GLM-5 on Vercel AI Gateway

GLM-5 · Zhipu AI

ServerlessOpen Source

Last refreshed 2026-06-30. Next refresh: weekly.

Why use GLM-5 on Vercel AI Gateway?

Vercel AI Gateway offers GLM-5 with pay-as-you-go pricing at $1.00/1M input tokens. Vercel AI Gateway is a unified AI proxy providing a single OpenAI-compatible API endpoint to 275+ models from 25+ providers including Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek, xAI, Alibaba, Amazon, ByteDance, Cohere, MiniMax, MoonshotAI, KwaiPilot, Black Forest Labs, Recraft, Voyage AI, NVIDIA, and more.

Compare GLM-5 across 7 providers to find the best fit for your use case
Input / 1M
$1.00
Output / 1M
$3.20
Cache
read $0.20
Batch
Not sourced

Setup recipe

Python + curl
Install
pip install openai
Auth
export AI_GATEWAY_API_KEY=...
Call
import os
from openai import OpenAI
client = OpenAI(
    api_key=os.environ["AI_GATEWAY_API_KEY"],
Model ID
zai/glm-5

Request example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AI_GATEWAY_API_KEY"],
    base_url="https://ai-gateway.vercel.sh/v1"
)
response = client.chat.completions.create(
    model="zai/glm-5",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

Gotchas

  • Use provider model ID "zai/glm-5", not the LLMReference slug "glm-5".
  • creator/model-name e.g. kwaipilot/kat-coder-pro-v2
  • The examples expect AI_GATEWAY_API_KEY; rename it only if your application config maps the new variable.

Compare GLM-5 Across Providers

ProviderInput (per 1M)Output (per 1M)
Fireworks AI$1.00$3.20
OpenRouter$0.60$2.08
Together AI$1.00$3.20
GCP Vertex AI$1.00$3.20
NVIDIA NIM——
View all 7 providers →

Pricing

TypePrice (per 1M)
Input tokens$1.00
Output tokens$3.20

Capabilities

ReasoningJSON / Tool useStructured OutputsPrompt Caching

About GLM-5

Flagship open-weight foundation model from Zhipu AI with 744B parameters (40B active per token) in Mixture of Experts architecture. Trained on 28.5T tokens using DeepSeek Sparse Attention on Huawei Ascend hardware. Achieves state-of-the-art performance on coding and agentic benchmarks (SWE-bench Verified: 77.8%). Supports autonomous planning, multi-step tool use, and self-correction.

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Model Specs

Released2026-02-11
Parameters744B total, 40B active
Context200k
ArchitectureMixture of Experts
Knowledge cutoff2025-11

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