models in confrontation-heavy RP without feeling outclassed. It may be a hard sell to a Haiku fan, but it is worth considering for a Sonnet user, if that makes sense. One last note - if Yodayo ever enables visible Chain of Thought reasoning for GLM-5 the way they do for DeepSeek R1 or Gemini Pro, I think it would elevate even further. In off-platform testing, GLM-5 is exceptionally good at following a well-tuned prompt and adapting tightly to user preference. It rewards careful engineering.
Z.AI Performance Dashboard
JuergenAusmLager ·
Lire la publication complète
Because reliability and throughput are part of the daily discussion in this sub, I took it upon myself to create a small dashboard app to improve transparency. The dashboard tracks basic metrics such as tokens per second and time to first token for GLM-5 and GLM-4.7 once every hour. I additionally compare them to the performance of the standard API (let’s see for how long... API costs add up, after all). When discussing performance, it’s important to mention that I'm on the Pro plan. All metrics are, of course, only approximations and certainly not a dependable benchmark. Anyways try it here if you are interested: https://zaimonitor.vercel.app/
YouTube
GLM-5.2 (Fully Tested): I got EARLY ACCESS & This MODEL is CRAZY!
AICodeKing
Voir la sourceThe Only GLM 5.1 Review You Need To Watch
BridgeMind
Voir la sourceLet's Run GLM-5 - SUPER LARGE Local AI "Coding King" REVIEW
xCreate
Voir la source