GLM-5.2 vs Grok 4.7

Grok 4.7 is the better all-round choice, scoring 61/100 against GLM-5.2 (55). It leads on capability and inputs & features. GLM-5.2 wins on price and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%. Capability uses the average of shared benchmarks (GPQA Diamond, FrontierMath Tiers 1–3, OTIS Mock AIME 2024–2025 and SimpleQA Verified), because Grok 4.7 has no Capabilities Index score yet.

GLM-5.2 is cheaper at $1.40 input / $4.40 output per million tokens (official Z.AI API price). Grok 4.7 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-5.2 versus $3.00 for Grok 4.7 (1.4× as much).

Not every model here has a Capabilities Index score, so we compare the average of shared benchmarks (GPQA Diamond, FrontierMath Tiers 1–3, OTIS Mock AIME 2024–2025 and SimpleQA Verified): Grok 4.7 74.9% and GLM-5.2 67.9%. On individual benchmarks: GPQA Diamond — Grok 4.7 92.7%, GLM-5.2 91.9%; FrontierMath Tiers 1–3 — GLM-5.2 59.2%, Grok 4.7 53.0%; OTIS Mock AIME 2024–2025 — Grok 4.7 98.1%, GLM-5.2 86.4%; SimpleQA Verified — Grok 4.7 56.0%, GLM-5.2 34.2%.

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

GLM-5.2 has the largest context window at 1,000,000 tokens, against 500,000 for Grok 4.7. Maximum output per response: GLM-5.2 up to 131,072, Grok 4.7 up to 500,000 tokens.

GLM-5.2 accepts text; Grok 4.7 accepts text, images and PDFs. Grok 4.7 handles the widest range of inputs.

GLM-5.2 publishes its weights and can be self-hosted; Grok 4.7 is proprietary.

Grok 4.7 is the newest, released Sep 21, 2026. GLM-5.2 came out Jun 13, 2026. Knowledge cutoff: Grok 4.7 May 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.