VIDEO
HIGHLIGHT
Log InSign up
Z.ai GLM 4.5 Air Tested: Cheap, Open, and Not Tiny
SummaryTranscriptChat

Z.ai GLM 4.5 Air Tested: Cheap, Open, and Not Tiny

Overview of GLM 4.5 Air Model

Introduction to GLM 4.5 Air

  • GLM 4.5 Air is a large model with 106 billion parameters, designed for efficiency in coding tasks.
  • It features a mixture of experts model with only 12 billion active parameters and supports up to 128,000 tokens of context.
  • The focus is on understanding the practical applications and limitations of this model rather than its hype.

Key Questions Addressed

  • The discussion aims to answer three main questions regarding the purpose, developer workflow implications, and performance benchmarks of GLM 4.5 Air.
  • The conclusion emphasizes that while promising, the model should be tested carefully rather than trusted blindly.

Features and Capabilities

Model Specifications

  • Part of the larger GLM 4.5 family, which includes models with up to 355 billion total parameters.
  • Despite being lighter than its flagship counterpart, it remains a serious contender for agent-oriented tasks like software engineering.

Efficiency Mechanism

  • Utilizes a mixture of experts routing system allowing efficient use of parameters during processing.
  • Full-featured inference may require multiple H100 GPUs due to high computational demands.

Developer Considerations

Hybrid Reasoning Modes

  • Offers two reasoning modes: thinking mode for complex tasks and non-thinking mode for quicker responses.
  • This flexibility allows developers to optimize user experience based on task requirements.

Broader Developer Surface

  • Supports various functionalities including function calling, streaming outputs, and structured output formats.
  • Integration is simplified through an OpenAI compatible API structure reducing friction for developers.

Licensing and Pricing

Open Weights Importance

  • The open-sourcing under MIT license allows commercial use and secondary development which is appealing for developers.

Pricing Structure

  • Competitive pricing at $0.20 per million input tokens and $1.10 per million output tokens; variations exist across platforms.

Performance Metrics

Benchmark Results

  • Aggregate scores show GLM 4.5 at 63.2 and GLM 4.5 Air at 59.8 across twelve benchmarks indicating solid but not exceptional performance.

Coding Benchmark Insights

  • In initial coding benchmarks using Open Router's free route, the model scored approximately 60% success rate on specific coding tasks.

Task Reliability Analysis

Pass Patterns Observed

  • Simple tasks were generally successful while more complex workflows showed mixed results with many failures or partial completions.

Interpretation of Results

  • Emphasizes cautious interpretation; not all tasks are equally reliable indicating variability in performance based on task complexity.

Conclusion on Practical Use

Future Relevance

  • While newer models are emerging post-July 2025, GLM 4.5 Air remains relevant as an efficient option within a rapidly evolving landscape.

Recommendations

  • Suggested usage includes testing in low-cost environments where efficiency matters; however, caution against relying solely on free routes for production reliability is advised.
Video description

GLM 4.5 Air is cheap to try and open-weight, but it is not a tiny model. This video breaks down Z.ai's Air model, developer access, pricing, and our scoped coding benchmark. GLM 4.5 Air is the lighter member of Z.ai's GLM 4.5 family: 106B total parameters, 12B active parameters, 128K context, and an agent/coding focus. The useful question is not whether the model is hyped. It is where it fits for real developer workflows. In our first-party LLMBench coding run through OpenRouter's free z-ai/glm-4.5-air:free route, GLM 4.5 Air scored 1265/2100, or 60.24%, with 6 full passes across 21 coding cases. Treat that as one narrow provider-route run, not a universal verdict on the model. Chapters: 00:00 Air is not tiny 00:28 The real questions 00:55 What Z.ai built 01:33 MoE reality 02:05 Thinking mode 02:33 Developer surface 03:07 Open weights 03:41 Hosted pricing 04:13 Official scores need labels 04:46 Local coding benchmark 05:17 Pass pattern 05:50 Not the newest 06:21 Practical verdict 06:50 Benchmark before trust Sources and attribution: Z.ai GLM 4.5 docs: https://docs.z.ai/guides/llm/glm-4.5 Z.ai pricing: https://docs.z.ai/guides/overview/pricing Z.ai model overview: https://docs.z.ai/guides/overview/overview Z.ai release notes: https://docs.z.ai/release-notes/new-released Hugging Face GLM 4.5 Air model card: https://huggingface.co/zai-org/GLM-4.5-Air GLM 4.5 technical report: https://arxiv.org/abs/2508.06471 Z.ai GLM 4.5 README: https://raw.githubusercontent.com/zai-org/GLM-4.5/main/README.md Z.ai GLM 4.5 license file: https://raw.githubusercontent.com/zai-org/GLM-4.5/main/LICENSE OpenRouter GLM 4.5 Air: https://openrouter.ai/z-ai/glm-4.5-air OpenRouter GLM 4.5 Air free route: https://openrouter.ai/z-ai/glm-4.5-air%3Afree Z.ai quick start: https://docs.z.ai/guides/overview/quick-start Vercel AI Gateway changelog: https://vercel.com/changelog/z-ais-glm-4-5-and-glm-4-5-air-are-now-supported-in-vercel-ai-gateway Subscribe for practical AI coding model breakdowns, benchmark interpretation, and developer-tool decisions without the leaderboard fog. #AI #Coding #OpenWeights #LLMBench #DeveloperTools