That's a wrap on ModCon 2026! Read the highlights ➔

All models
GLM 5.3 logo

GLM 5.3: Z.ai's open-weights coding and agentic MoE model

GLM 5.3 is Z.ai's flagship Mixture-of-Experts model, built to code and ready for cyber defense. Sharing the GLM 5.2 base with substantially scaled post-training, it sustains a 1M-token context window with up to 128K output tokens and posts open-weights state-of-the-art results on Terminal-Bench 3.0 and agentic coding benchmarks.

Deploy dedicated endpoint
Try in Playground

Example Usage

Output

  Speculative decoding uses a smaller draft model to predict multiple
  tokens ahead, then verifies them against the full model in a single
  pass. Accepted tokens skip individual generation steps, improving
  throughput without sacrificing accuracy. It's most effective when the
  draft model closely matches the target model's distribution.
Code to use
Python

  from openai import OpenAI
  
  client = OpenAI(
      base_url="https://model.api.modular.com",
      api_key="<your_api_token>",
  )
  
  response = client.chat.completions.create(
      model="zai-org/GLM-5.3",
      messages=[
          {"role": "system", "content": "You are a helpful assistant."},
          {"role": "user", "content": "Explain speculative decoding in 3 sentences."},
      ],
      stream=True,
  )
  
  for chunk in response:
      if chunk.choices[0].delta.content:
          print(chunk.choices[0].delta.content, end="")
Get your API token
Model Details
  • Developed by
    Z.ai
  • Model family
    zai-org/GLM-5.3
  • Modality
    LLM,
  • Context Window
    1M
  • Total Params
    743B
  • Precision
    BF16 / FP8
  • Deployment options
    Shared, Dedicated, Self-hosted

Why choose GLM 5.3 on Modular?

  • High performance, out of the box

    Run leading open models with strong default performance and the ability to optimize down to the kernel — extracting more from every GPU.

  • Lower Infrastructure Costs

    Deploy efficiently across NVIDIA and AMD hardware to reduce GPU count, increase throughput, and avoid expensive closed-model licensing.

  • Easy Integration

    Integrate through an OpenAI-compatible endpoint, swap models freely, and scale across clouds or hardware without redesigning your application stack.

GLM 5.3
Want to self-host this model with our open source infrastructure?
Read How

🔥 Trending models

FLUX.2 Klein 9B logo
FLUX.2 Klein 9B

FLUX.2 [klein] 9B is a 9 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.

Image
MiniMax M3 logo
MiniMax M3

MiniMax M3 is an open-weight, natively multimodal frontier model with ~428B total parameters and ~23B activated parameters. It combines frontier-level coding and agentic performance, an ultra-long context window of up to 1M tokens, and mixed-modality training across text, image, and video.

LLM
Vision
Kimi K2.7 Code logo
Kimi K2.7 Code

Kimi K2.7 Code is Moonshot AI's coding-focused agentic model, built on the Kimi K2.6 architecture. K2.7 Code delivers substantial gains on real-world long-horizon software engineering tasks while reducing thinking-token usage by approximately 30% compared with K2.6.

LLM
Vision
Inkling logo
Inkling

Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. The model is designed to be used by developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-following, and other natural language and multimodal tasks.

LLM
Vision
Audio

Similar models

Llama 4 Scout logo
Llama 4 Scout

Llama 4 Scout by Meta is a 109B MoE model with 17B active parameters and 16 experts, supporting text and vision with a 10M context window.

LLM
Vision
Gemma 4 26B A4B logo
Gemma 4 26B A4B

Gemma 4 26B A4B is a Mixture-of-Experts (MoE)model with 26B total parameters but only 4B activated per forward pass, meaning you get the quality of a much larger model at a fraction of the compute cost. It also supports a 256K context window and is designed to fit the memory footprint of high-end servers.

LLM
Vision
gpt-oss-120b logo
gpt-oss-120b

gpt-oss-120b by OpenAI is a 117B MoE model with 5.1B active parameters and 128 experts, featuring reasoning capabilities.

LLM
Kimi K2.5 logo
Kimi K2.5

Kimi K2.5 by Moonshot AI is a ~1T MoE model with 32B active parameters, supporting text and vision with reasoning capabilities.

LLM
Vision
View all models

Get started with Modular

  • Request a demo

    Schedule a demo of Modular and explore a custom end-to-end deployment built around your models, hardware, and performance goals.

    • Distributed, large-scale online inference endpoints

    • Highest-performance to maximize ROI and latency

    • Deploy in Modular cloud or your cloud

    • View all features with a custom demo

    Book a demo

    Talk with our sales lead Adam!

    30min demo.  Evaluate with your workloads.  Ask us anything.

  • Talk to us!

    Book a demo for a personalized walkthrough of Modular in your environment. Learn how teams use it to simplify systems and tune performance at scale.

    • Custom 30 min walkthrough of our platform

    • Cover specific model or deployment needs

    • Flexible pricing to fit your specific needs

    Book a demo

    Talk with our sales lead Jay!

  • Start using MAX

    ( FREE )

    Run any open source model in 5 minutes, then benchmark it. Scale it to millions yourself (for free!).

    Install MAX
    What is MAX?

  • Start using Mojo

    ( FREE )

    Install Mojo and get up and running in minutes. A simple install, familiar tooling, and clear docs make it easy to start writing code immediately.

    Install Mojo🔥
    What is Mojo🔥?