GLM-5

GLM-5

Zhipu AI
TabFM

TabFM

Google
+
+

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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

TabFM is a zero-shot foundation model for tabular data, designed to simplify classification and regression workflows that traditionally require manual model training, hyperparameter tuning, and domain-specific feature engineering. Built specifically for tables, TabFM reframes tabular prediction as an in-context learning problem: instead of fitting a new supervised model to each dataset, it takes historical training examples and target testing rows together as one unified prompt, then interprets relationships between columns and rows at inference time. Because tables are two-dimensional and orderless, TabFM uses a hybrid architecture that combines alternating row and column attention, row compression, and a dedicated Transformer for in-context learning over compressed row embeddings. This design lets the model capture complex feature interactions and dependencies while keeping prediction computationally efficient for larger datasets.

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

Data scientists and analytics teams that need accurate tabular classification and regression without repetitive training, tuning, and feature-engineering work

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

Free
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:

Review this Software

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:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Zhipu AI
Founded: 2023
China
z.ai/

Company Information

Google
Founded: 1998
United States
research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/

Alternatives

Alternatives

Evo 2

Evo 2

Arc Institute
Claude Opus 4.5

Claude Opus 4.5

Anthropic
Claude Opus 4.6

Claude Opus 4.6

Anthropic
ERNIE 5.1

ERNIE 5.1

Baidu
DeepSeek-V3.2

DeepSeek-V3.2

DeepSeek
MLBox

MLBox

Axel ARONIO DE ROMBLAY

Categories

Categories

Integrations

APIFree
Cherry Studio
Claude Code
Claw Code
Cline
Dessix
GLM Coding Plan
GLM-5-Turbo
Kilo Code
Ollama
OpenClaw
OpenRouter
Oxlo.ai
Qoder
Roo Code
Shiori
Sup AI
Tabbit Browser
Yonoo
Zo Computer

Integrations

APIFree
Cherry Studio
Claude Code
Claw Code
Cline
Dessix
GLM Coding Plan
GLM-5-Turbo
Kilo Code
Ollama
OpenClaw
OpenRouter
Oxlo.ai
Qoder
Roo Code
Shiori
Sup AI
Tabbit Browser
Yonoo
Zo Computer
Claim GLM-5 and update features and information
Claim GLM-5 and update features and information
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