GPT-5 Nano vs GLM-4.7

GPT-5 Nano is the better all-round choice, scoring 68/100 against GLM-4.7 (55). It leads on price, inputs & features and context window. GLM-4.7 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GPT-5 Nano is cheaper at $0.05 input / $0.40 output per million tokens (official OpenAI API price). GLM-4.7 costs $0.60 input / $2.20 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.138 per million tokens for GPT-5 Nano versus $1.00 for GLM-4.7 (7.3× as much).

GLM-4.7 scores higher on the Capabilities Index (ECI): GLM-4.7 143.7 (#94 of 165) and GPT-5 Nano 139.3 (#116 of 165). The confidence ranges of the top two overlap (141.4–145.8 vs 134.3–141.5), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-4.7 83.3%, GPT-5 Nano 69.4%; OTIS Mock AIME 2024–2025 — GLM-4.7 83.3%, GPT-5 Nano 81.1%; SimpleQA Verified — GLM-4.7 32.2%, GPT-5 Nano 11.7%.

There are no published SWE-bench Verified results for GPT-5 Nano and GLM-4.7 yet, so there is no like-for-like coding score. On overall capability, GLM-4.7 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GPT-5 Nano has the largest context window at 400,000 tokens, against 204,800 for GLM-4.7. Maximum output per response: GPT-5 Nano up to 128,000, GLM-4.7 up to 131,072 tokens.

GPT-5 Nano accepts text and images; GLM-4.7 accepts text. GPT-5 Nano handles the widest range of inputs.

GLM-4.7 publishes its weights and can be self-hosted; GPT-5 Nano is proprietary.

GLM-4.7 is the newest, released Dec 22, 2025. GPT-5 Nano came out Aug 7, 2025. Knowledge cutoff: GPT-5 Nano May 30, 2024, GLM-4.7 Apr 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 Nov 2, 2026.