Models / ZAIGLM /  / GLM-4.6 API

GLM-4.6 API

Advanced agentic AI with superior coding and reasoning capabilities

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Introducing GLM-4.6

GLM-4.6 is the latest flagship model from Z.ai's GLM series, delivering state-of-the-art agentic and coding capabilities that rival Claude Sonnet 4. With 357B parameters in a Mixture-of-Experts architecture, an expanded 200K context window, and 30% improved token efficiency, GLM-4.6 represents the top-performing model developed in China.

48.6%
Win Rate vs Claude Sonnet 4
Real-world coding tasks (CC-Bench)
200K
Context Window
Extended from 128K for complex agentic tasks
30%
More Token Efficient
Compared to GLM-4.5 for equivalent tasks
Key Capabilities
Advanced Agentic Reasoning: Competitive with Claude Sonnet 4 across 8 authoritative benchmarks (AIME 25, GPQA, LCB v6, HLE)
Enhanced Tool Use: Native function calling with autonomous planning and cross-tool collaboration
Refined Writing & Translation: Human-aligned content creation and optimized multilingual capabilities

GLM-4.6 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-4.6",
    "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-4.6",
    "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-4.6",
    "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-4.6",
    "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-4.6",
    "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-4.6"
  }'
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-4.6"
  }'
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-4.6"
  }'
curl -X POST "https://api.together.xyz/v1/audio/transcriptions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -F "model=zai-org/GLM-4.6" \
  -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-4.6",
    "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-4.6",
    "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-4.6",
  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-4.6",
    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-4.6",
    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-4.6",
  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-4.6",
  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-4.6",
  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-4.6",
)

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-4.6",
  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-4.6",
    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-4.6"
)
from together import Together

client = Together()

job = client.videos.create(
    model="zai-org/GLM-4.6",
    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-4.6',
  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-4.6",
    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-4.6",
    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-4.6",
    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-4.6",
    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-4.6',
  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-4.6"
  });
  
  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-4.6',
  });

  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-4.6",
  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-4.6"
  });
import Together from "together-ai";

const together = new Together();

const job = await together.videos.create({
  model: "zai-org/GLM-4.6",
  frame_images: [
    {
      input_image: "https://cdn.pixabay.com/photo/2020/05/20/08/27/cat-5195431_1280.jpg",
    }
  ]
});

How to use GLM-4.6

Model details

Architecture Overview:
• Mixture-of-Experts (MoE) architecture with 357B total parameters optimized for efficient inference
• Extended context window from 128K to 200K tokens enabling complex agentic task handling
• Advanced attention mechanisms supporting multi-turn conversations and long-form content generation
• Optimized token efficiency achieving 30% reduction in consumption compared to GLM-4.5

Training Methodology:
• Trained on diverse multilingual datasets with emphasis on code, reasoning, and conversational data
• Enhanced alignment training for human preference matching in writing style and readability
• Specialized training for tool-use capabilities and agentic behavior
• Reinforcement learning from human feedback (RLHF) for improved instruction following

Performance Characteristics:
• Competitive performance with Claude Sonnet 4 across 8 authoritative benchmarks (AIME 25, GPQA, LCB v6, HLE, SWE-Bench Verified)
• 48.6% win rate against Claude Sonnet 4 in real-world coding tasks (CC-Bench evaluation)
• Superior aesthetics and logical layout in frontend code generation
• Enhanced translation quality for minor languages (French, Russian, Japanese, Korean)
• Top-performing model developed in China with state-of-the-art domestic capabilities

Prompting GLM-4.6

Conversation Format:
• Multi-turn conversation support with full context retention across 200K tokens
• System message configuration for role definition and behavior customization
• Streaming and non-streaming response modes available
• Thinking mode with tool-use capabilities during inference

Advanced Techniques:
• Recommended temperature: 1.0 for general tasks
• Code-related tasks: top_p=0.95, top_k=40 for optimal results
• Tool-integrated reasoning with native function calling support
• Search-based agent capabilities with specialized toolcall formatting
• Maximum output tokens: 128K for extended generation tasks

Optimization Strategies:
• 15% more token-efficient than GLM-4.5 for equivalent task completion
• Native support for autonomous planning and tool invocation in agentic workflows
• Enhanced task decomposition and cross-tool collaboration capabilities
• Dynamic adjustment support for complex development and office automation workflows

Applications & Use Cases

AI Coding & Development:
• Superior performance in Python, JavaScript, and Java with aesthetically advanced frontend code generation
• Real-world coding excellence demonstrated across 74 CC-Bench evaluation tasks
• Native integration with popular coding assistants and agent frameworks
• Enhanced debugging, testing, and algorithm implementation capabilities

Agentic Applications:
• Complex multi-step task execution with autonomous planning and tool invocation
• Search-based agents with enhanced user intent understanding and result integration
• Office automation including PowerPoint creation with aesthetically advanced layouts
• Deep Research scenarios with comprehensive information synthesis

Smart Office & Automation:
• High-quality presentation generation with clear logical structures
• Document creation maintaining content integrity and expression accuracy
• Cross-tool collaboration for complex development and office workflows
• Ideal for AI presentation tools and office automation systems

Translation & Multilingual Content:
• Optimized translation for French, Russian, Japanese, Korean and informal contexts
• Semantic coherence and stylistic consistency in lengthy passages
• Superior style adaptation and localized expression for global enterprises
• Suitable for social media, e-commerce content, and cross-border services

Content Creation & Virtual Characters:
• Diverse content production including novels, scripts, and copywriting
• Natural expression through contextual expansion and emotional regulation
• Consistent tone and behavior across multi-turn conversations
• Ideal for virtual humans, social AI, and brand personification operations

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