Models / ZAIGLM /  / GLM-4.7 API

GLM-4.7 API

Advanced agentic coding and reasoning with instant serverless access on Together AI

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Frontier Agentic Coding:
GLM-4.7 is Z.AI's latest flagship model engineered for task-oriented development and complex agent workflows. Ranking #1 open-source on LMArena Code Arena and achieving 73.8% on SWE-bench Verified, it delivers enhanced agentic coding, superior frontend aesthetics, and stable multi-step reasoning through advanced interleaved thinking. Access instantly via Together AI serverless APIs for rapid prototyping, evaluation, and production deployment with 200K context and 128K max output.

GLM-4.7 API Usage

Endpoint

curl -X POST "https://api.together.xyz/v1/chat/completions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zai-org/GLM-4.7",
    "messages": [
      {
        "role": "user",
        "content": "What are some fun things to do in New York?"
      }
    ]
}'
curl -X POST "https://api.together.xyz/v1/images/generations" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "zai-org/GLM-4.7",
    "prompt": "Draw an anime style version of this image.",
    "width": 1024,
    "height": 768,
    "steps": 28,
    "n": 1,
    "response_format": "url",
    "image_url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
  }'
curl -X POST https://api.together.xyz/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -d '{
    "model": "zai-org/GLM-4.7",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Describe what you see in this image."},
        {"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"}}
      ]
    }],
    "max_tokens": 512
  }'
curl -X POST https://api.together.xyz/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -d '{
    "model": "zai-org/GLM-4.7",
    "messages": [{
      "role": "user",
      "content": "Given two binary strings `a` and `b`, return their sum as a binary string"
    }]
  }'
curl -X POST https://api.together.xyz/v1/rerank \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -d '{
    "model": "zai-org/GLM-4.7",
    "query": "What animals can I find near Peru?",
    "documents": [
      "The giant panda (Ailuropoda melanoleuca), also known as the panda bear or simply panda, is a bear species endemic to China.",
      "The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
      "The wild Bactrian camel (Camelus ferus) is an endangered species of camel endemic to Northwest China and southwestern Mongolia.",
      "The guanaco is a camelid native to South America, closely related to the llama. Guanacos are one of two wild South American camelids; the other species is the vicuña, which lives at higher elevations."
    ],
    "top_n": 2
  }'
curl -X POST https://api.together.xyz/v1/embeddings \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Our solar system orbits the Milky Way galaxy at about 515,000 mph.",
    "model": "zai-org/GLM-4.7"
  }'
curl -X POST https://api.together.xyz/v1/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -d '{
    "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    "prompt": "A horse is a horse",
    "max_tokens": 32,
    "temperature": 0.1,
    "safety_model": "zai-org/GLM-4.7"
  }'
curl --location 'https://api.together.ai/v1/audio/generations' \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer $TOGETHER_API_KEY' \
  --output speech.mp3 \
  --data '{
    "input": "Today is a wonderful day to build something people love!",
    "voice": "helpful woman",
    "response_format": "mp3",
    "sample_rate": 44100,
    "stream": false,
    "model": "zai-org/GLM-4.7"
  }'
curl -X POST "https://api.together.xyz/v1/audio/transcriptions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -F "model=zai-org/GLM-4.7" \
  -F "language=en" \
  -F "response_format=json" \
  -F "timestamp_granularities=segment"
curl --request POST \
  --url https://api.together.xyz/v2/videos \
  --header "Authorization: Bearer $TOGETHER_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "zai-org/GLM-4.7",
    "prompt": "some penguins building a snowman"
  }'
curl --request POST \
  --url https://api.together.xyz/v2/videos \
  --header "Authorization: Bearer $TOGETHER_API_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "zai-org/GLM-4.7",
    "frame_images": [{"input_image": "https://cdn.pixabay.com/photo/2020/05/20/08/27/cat-5195431_1280.jpg"}]
  }'

from together import Together

client = Together()

response = client.chat.completions.create(
  model="zai-org/GLM-4.7",
  messages=[
    {
      "role": "user",
      "content": "What are some fun things to do in New York?"
    }
  ]
)
print(response.choices[0].message.content)
from together import Together

client = Together()

imageCompletion = client.images.generate(
    model="zai-org/GLM-4.7",
    width=1024,
    height=768,
    steps=28,
    prompt="Draw an anime style version of this image.",
    image_url="https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png",
)

print(imageCompletion.data[0].url)


from together import Together

client = Together()

response = client.chat.completions.create(
    model="zai-org/GLM-4.7",
    messages=[{
    	"role": "user",
      "content": [
        {"type": "text", "text": "Describe what you see in this image."},
        {"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"}}
      ]
    }]
)
print(response.choices[0].message.content)

from together import Together

client = Together()
response = client.chat.completions.create(
  model="zai-org/GLM-4.7",
  messages=[
  	{
	    "role": "user", 
      "content": "Given two binary strings `a` and `b`, return their sum as a binary string"
    }
 ],
)

