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GLM 5

Masukan:$0.672/M
Keluaran:$2.688/M
<think>**Considering user intent** I see that the user shared a paragraph about "GLM-5", Z.ai’s open-source foundation model. Since there’s no clear question, it seems like they might want me to improve, summarize, or maybe even rewrite it. I should ask them for clarification. I could propose options like summarizing, editing, or creating a tagline. Although the CLI guidelines focus on code, I can still assist with textual editing. I’ll ask, “How can I help?” to guide the conversation forward.**Exploring content options** I’m considering various options to effectively present the information. I could tighten the copy, rewrite it for an executive summary, or create landing page elements like hero text and bullet points. Benchmark comparisons against closed-source models, like GPT-4.1, can help here too. It seems I should offer concise variants, like a one-liner, an elevator pitch, and a more detailed paragraph, while also keeping disclaimers in mind. I'll propose these and see if the user wants more alternatives.**Drafting content structure** I’m thinking about including a GitHub README intro snippet. I want to keep it simple with minimal formatting, just a few lines and some bullet points. I'll also craft a concise rewrite while suggesting a structure for the product page. This could include a hero section, subhead, calls-to-action, value propositions, and more. For the snippet, I'll say something like: "GLM-5 is Z.ai's open-source foundation model designed for complex systems and agent workflows." It sounds cleaner and more to the point!**Crafting product variants** I’m thinking of proposing more variations like a tagline and bullet points, highlighting features such as full-system build and deploy, agentic planning, long-horizon reasoning, and production-grade performance. I should be cautious with claims about latency if it's not mentioned. It might be safer to stick to "production-grade performance" instead. I'm considering adding a call to action, like "Docs | GitHub," and I'll ask the user what they want to do. For a rewrite, I’ll offer a brief product-line hero: “GLM-5 is Z.ai’s open-source foundation model for complex systems and agent workflows.” It sounds appealing!**Polished Variants** I’m considering several polished variants for the user’s content. I need to ensure it’s concise and tailored to the audience, whether they’re execs or developers. I'll suggest a headline like "Build Entire Systems, Not Just Code” and include bullet points for features like agentic planning and iterative self-correction. I’ll provide three versions: a one-sentence summary, a brief two- to three-sentence statement, and a more expanded paragraph. I’ll also ask how they’d like to proceed: tighten, create a headline, or analyze claims. I'll craft the response, keeping formatting minimal and ensuring everything is ASCII compliant. Let's see which option they prefer!</think> Want this tightened or repurposed? Here are a few polished variants—pick a direction and I’ll tune tone and audience. - Concise: GLM-5 is Z.ai’s open-source foundation model for complex systems design and long-horizon agent workflows, pairing advanced planning, deep backend reasoning, and iterative self-correction to move beyond code generation into full-system construction and autonomous execution—at production-grade performance that rivals leading closed-source models. - Developer-forward: GLM-5 is an open-source model built for expert developers who need to ship large, multi-service systems. It plans, reasons across backends, and self-corrects over long horizons, so it doesn’t just write code—it assembles working systems and runs them autonomously, with production-grade reliability and throughput. - Landing-page style: - Headline: Build Entire Systems, Not Just Code - Subhead: GLM-5 is Z.ai’s open-source model engineered for complex systems design and long-horizon agent workflows. - Value props: agentic planning; deep backend reasoning; iterative self-correction; production-grade performance; autonomous execution from spec to system. If you share target audience (research, enterprise, OSS devs), voice (technical vs marketing), and any benchmark links to back the “rivals closed-source” claim, I can finalize copy for a README, landing page hero, or press release.
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Penggunaan komersial
Playground
Gambaran Keseluruhan
Ciri-ciri
Harga
API

Spesifikasi teknikal GLM-5

PerkaraGLM-5 (dilaporkan)
Keluarga modelGLM (Z.ai / Zhipu AI) — generasi perdana
Seni binaMixture-of-Experts (MoE) + perhatian jarang (pengoptimuman DeepSeek/DSA).
Jumlah parameter≈744–745B (kumpulan MoE).
Parameter aktif/dirutekan (setiap token)~40–44B aktif (bergantung pada perutean/pakar).
Token pra-latihan~28.5T token (dilaporkan).
Tetingkap konteks (input)Sehingga 200,000 token (mod konteks panjang).
Token output maksimum128,000 token (penjanaan maksimum per panggilan yang dilaporkan).
Modaliti inputTeks sahaja (utama); direkayasa untuk teks kaya → keluaran (penjanaan doc/xlsx melalui alat).

