GLM-5.3 vs GPT-5.6 Sol

It is close. Our weighted score puts them within 3 points (GPT-5.6 Sol 66/100, GLM-5.3 63/100), so choose by what matters most for your work: GPT-5.6 Sol for raw capability and GLM-5.3 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

GLM-5.3 is cheaper at $1.40 input / $4.40 output per million tokens (official Z.AI API price). GPT-5.6 Sol costs $4.00 input / $20.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $2.15 per million tokens for GLM-5.3 versus $8.00 for GPT-5.6 Sol (3.7× as much).

GPT-5.6 Sol scores higher on the Capabilities Index (ECI): GPT-5.6 Sol 162.6 (#12 of 165) and GLM-5.3 156.2 (#33 of 165). Their confidence ranges do not overlap (159.6–166.2 vs 154.3–158.5), so the gap is a real one. On individual benchmarks: GPQA Diamond — GPT-5.6 Sol 93.5%, GLM-5.3 90.9%; FrontierMath Tiers 1–3 — GPT-5.6 Sol 89.1%, GLM-5.3 68.8%; OTIS Mock AIME 2024–2025 — GPT-5.6 Sol 100%, GLM-5.3 91.1%; SimpleQA Verified — GPT-5.6 Sol 69.7%, GLM-5.3 41.0%.

There are no published SWE-bench Verified results for GLM-5.3 and GPT-5.6 Sol yet, so there is no like-for-like coding score. On overall capability, GPT-5.6 Sol leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GPT-5.6 Sol 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-5.6 Sol up to 128,000 tokens.

GLM-5.3 accepts text; GPT-5.6 Sol accepts text, images and PDFs. GPT-5.6 Sol handles the widest range of inputs.

GLM-5.3 publishes its weights and can be self-hosted; GPT-5.6 Sol is proprietary.

GLM-5.3 is the newest, released Aug 14, 2026. GPT-5.6 Sol came out Jul 9, 2026. Knowledge cutoff: GPT-5.6 Sol Feb 16, 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.