Want this tightened or tailored for a landing page/README/press? I can adapt it. Here are sharper versions plus what info to add for credibility.
Options
- One‑liner: GLM‑5 is Z.ai’s open-source foundation model for end‑to‑end systems design and long‑horizon agent workflows—built for experts, production‑ready, and competitive with top closed models on large‑scale coding tasks.
- 3 value bullets:
- Agentic planning + iterative self‑correction for multi‑step, repo‑wide changes
- Strong backend reasoning and tool use; ships for production workloads
- Open weights, competitive benchmarks vs. closed leaders on code+agent tasks
- 3‑sentence blurb: GLM‑5 is Z.ai’s flagship open‑source model for complex systems design and long‑horizon agent workflows. Built for expert developers, it pairs advanced planning with deep backend reasoning and iterative self‑correction, enabling full‑system construction—not just code snippets. In benchmarks and real deployments, it delivers production‑grade performance on large programming tasks, rivaling leading closed models.
Make it concrete (replace placeholders and I’ll plug them in)
- Model sizes/context: {N}B params, {K} tokens context, {toolformer/routers?}
- Inference: {tok/s} on A100/H100, {batch size}, {throughput/latency P50/P95}
- Benchmarks: SWE‑bench verified {X}%, HumanEval+ {Y}%, MBPP {Z}%, AgentBench {A}%, Repo‑level tasks {B}% (with eval setup)
- Production: pass@k for PR generation, test coverage deltas, rollback rate, success on multi‑repo tasks
- Ecosystem: supports {tools} (Git, shell, HTTP, DB, code indexers), {frameworks} (LangChain, LlamaIndex), license {Apache‑2.0/MIT}, safety/guardrails
Copy tweaks to consider
- Replace “autonomous execution” with “safe autonomous execution with guardrails (approval gates, sandboxes, timeouts)”
- Avoid vague “rivaling” without numbers; pair every claim with a metric and hardware spec
- Add one concrete example: “Upgraded a 120‑service monorepo from Django 3.2→4.2 in 3.1 hours wall‑clock with 92% tests passing on first run”
Tell me the target (website hero, README, press note, tweet/thread), and any real metrics you can share—I’ll produce the final copy.
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.
Recursos para GLM 5
Explore os principais recursos do GLM 5, projetado para aprimorar o desempenho e a usabilidade. Descubra como essas capacidades podem beneficiar seus projetos e melhorar a experiência do usuário.
Preços para GLM 5
Explore preços competitivos para GLM 5, projetado para atender diversos orçamentos e necessidades de uso. Nossos planos flexíveis garantem que você pague apenas pelo que usar, facilitando o dimensionamento conforme suas necessidades crescem. Descubra como GLM 5 pode aprimorar seus projetos mantendo os custos gerenciáveis.
Preço do Comet (USD / M Tokens)
Preço Oficial (USD / M Tokens)
ModelDetail.discount
Entrada:$0.672/M
Saída:$2.688/M
Entrada:$0.84/M
Saída:$3.36/M
-20%
Código de exemplo e API para GLM 5
Acesse código de exemplo abrangente e recursos de API para GLM 5 para otimizar seu processo de integração. Nossa documentação detalhada fornece orientação passo a passo, ajudando você a aproveitar todo o potencial do GLM 5 em seus projetos.
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)