at a glance
| GLM-4.7 Flash | GPT-5.4 | |
|---|---|---|
| provider | Zhipu AI | OpenAI |
| parameters | 730B total / 3B active (MoE) | ~large (est.) |
| context window | 128k tokens | 1m tokens |
benchmarks
what are these models?
GLM-4.7 Flash is Zhipu AI’s fast inference model from the GLM-4.7 family — optimized for speed and low-cost deployment. It is designed for efficient, high-throughput inference rather than maximum capability.
GPT-5.4 is OpenAI’s flagship model in the GPT-5.4 family — the highest-capability tier designed for maximum reasoning, agentic reliability, and near-perfect tool-use performance. It is closed-source and accessed via OpenAI’s API.
benchmark breakdown
GPT-5.4 leads across all benchmarks. This comparison is not close:
- GPQA Diamond: 93.0% vs 75.2% — 17.8-point gap on graduate science
- TAU2-Bench: 98.9% vs 79.5% — 19.4 points ahead on agentic tool use (GPT-5.4 is near-perfect)
- Terminal Bench 2: 75.1% vs 64.0%* — 11 points ahead on shell automation
- HLE: 39.8–52.1% vs 14.4% — large gap on the hardest knowledge benchmark
The TAU2-Bench score of 98.9% for GPT-5.4 is notable — it’s near-perfect on multi-step agentic tool workflows.
what people are saying
when to use GLM-4.7 Flash
- cost and speed are primary constraints — you need fast, cheap inference at scale
- your task quality requirements are met by GLM’s capability tier
- you’re building prototypes or lightweight integrations that don’t require frontier accuracy
- Chinese-language tasks are a primary focus (Zhipu’s specialty)
when to use GPT-5.4
- you need the highest possible accuracy on any of these benchmarks
- agentic and computer-use tasks with near-perfect reliability are required
- graduate-level scientific reasoning is part of your workflow
- expert-knowledge retrieval and synthesis (HLE-class tasks) is a core use case
unlocking frontier performance with fine-tuning
While GPT-5.4 sets a high bar on knowledge-intensive and agentic tasks, fine-tuning GLM-4.7 Flash can close a meaningful portion of that gap in practice — especially when optimized on your own data and workflows. For many production use cases, this targeted tuning is enough to reach strong, reliable performance without incurring frontier-model costs.
On narrowly defined, high-volume tasks, fine-tuning a fast model like GLM-4.7 Flash doesn’t just compete — it often delivers the best efficiency-to-performance tradeoff.
frequently asked questions
is glm-4.7 flash competitive with gpt-5.4?
no — not on these benchmarks. gpt-5.4 leads by 15-20 points across science, agentic tasks, and expert knowledge. for tasks requiring frontier accuracy, gpt-5.4 is the stronger choice.
when does glm-4.7 flash make sense?
for high-volume, lower-complexity tasks where speed and cost matter more than pushing benchmark ceilings. it can be a practical choice for structured tasks in its capability tier.
what does the asterisk on glm’s terminal bench score mean?
the * indicates potential evaluation-specific conditions or self-reporting. the score may not be directly comparable to other models on the same benchmark.
are there open-weight alternatives to gpt-5.4?
yes — qwen3.5-397b-a17b approaches or beats gpt-5.4 on several benchmarks while being open-weight. see our qwen3.5-397b vs gpt-5.4 comparison for details.