Muse Wonder vs GLM-5.3

Muse Wonder is the better all-round choice, scoring 68/100 against GLM-5.3 (63). It leads on capability and inputs & features. GLM-5.3 wins 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). Muse Wonder costs $4.00 input / $20.00 output per million tokens (official Meta 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 Muse Wonder (3.7× as much).

Muse Wonder scores higher on the Capabilities Index (ECI): Muse Wonder 161.7 (#13 of 165) and GLM-5.3 156.2 (#33 of 165). Their confidence ranges do not overlap (159.2–164.6 vs 154.3–158.5), so the gap is a real one. On individual benchmarks: FrontierMath Tiers 1–3 — Muse Wonder 82.1%, GLM-5.3 68.8%; OTIS Mock AIME 2024–2025 — Muse Wonder 99.4%, GLM-5.3 91.1%.

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

Muse Wonder has the largest context window at 1,048,576 tokens, against 1,000,000 for GLM-5.3. Maximum output per response: Muse Wonder up to 131,072, GLM-5.3 up to 131,072 tokens.

Muse Wonder accepts text, images, PDFs, audio and video; GLM-5.3 accepts text. Muse Wonder handles the widest range of inputs.

GLM-5.3 publishes its weights and can be self-hosted; Muse Wonder is proprietary.

Muse Wonder is the newest, released Oct 12, 2026. GLM-5.3 came out Aug 14, 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.