GLM-5-Turbo Matches Gemini Flash Speed
💡Private GLM-5-Turbo rivals top models—open-source soon?
⚡ 30-Second TL;DR
What Changed
Performs at or above Gemini 3.2 Flash level
Why It Matters
Highlights emerging Chinese models challenging Western leaders, potentially shifting competitive landscape if open-sourced.
What To Do Next
Test GLM-5-Turbo via OpenRouter API for high-speed tasks.
Key Points
- •Performs at or above Gemini 3.2 Flash level
- •Very fast inference on OpenRouter
- •Private model from Z.AI developer docs
- •No Hugging Face release yet
🧠 Deep Insight
Background and context from public sources — not the original article. 8 sources cited.
🔑 Enhanced Key Takeaways
- •GLM-5-Turbo is a specialized high-speed variant of the GLM-5 family, optimized specifically for fast inference in agent-driven environments like OpenClaw.[5][6]
- •GLM-5, the base model, uses a Mixture-of-Experts (MoE) architecture with 744B total parameters (40B active), trained on 28.5T tokens, and integrates DeepSeek Sparse Attention (DSA) for efficiency.[1][2]
- •GLM-5 achieves top open-model scores on agentic benchmarks like SWE-bench Verified (77.8), Terminal Bench 2.0 (56.2), and leads in BrowseComp, MCP-Atlas, and τ²-Bench.[4]
- •GLM-5 tops the Artificial Analysis Agentic Index at 63 among open weights models, with GDPval-AA ELO of 1412, excelling in knowledge work tasks.[3]
📊 Competitor Analysis▸ Show
| Feature | GLM-5-Turbo (Z.ai) | Gemini 3.2 Flash (Google) | DeepSeek V3 | Kimi K2 (Moonshot) |
|---|---|---|---|---|
| Parameter Scale | 744B total / 40B active (base GLM-5) | Proprietary | 671B / 37B active | 1T total / 32B active |
| Context Window | 205K tokens | Not specified | Comparable | Comparable |
| Key Benchmarks | SWE-bench 77.8, Agentic Index 63 | Surpassed by GLM-5 in some agent tasks | Lower agent scores | Lower agent scores |
| Pricing | Available via OpenRouter (details unspecified) | Proprietary API | Open weights | Open weights (INT4) |
| Precision/Size | BF16 / ~1.5TB | N/A | FP8 | INT4 |
🛠️ Technical Deep Dive
- •Architecture: Transformer-based Mixture-of-Experts (MoE) with 744B total parameters, 40B active per token, 80 layers, Multi-Head Attention, RMS Normalization, and Absolute Position Embedding.[1][2]
- •Attention Mechanism: DeepSeek Sparse Attention (DSA) dynamically allocates resources to reduce memory/compute for long sequences.[1][2]
- •Training: Pre-trained on 28.5T tokens emphasizing code and reasoning data; post-training uses 'slime' asynchronous RL framework for efficient multi-step interactions.[1][2][4]
- •Capabilities: 204,800-token context window, up to 128,000-token generation; supports tool-use, real-time streaming, structured output; text-only (no multimodal input).[1][3]
- •Deployment: Open weights under MIT License, BF16 precision requiring ~1,490GB VRAM; available via NVIDIA NIM and OpenRouter.[1][2]
🔮 Future ImplicationsAI analysis grounded in cited sources
⏳ Timeline
📎 Sources (8)
Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.
Weekly AI Recap
Read this week's curated digest of top AI events →
👉Related Updates
AI-curated news aggregator. All content rights belong to original publishers.
Original source: Reddit r/LocalLLaMA ↗
This is a summary, not the original. Read the source, or get the weekly briefing.
Weekly AI briefing
One email a week. Unsubscribe anytime.