GPT-5.4 nano vs GLM-5.1

GPT-5.4 nano is the better all-round choice, scoring 68/100 against GLM-5.1 (57). It leads on price, inputs & features and context window. GLM-5.1 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GPT-5.4 nano is cheaper at $0.20 input / $1.25 output per million tokens (official OpenAI API price). GLM-5.1 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 $0.463 per million tokens for GPT-5.4 nano versus $2.15 for GLM-5.1 (4.6× as much).

GLM-5.1 scores higher on the Capabilities Index (ECI): GLM-5.1 149.9 (#51 of 148) and GPT-5.4 nano 145.8 (#75 of 148). Their confidence ranges do not overlap (148.0–151.6 vs 143.2–147.7), so the gap is a real one. On individual benchmarks: GPQA Diamond — GLM-5.1 89.9%, GPT-5.4 nano 78.5%; FrontierMath Tiers 1–3 — GPT-5.4 nano 44.9%, GLM-5.1 36.8%; OTIS Mock AIME 2024–2025 — GLM-5.1 93.3%, GPT-5.4 nano 87.8%; SimpleQA Verified — GLM-5.1 34.0%, GPT-5.4 nano 11.7%.

There are no published SWE-bench Verified results for GPT-5.4 nano yet, so there is no like-for-like coding score. On overall capability, GLM-5.1 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GPT-5.4 nano has the largest context window at 400,000 tokens, against 200,000 for GLM-5.1. Maximum output per response: GPT-5.4 nano up to 128,000, GLM-5.1 up to 131,072 tokens.

GPT-5.4 nano accepts text and images; GLM-5.1 accepts text. GPT-5.4 nano handles the widest range of inputs.

GLM-5.1 publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.

GLM-5.1 is the newest, released Apr 7, 2026. GPT-5.4 nano came out Mar 17, 2026. Knowledge cutoff: GPT-5.4 nano 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.