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

from together import Together

client = Together()

query = "What animals can I find near Peru?"

documents = [
  "The giant panda (Ailuropoda melanoleuca), also known as the panda bear or simply panda, is a bear species endemic to China.",
  "The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
  "The wild Bactrian camel (Camelus ferus) is an endangered species of camel endemic to Northwest China and southwestern Mongolia.",
  "The guanaco is a camelid native to South America, closely related to the llama. Guanacos are one of two wild South American camelids; the other species is the vicuña, which lives at higher elevations.",
]

response = client.rerank.create(
  model="zai-org/GLM-4.7",
  query=query,
  documents=documents,
  top_n=2
)

for result in response.results:
    print(f"Relevance Score: {result.relevance_score}")

from together import Together

client = Together()

response = client.embeddings.create(
  model = "zai-org/GLM-4.7",
  input = "Our solar system orbits the Milky Way galaxy at about 515,000 mph"
)

from together import Together

client = Together()

response = client.completions.create(
  model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
  prompt="A horse is a horse",
  max_tokens=32,
  temperature=0.1,
  safety_model="zai-org/GLM-4.7",
)

print(response.choices[0].text)

from together import Together

client = Together()

speech_file_path = "speech.mp3"

response = client.audio.speech.create(
  model="zai-org/GLM-4.7",
  input="Today is a wonderful day to build something people love!",
  voice="helpful woman",
)
    
response.stream_to_file(speech_file_path)

from together import Together

client = Together()
response = client.audio.transcribe(
    model="zai-org/GLM-4.7",
    language="en",
    response_format="json",
    timestamp_granularities="segment"
)
print(response.text)
from together import Together

client = Together()

# Create a video generation job
job = client.videos.create(
    prompt="A serene sunset over the ocean with gentle waves",
    model="zai-org/GLM-4.7"
)
from together import Together

client = Together()

job = client.videos.create(
    model="zai-org/GLM-4.7",
    frame_images=[
        {
            "input_image": "https://cdn.pixabay.com/photo/2020/05/20/08/27/cat-5195431_1280.jpg",
        }
    ]
)
import Together from 'together-ai';
const together = new Together();

const completion = await together.chat.completions.create({
  model: 'zai-org/GLM-4.7',
  messages: [
    {
      role: 'user',
      content: 'What are some fun things to do in New York?'
     }
  ],
});

console.log(completion.choices[0].message.content);
import Together from "together-ai";

const together = new Together();

async function main() {
  const response = await together.images.create({
    model: "zai-org/GLM-4.7",
    width: 1024,
    height: 1024,
    steps: 28,
    prompt: "Draw an anime style version of this image.",
    image_url: "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png",
  });

  console.log(response.data[0].url);
}

main();

import Together from "together-ai";

const together = new Together();
const imageUrl = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png";

async function main() {
  const response = await together.chat.completions.create({
    model: "zai-org/GLM-4.7",
    messages: [{
      role: "user",
      content: [
        { type: "text", text: "Describe what you see in this image." },
        { type: "image_url", image_url: { url: imageUrl } }
      ]
    }]
  });
  
  console.log(response.choices[0]?.message?.content);
}

main();

import Together from "together-ai";

const together = new Together();

async function main() {
  const response = await together.chat.completions.create({
    model: "zai-org/GLM-4.7",
    messages: [{
      role: "user",
      content: "Given two binary strings `a` and `b`, return their sum as a binary string"
    }]
  });
  
  console.log(response.choices[0]?.message?.content);
}

main();

import Together from "together-ai";

const together = new Together();

const query = "What animals can I find near Peru?";
const documents = [
  "The giant panda (Ailuropoda melanoleuca), also known as the panda bear or simply panda, is a bear species endemic to China.",
  "The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
  "The wild Bactrian camel (Camelus ferus) is an endangered species of camel endemic to Northwest China and southwestern Mongolia.",
  "The guanaco is a camelid native to South America, closely related to the llama. Guanacos are one of two wild South American camelids; the other species is the vicuña, which lives at higher elevations."
];

async function main() {
  const response = await together.rerank.create({
    model: "zai-org/GLM-4.7",
    query: query,
    documents: documents,
    top_n: 2
  });
  
  for (const result of response.results) {
    console.log(`Relevance Score: ${result.relevance_score}`);
  }
}

main();


import Together from "together-ai";

const together = new Together();

const response = await client.embeddings.create({
  model: 'zai-org/GLM-4.7',
  input: 'Our solar system orbits the Milky Way galaxy at about 515,000 mph',
});