Apakah GLM-5

GLM-5 ialah model asas generasi seterusnya daripada Zhipu AI yang menskalakan siri GLM dengan reka bentuk perutean MoE dan pengoptimuman perhatian jarang untuk menyampaikan penaakulan konteks panjang dan alur kerja agenik (perancangan berbilang langkah, orkestrasi kod & sistem). Ia dengan jelas diposisikan sebagai pesaing open-weights untuk tugas agenik dan kejuruteraan, dengan kebolehcapaian perusahaan melalui API dan hos kendiri.

🚀 Ciri Utama GLM-5

1. Kecerdasan Agenik & Penaakulan

GLM-5 dioptimumkan untuk alur kerja di mana model menguraikan tugas panjang dan kompleks kepada langkah bertertib dengan halusinasi yang dikurangkan — satu penambahbaikan besar berbanding versi GLM terdahulu. Ia mendahului open weights model benchmarks tertentu dalam kebolehpercayaan pengetahuan dan produktiviti tugas.

2. Sokongan Konteks Panjang

Dengan tetingkap konteks 200K token, GLM-5 boleh mengekalkan perbualan yang sangat panjang, dokumen besar, dan rantaian penaakulan lanjutan tanpa kehilangan koherensi — keupayaan yang semakin kritikal untuk aplikasi profesional dunia sebenar.

3. Perhatian Jarang DeepSeek

Dengan mengintegrasikan mekanisme perhatian jarang, GLM-5 menskala jejak memori dengan cekap, membolehkan jujukan lebih panjang tanpa peningkatan kos secara linear.

4. Integrasi Alat & Format Keluaran

Sokongan asli untuk keluaran berstruktur dan integrasi alat luaran (JSON, panggilan API, penggunaan alat dinamik) menjadikan GLM-5 praktikal untuk aplikasi perusahaan seperti lembaran hampar, laporan, dan pembantu pengkodan automatik.

5. Kecekapan Kos

GLM-5 diposisikan sebagai berdaya saing kos berbanding setara proprietari, dengan harga input/output yang jauh lebih rendah daripada penawaran utama, menjadikannya menarik untuk penggunaan berskala besar.

Prestasi Penanda Aras GLM-5

Pelbagai penilaian bebas dan penanda aras industri awal menunjukkan GLM-5 berprestasi kukuh dalam kalangan model open-weight:

  • Ia mencapai kadar halusinasi serendah rekod pada Artificial Analysis Intelligence Index — ukuran kebolehpercayaan dan ketepatan — mengatasi model terdahulu dengan jurang yang besar.
  • Penanda aras berpusatkan agen menunjukkan peningkatan ketara dalam pelaksanaan tugas kompleks berbanding GLM-4.7 dan model terbuka lain.
  • Metrik kos-kepada-prestasi memposisikan GLM-5 dalam kuartil ke-4 untuk kelajuan tetapi peringkat teratas (terbaik) bagi kecerdasan dan harga dalam kalangan model open-weight.

Skor Kuantitatif (Contoh daripada platform pemeringkatan):

  • Indeks Kecerdasan: #1 dalam kalangan model open weights.
  • Kecekapan Harga: Penarafan tinggi untuk kos input/output yang rendah.

Cara mengakses dan menggunakan API GLM-5

Langkah 1: Daftar untuk Kunci API

Log masuk ke cometapi.com. Jika anda belum menjadi pengguna kami, sila daftar terlebih dahulu. Log masuk ke konsol CometAPI. Dapatkan kunci API kelayakan akses antara muka. Klik “Add Token” pada token API di pusat peribadi, dapatkan kunci token: sk-xxxxx dan hantar.

