GLM-5.3 vs GPT-6.1 Terra

GPT-6.1 Terra is the better all-round choice, scoring 76/100 against GLM-5.3 (63). It leads on capability and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GPT-6.1 Terra is cheaper at $1.00 input / $5.00 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 $2.00 per million tokens for GPT-6.1 Terra versus $2.15 for GLM-5.3 (1.1× as much).

GPT-6.1 Terra scores higher on the Capabilities Index (ECI): GPT-6.1 Terra 168.1 (#3 of 165) and GLM-5.3 156.2 (#33 of 165). Their confidence ranges do not overlap (164.9–172.9 vs 154.3–158.5), so the gap is a real one. On individual benchmarks: GPQA Diamond — GPT-6.1 Terra 94.3%, GLM-5.3 90.9%; FrontierMath Tiers 1–3 — GPT-6.1 Terra 90.9%, GLM-5.3 68.8%; OTIS Mock AIME 2024–2025 — GPT-6.1 Terra 100%, GLM-5.3 91.1%; SimpleQA Verified — GPT-6.1 Terra 57.3%, GLM-5.3 41.0%.

There are no published SWE-bench Verified results for GLM-5.3 and GPT-6.1 Terra yet, so there is no like-for-like coding score. On overall capability, GPT-6.1 Terra leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GPT-6.1 Terra has the largest context window at 1,050,000 tokens, against 1,000,000 for GLM-5.3. Maximum output per response: GLM-5.3 up to 131,072, GPT-6.1 Terra up to 128,000 tokens.

GLM-5.3 accepts text; GPT-6.1 Terra accepts text, images and PDFs. GPT-6.1 Terra handles the widest range of inputs.

GLM-5.3 publishes its weights and can be self-hosted; GPT-6.1 Terra is proprietary.

GPT-6.1 Terra is the newest, released Oct 26, 2026. GLM-5.3 came out Aug 14, 2026. Knowledge cutoff: GPT-6.1 Terra Jun 1, 2026.

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.