Gemini 4 Argon vs GLM-5.3

Gemini 4 Argon is the better all-round choice, scoring 74/100 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). Gemini 4 Argon costs $2.00 input / $10.00 output per million tokens (official Google 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 $4.00 for Gemini 4 Argon (1.9× as much).

Gemini 4 Argon scores higher on the Capabilities Index (ECI): Gemini 4 Argon 166.1 (#7 of 165) and GLM-5.3 156.2 (#33 of 165). Their confidence ranges do not overlap (163.1–170.3 vs 154.3–158.5), so the gap is a real one. On individual benchmarks: GPQA Diamond — Gemini 4 Argon 96.4%, GLM-5.3 90.9%; FrontierMath Tiers 1–3 — Gemini 4 Argon 86.0%, GLM-5.3 68.8%; OTIS Mock AIME 2024–2025 — Gemini 4 Argon 100%, GLM-5.3 91.1%; SimpleQA Verified — Gemini 4 Argon 76.0%, GLM-5.3 41.0%.

There are no published SWE-bench Verified results for Gemini 4 Argon and GLM-5.3 yet, so there is no like-for-like coding score. On overall capability, Gemini 4 Argon leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Gemini 4 Argon has the largest context window at 1,048,576 tokens, against 1,000,000 for GLM-5.3. Maximum output per response: Gemini 4 Argon up to 65,536, GLM-5.3 up to 131,072 tokens.

Gemini 4 Argon accepts text, images, PDFs, audio and video; GLM-5.3 accepts text. Gemini 4 Argon handles the widest range of inputs.

GLM-5.3 publishes its weights and can be self-hosted; Gemini 4 Argon is proprietary.

Gemini 4 Argon is the newest, released Oct 13, 2026. GLM-5.3 came out Aug 14, 2026. Knowledge cutoff: Gemini 4 Argon Mar 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.