Models / ZAIGLM /  / GLM-4.6 API

GLM-4.6 API

Advanced agentic AI with superior coding and reasoning capabilities

Coming Soon
Coming Soon

This model is not currently supported on Together AI.

Visit our Models page to view all the latest models.

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"
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)
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)

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

Looking for production scale? Deploy on a dedicated endpoint

Deploy GLM-4.6 on a dedicated endpoint with custom hardware configuration, as many instances as you need, and auto-scaling.

Get started