import Together from "together-ai";

const together = new Together();

async function main() {
  const response = await together.completions.create({
    model: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    prompt: "A horse is a horse",
    max_tokens: 32,
    temperature: 0.1,
    safety_model: "zai-org/GLM-4.7"
  });
  
  console.log(response.choices[0]?.text);
}

main();

import Together from 'together-ai';

const together = new Together();

async function generateAudio() {
   const res = await together.audio.create({
    input: 'Today is a wonderful day to build something people love!',
    voice: 'helpful woman',
    response_format: 'mp3',
    sample_rate: 44100,
    stream: false,
    model: 'zai-org/GLM-4.7',
  });

  if (res.body) {
    console.log(res.body);
    const nodeStream = Readable.from(res.body as ReadableStream);
    const fileStream = createWriteStream('./speech.mp3');

    nodeStream.pipe(fileStream);
  }
}

generateAudio();

import Together from "together-ai";

const together = new Together();

const response = await together.audio.transcriptions.create(
  model: "zai-org/GLM-4.7",
  language: "en",
  response_format: "json",
  timestamp_granularities: "segment"
});
console.log(response)
import Together from "together-ai";

const together = new Together();

async function main() {
  // Create a video generation job
  const job = await together.videos.create({
    prompt: "A serene sunset over the ocean with gentle waves",
    model: "zai-org/GLM-4.7"
  });
import Together from "together-ai";

const together = new Together();

const job = await together.videos.create({
  model: "zai-org/GLM-4.7",
  frame_images: [
    {
      input_image: "https://cdn.pixabay.com/photo/2020/05/20/08/27/cat-5195431_1280.jpg",
    }
  ]
});

How to use GLM-4.7

Model details

Core Coding Capabilities:
• #1 open-source model on LMArena Code Arena (outperforming GPT-5.2)
• 73.8% on SWE-bench Verified (+5.8% over GLM-4.6)
• 66.7% on SWE-bench Multilingual (+12.9% improvement)
• 84.9% on LiveCodeBench-v6 (surpassing Claude Sonnet 4.5 at 64%)
• 41% on Terminal Bench 2.0 (+16.5% improvement)

Advanced Reasoning:
• AIME 2025: 95.7% (open-source SOTA)
• GPQA-Diamond: 85.7%
• HLE with Tools: 42.8% (+12.4% over GLM-4.6)
• HMMT Feb 2025: 97.1%
• MMLU-Pro: 84.3%

Agent & Tool Capabilities:
• τ²-Bench: 87.4% (approaching Claude Sonnet 4.5 at 87.2%)
• BrowseComp: 52% (67.5% with context management)
• Significantly improved tool-calling and web browsing performance

Interleaved Thinking System:
• Interleaved Thinking: Thinks before every response and tool call for improved instruction following
• Preserved Thinking: Automatically retains thinking blocks across multi-turn conversations, reducing information loss
• Turn-level Thinking: Per-turn control over reasoning—disable for lightweight requests, enable for complex tasks
• Optimal for long-horizon, complex agentic workflows

Vibe Coding:
• Enhanced UI quality with cleaner, more modern webpages
• Better-looking slides with accurate layout and sizing
• Improved frontend aesthetic generation for low-code platforms and rapid prototyping

Architecture:
• 200K context window with 128K maximum output tokens
• MIT licensed, open weights
• Supports vLLM, SGLang, and Transformers inference frameworks

Prompting GLM-4.7

Applications & Use Cases

Agentic Coding & Development:
• End-to-end software engineering from requirement comprehension to executable code
• Multilingual agentic coding across multiple programming languages
• Terminal-based development tasks with stable multi-step execution

Frontend & UI Generation:
• Production-quality web UI generation with enhanced aesthetics
• Modern webpage creation with clean layouts and accurate sizing
• Slide and poster generation with professional design consistency
• Low-code platforms and AI frontend generation tools

Complex Agent Workflows:
• Long-horizon tasks requiring preserved reasoning across turns
• Web browsing and information retrieval with context management
• Tool-calling and function execution for enterprise automation
• Real-world interaction scenarios (τ²-Bench: retail, telecom, airline)

Enterprise Applications:
• Development support and solution discussions with context-aware responses
• Decision-making assistance with advanced reasoning capabilities
• Mathematical problem-solving and scientific reasoning
• Creative writing, role-play, and conversational AI with improved quality

Research & Prototyping:
• Rapid prototyping with superior UI aesthetics and accurate implementation
• Complex demos and proof-of-concept development
• Educational tools and interactive learning applications with 200K context support

Looking for production scale? Deploy on a dedicated endpoint

Deploy GLM-4.7 on a dedicated endpoint with custom hardware configuration, as many instances as you need, and auto-scaling.

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