at a glance
| GLM-5 | GPT-5.4 | |
|---|---|---|
| provider | Zhipu AI | OpenAI |
| parameters | 744B total / 40B active (MoE) | ~large (est.) |
| context window | 200k tokens | 1m tokens |
benchmarks
what are these models?
GLM-5 is Zhipu AI’s flagship language model — the successor to GLM-4 and the top of their current lineup. It is designed for strong reasoning, coding, and tool-use capabilities, competing at the frontier against closed models from OpenAI and Anthropic.
GPT-5.4 is OpenAI’s flagship model in the GPT-5.4 family — the highest-capability tier, with near-perfect agentic tool use and top-tier reasoning across all benchmark categories. It is closed-source and accessed via OpenAI’s API.
benchmark breakdown
GPT-5.4 leads on agentic tasks. TAU2-Bench (98.9% vs 89.7%) is near-perfect for GPT-5.4 — a 9.2-point gap. Terminal Bench 2 (75.1% vs 56.2%) shows an 18.9-point advantage on shell tasks.
GPT-5.4 leads on science and expert knowledge. GPQA Diamond (93.0% vs 86.0%) is a 7-point gap. HLE without tools (39.8% vs 30.5%) shows a larger gap in raw knowledge retrieval; HLE with tools narrows to 52.1% vs 50.4% — nearly equivalent.
what people are saying
when to use GLM-5
- you want a model with strong HLE-with-tools performance (within 2 points of GPT-5.4)
- cost efficiency is a concern at the frontier
- you’re exploring alternatives to OpenAI’s ecosystem
when to use GPT-5.4
- you need near-perfect agentic tool use (TAU2 at 98.9%)
- terminal and shell automation are core workflows
- graduate-level scientific reasoning is required
- you want the highest-known performance on expert knowledge tasks
closing the gap with fine-tuning
For agentic tasks where GPT-5.4 currently leads, the raw gap is meaningful — but it’s also highly tunable. With the right data and feedback loops, fine-tuning can dramatically improve planning, tool use, and multi-step reasoning in production settings.
frequently asked questions
which model is better for agent workflows?
gpt-5.4 — 98.9% on tau2-bench is near-perfect. for high-stakes multi-step tool workflows, gpt-5.4’s reliability is unmatched.
how do they compare on the hardest knowledge tasks?
hle with tools: 50.4% vs 52.1% — essentially tied. raw knowledge without tools: gpt-5.4 leads 39.8% vs 30.5%.
is there an open-weight model that competes with gpt-5.4?
qwen3.5-397b-a17b beats gpt-5.4 on mmmu-pro and is within 5 points on gpqa diamond. see that comparison page for details.