GPT-5 Nano vs GLM-4.7 vs Qwen3 30B-A3B Instruct 2507

GPT-5 Nano is the better all-round choice, scoring 68/100 against Qwen3 30B-A3B Instruct 2507 (59) and 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). Qwen3 30B-A3B Instruct 2507 costs $0.10 input / $0.30 output per million tokens (median across 12 API providers); 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 $0.15 for Qwen3 30B-A3B Instruct 2507 (1.1× as much) and $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), GPT-5 Nano 139.3 (#116 of 165) and Qwen3 30B-A3B Instruct 2507 137.3 (#124 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%, Qwen3 30B-A3B Instruct 2507 55.6%; OTIS Mock AIME 2024–2025 — GLM-4.7 83.3%, GPT-5 Nano 81.1%, Qwen3 30B-A3B Instruct 2507 62.2%.

There are no published SWE-bench Verified results for GPT-5 Nano, GLM-4.7 and Qwen3 30B-A3B Instruct 2507 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. All three support tool calling for agent workflows.

GPT-5 Nano has the largest context window at 400,000 tokens, against 262,144 for Qwen3 30B-A3B Instruct 2507 and 204,800 for GLM-4.7. Maximum output per response: GPT-5 Nano up to 128,000, GLM-4.7 up to 131,072, Qwen3 30B-A3B Instruct 2507 up to 16,384 tokens.

GPT-5 Nano accepts text and images; GLM-4.7 accepts text; Qwen3 30B-A3B Instruct 2507 accepts text. GPT-5 Nano handles the widest range of inputs.

GLM-4.7 and Qwen3 30B-A3B Instruct 2507 publishes its weights (Apache 2.0) 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; Qwen3 30B-A3B Instruct 2507 came out Jul 28, 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.