Langkah 2: Hantar Permintaan kepada glm-5 API

Pilih titik akhir “glm-5” untuk menghantar permintaan API dan tetapkan badan permintaan. Kaedah permintaan dan badan permintaan diperoleh daripada dokumen API laman web kami. Laman web kami juga menyediakan ujian Apifox untuk kemudahan anda. Gantikan <YOUR_API_KEY> dengan kunci CometAPI sebenar daripada akaun anda. Tempat untuk memanggilnya: format Chat.

Masukkan soalan atau permintaan anda ke dalam medan content—inilah yang akan dibalas oleh model. Proses respons API untuk mendapatkan jawapan yang dijana.

Langkah 3: Dapatkan dan Sahkan Keputusan

Proses respons API untuk mendapatkan jawapan yang dijana. Selepas pemprosesan, API membalas dengan status tugas dan data output.

Soalan Lazim

What distinguishes GLM-5’s architecture from earlier GLM models?

GLM-5 uses a Mixture of Experts (MoE) architecture with ~745B total parameters and 8 active experts per token (~44B active), enabling efficient large-scale reasoning and agentic workflows compared to previous GLM series.

How long of a context window does GLM-5 support via its API?

GLM-5 supports a 200K token context window with up to 128K output tokens, making it suitable for extended reasoning and document tasks.

Can GLM-5 handle complex agentic and engineering tasks?

Yes — GLM-5 is explicitly optimized for long-horizon agent tasks and complex systems engineering workflows, with deep reasoning and planning capabilities beyond standard chat models.

Does GLM-5 support tool calling and structured output?

Yes — GLM-5 supports function calling, structured JSON outputs, context caching, and real-time streaming to integrate with external tools and systems.

How does GLM-5 compare to proprietary models like GPT and Claude?

GLM-5 is competitive with top proprietary models in benchmarks, performing close to Claude Opus 4.5 and offering significantly lower per-token costs and open-weight availability, though closed-source models may still lead in some fine-grained benchmarks.

Is GLM-5 open source and what license does it use?

Yes — GLM-5 is released under a permissive MIT license, enabling open-weight access and community development.

What are typical use cases where GLM-5 excels?

GLM-5 is well suited for long-sequence reasoning, agentic automation, coding assistance, creative writing at scale, and backend system design tasks that demand coherent multi-step outputs.

What are known limitations of GLM-5?

While powerful, GLM-5 is primarily text-only (no native multimodal support) and may be slower or more resource-intensive than smaller models, especially for shorter tasks.

Ciri-ciri untuk GLM 5

Terokai ciri-ciri utama GLM 5, yang direka untuk meningkatkan prestasi dan kebolehgunaan. Temui bagaimana keupayaan ini boleh memberi manfaat kepada projek anda dan meningkatkan pengalaman pengguna.

Harga untuk GLM 5

Terokai harga yang kompetitif untuk GLM 5, direka bentuk untuk memenuhi pelbagai bajet dan keperluan penggunaan. Pelan fleksibel kami memastikan anda hanya membayar untuk apa yang anda gunakan, menjadikannya mudah untuk meningkatkan skala apabila keperluan anda berkembang. Temui bagaimana GLM 5 boleh meningkatkan projek anda sambil mengekalkan kos yang terurus.
Harga Comet (USD / M Tokens)Harga Rasmi (USD / M Tokens)ModelDetail.discount
Masukan:$0.672/M
Keluaran:$2.688/M
Masukan:$0.84/M
Keluaran:$3.36/M
-20%

Kod contoh dan API untuk GLM 5

Akses kod sampel yang komprehensif dan sumber API untuk GLM 5 bagi memperlancar proses integrasi anda. Dokumentasi terperinci kami menyediakan panduan langkah demi langkah, membantu anda memanfaatkan potensi penuh GLM 5 dalam projek anda.
Python
JavaScript
Curl
from openai import OpenAI
import os

# Get your CometAPI key from https://api.cometapi.com/console/token
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com/v1"

client = OpenAI(base_url=BASE_URL, api_key=COMETAPI_KEY)

# glm-5: Zhipu GLM-5 model via chat/completions
completion = client.chat.completions.create(
    model="glm-5",
    messages=[{"role": "user", "content": "Hello! Tell me a short joke."}],
)

print(completion.choices[0].message.content)

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