Agentic capability hype vs reality: GLM 4.6, Granite 4, and the limits of 'thinking' in LLMs

Agentic capability is the hot topic, and 2025’s chatter centers on two names: GLM 4.6 and Granite 4. Enthusiasts tout tool calls and long-context autonomy, while critics flag real-world limits and mixed accuracy ^[1]. The takeaway? hype clashes with benchmarks and practical use cases.

GLM 4.6 for agentic tasks — Proponents call GLM 4.6 astonishing for agentic work, often outperforming rivals in tool calls and staying coherent over long tasks ^[1]. Some testers report it feeling more autonomous than proprietary players like Sonnet, GPT 5, or others, though one user notes it can “think” for too long unless prompts are tuned ^[1]. In code-review scenarios, GLM 4.6 reportedly did better than Qwen 235B on the same codebase ^[1].

Granite 4: speed, context windows, and accuracy — Granite peers emphasize speed and context handling but with caveats: - Granite 4 small 32B runs surprisingly fast (about 79 tokens/sec from blank) and scales up to around 128k context, yet its performance on hard questions lags behind SEED OSS; memory per context remains low ^[3]. - A version offered via Ollama is touted as insanely fast for a 1.9GB model and is linked to a claimed 1M context window; speed considerations rise as the window grows ^[2]. - The architecture is described as a Mamba/Transformer hybrid, with ISO 42001 certification discussions, and experimentation notes show mixed results depending on API usage and quantization ^[2].

Bottom line: agentic tools can help with slots like code research, but long thinking times, context limits, and real-world accuracy keep expectations grounded ^[1][3].

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