at a glance
| GLM-4.7 Flash | GPT-5.4-mini | |
|---|---|---|
| provider | Zhipu AI | OpenAI |
| parameters | 730B total / 3B active (MoE) | ~mid-size (est.) |
| context window | 128k tokens | 400k 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 cost-efficiency. It shows notably strong terminal task performance relative to its standing on other benchmarks.
GPT-5.4-mini is OpenAI’s mid-tier model in the GPT-5.4 family — faster and cheaper than the full GPT-5.4, but significantly more capable than nano. It delivers strong accuracy across reasoning, tool-use, and knowledge tasks. It is closed-source and accessed via OpenAI’s API.
benchmark breakdown
GPT-5.4-mini leads on most benchmarks. The science gap (88.0% vs 75.2%) and agentic tool-use gap (93.4% vs 79.5%) are substantial. HLE expert knowledge is not close: 28.2–41.5% vs 14.4%.
GLM-4.7 Flash wins on terminal tasks. Terminal Bench 2 shows 64.0%* vs 60.0% — a 4-point advantage for shell and CLI automation. This is GLM’s strongest result in this comparison.
Note: GLM-4.7 Flash’s Terminal Bench 2 score (64.0%*) may reflect specific evaluation conditions — verify independently if this benchmark is critical.
what people are saying
when to use GLM-4.7 Flash
- terminal and CLI automation is your primary focus
- you need fast, cost-efficient inference at scale
- Chinese-language tasks are relevant to your use case
when to use GPT-5.4-mini
- you need strong reasoning, science knowledge, or agentic tool use
- multi-step expert-knowledge tasks (HLE-class) are part of your workflow
- you want a hosted mid-tier API without infrastructure overhead
pushing performance further with fine-tuning
While GPT-5.4-mini shows strong leads in areas like science, tool use, and HLE, targeted fine-tuning can meaningfully narrow these gaps for many real-world applications. By training GLM-4.7 Flash on domain-specific data, teams can unlock substantial gains and reach competitive performance without relying on higher-cost models.
In terminal automation — where GLM already performs well — fine-tuning on your actual CLI environment can push results even further, often making it a highly efficient and specialized solution.
frequently asked questions
which model is better overall?
gpt-5.4-mini leads on three of four benchmarks by meaningful margins. glm-4.7 flash is only clearly competitive on terminal tasks.
why does glm-4.7 flash do well on terminal bench?
it may reflect training data distribution or optimizations specific to cli-style tasks. the asterisk on the score warrants independent verification.
is there an open-weight alternative that beats gpt-5.4-mini?
yes — qwen3.5-27b and qwen3.5-35b-a3b are both competitive with or better than gpt-5.4-mini on most benchmarks. see those comparison pages for details.
what does the asterisk mean?
the glm-4.7 flash terminal bench 2 score (64.0%*) may have been measured under specific evaluation conditions. treat it as directionally useful but verify for your exact use case.