GPT-5.4 mini vs GLM-5.3

It is close. Our weighted score puts them within 1 points (GLM-5.3 64/100, GPT-5.4 mini 63/100), so choose by what matters most for your work: GLM-5.3 for raw capability and GPT-5.4 mini on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GPT-5.4 mini is cheaper at $0.75 input / $4.50 output per million tokens (official OpenAI API price). GLM-5.3 costs $1.40 input / $4.40 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $1.69 per million tokens for GPT-5.4 mini versus $2.15 for GLM-5.3 (1.3× as much).

GLM-5.3 scores higher on the Capabilities Index (ECI): GLM-5.3 155.8 (#24 of 148) and GPT-5.4 mini 148.8 (#56 of 148). Their confidence ranges do not overlap (153.7–158.3 vs 147.1–150.5), so the gap is a real one. On individual benchmarks: GPQA Diamond — GLM-5.3 90.9%, GPT-5.4 mini 86.9%; FrontierMath Tiers 1–3 — GLM-5.3 68.8%, GPT-5.4 mini 51.2%; OTIS Mock AIME 2024–2025 — GLM-5.3 91.1%, GPT-5.4 mini 88.9%; SimpleQA Verified — GLM-5.3 41.0%, GPT-5.4 mini 29.4%.

There are no published SWE-bench Verified results for GPT-5.4 mini and GLM-5.3 yet, so there is no like-for-like coding score. On overall capability, GLM-5.3 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GLM-5.3 has the largest context window at 1,000,000 tokens, against 400,000 for GPT-5.4 mini. Maximum output per response: GPT-5.4 mini up to 128,000, GLM-5.3 up to 131,072 tokens.

GPT-5.4 mini accepts text and images; GLM-5.3 accepts text. GPT-5.4 mini handles the widest range of inputs.

GLM-5.3 publishes its weights and can be self-hosted; GPT-5.4 mini is proprietary.

GLM-5.3 is the newest, released Aug 14, 2026. GPT-5.4 mini came out Mar 17, 2026. Knowledge cutoff: GPT-5.4 mini Aug 31, 2025.

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.