Grok 4.5 vs GLM-5.3

It is close. Our weighted score puts them within a point (GLM-5.3 64/100, Grok 4.5 64/100), so choose by what matters most for your work: GLM-5.3 for raw capability. 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). Grok 4.5 costs $2.00 input / $6.00 output per million tokens (official xAI 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 $3.00 for Grok 4.5 (1.4× as much).

GLM-5.3 scores higher on the Capabilities Index (ECI): GLM-5.3 155.8 (#24 of 148) and Grok 4.5 154.0 (#36 of 148). The confidence ranges of the top two overlap (153.7–158.3 vs 152.3–156.1), so treat the gap as small. On individual benchmarks: GPQA Diamond — Grok 4.5 93.4%, GLM-5.3 90.9%; FrontierMath Tiers 1–3 — GLM-5.3 68.8%, Grok 4.5 57.2%; OTIS Mock AIME 2024–2025 — Grok 4.5 97.8%, GLM-5.3 91.1%; SimpleQA Verified — Grok 4.5 48.3%, GLM-5.3 41.0%.

There are no published SWE-bench Verified results for Grok 4.5 and GLM-5.3 yet, so there is no like-for-like coding score. On overall capability, GLM-5.3 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

GLM-5.3 has the largest context window at 1,000,000 tokens, against 500,000 for Grok 4.5. Maximum output per response: Grok 4.5 up to 500,000, GLM-5.3 up to 131,072 tokens.

Grok 4.5 accepts text and images; GLM-5.3 accepts text. Grok 4.5 handles the widest range of inputs.

GLM-5.3 publishes its weights and can be self-hosted; Grok 4.5 is proprietary.

GLM-5.3 is the newest, released Aug 14, 2026. Grok 4.5 came out Jul 8, 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 Oct 4, 2026.