GLM-5

GLM-5

Z.ai
+
+

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About

GLM-5 is Z.ai’s latest large language model built for complex systems engineering and long-horizon agentic tasks. It scales significantly beyond GLM-4.5, increasing total parameters and training data while integrating DeepSeek Sparse Attention to reduce deployment costs without sacrificing long-context capacity. The model combines enhanced pre-training with a new asynchronous reinforcement learning infrastructure called slime, improving training efficiency and post-training refinement. GLM-5 achieves best-in-class performance among open-source models across reasoning, coding, and agent benchmarks, narrowing the gap with leading frontier models. It ranks highly on evaluations such as Vending Bench 2, demonstrating strong long-term planning and operational capabilities. The model is open-sourced under the MIT License.

About

Muse Spark 1.2 is Meta’s coding-focused model update designed to power Muse Code and improve software engineering workflows. The model is built for code generation, complex debugging, codebase understanding, long-horizon development tasks, and end-to-end developer workflows. Muse Spark 1.2 was co-trained with Muse Code to improve performance inside the terminal coding agent environment. It supports planning, goal conditioning, context compaction, subagent coordination, and iterative coding workflows across large repositories. The model was trained with expanded coding compute, diverse development environments, self-improvement loops, and long-running engineering tasks. Built for AI developers and software teams, Muse Spark 1.2 helps agents plan, write, validate, debug, and optimize code with greater autonomy.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

AI researchers, developers, and engineering teams seeking an open-source, high-performance foundation model for advanced reasoning, coding, and long-horizon agentic applications

Audience

AI developers, software engineers, coding agent builders, research teams, platform teams, DevOps teams, ML engineers, enterprise development teams, and organizations that need code generation, debugging, codebase understanding, long-horizon coding, terminal agents, subagent coordination, repository automation, kernel optimization, and end-to-end developer workflow support

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Open source
Free Version
Free Trial

Pricing

$1.25 per 1M tokens (input)
$1.25 per million tokens in input, and $4.25 per million tokens of output
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

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Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5

Pros & Cons from Real Users

Pros

  • Muse Spark 1.2 looks like a big step up for developers because it is clearly aimed at real software engineering work, not just casual code suggestions. The fact that it powers Muse Code makes it feel more practical right away, especially for terminal-based workflows where the model can help write code, validate changes, and work through bigger tasks. I like that Meta seems to be pushing hard into agentic coding. Earlier Muse Spark versions were already positioned around multimodal reasoning, tool use, and visual coding, and 1.2 feels like the more developer-focused evolution of that direction. The cost angle is interesting too. Reports mention Muse Code having multiple pricing tiers, including a cheaper option, which could matter a lot for developers running coding agents frequently instead of only using AI once in a while.

Cons

  • It is still new and tied to a beta coding agent, so I would not trust it blindly yet. I would want to test it on real repos, messy bugs, failing tests, multi-file edits, and longer agent runs before making it part of my daily stack. Meta also still has to prove the developer experience. A strong model is one thing, but coding agents live or die on tooling, speed, reliability, permissions, logs, diffs, and how well they recover when something breaks.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Z.ai
Founded: 2023
China
z.ai/

Company Information

Meta
Founded: 2004
United States
meta.ai

Alternatives

Alternatives

Claude Opus 4.5

Claude Opus 4.5

Anthropic
Claude Opus 4.6

Claude Opus 4.6

Anthropic
GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
GLM-5.3

GLM-5.3

Z.ai
ERNIE 5.1

ERNIE 5.1

Baidu

Categories

Categories

Integrations

Cheaper Inference
Claude Code
OpenClaw
C
C#
Cherry Studio
Cline
Codex CLI
Continue
Gray Swan
Instagram
LlamaIndex
OpenCode
PHP
Qoder
Roo Code
Scala
Shiori
Solidity
Yonoo

Integrations

Cheaper Inference
Claude Code
OpenClaw
C
C#
Cherry Studio
Cline
Codex CLI
Continue
Gray Swan
Instagram
LlamaIndex
OpenCode
PHP
Qoder
Roo Code
Scala
Shiori
Solidity
Yonoo
Claim GLM-5 and update features and information
Claim GLM-5 and update features and information
Claim Muse Spark 1.2 and update features and information
Claim Muse Spark 1.2 and update features and information