AutoGLM Phone 9B Multilingual vs GLM-4.7 vs Trinity Mini

Trinity Mini is the better all-round choice, scoring 65/100 against AutoGLM Phone 9B Multilingual (60) and GLM-4.7 (41). GLM-4.7 wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

AutoGLM Phone 9B Multilingual is cheaper at $0.035 input / $0.138 output per million tokens (median across 2 API providers). Trinity Mini costs $0.045 input / $0.15 output per million tokens (median across 1 API provider); GLM-4.7 costs $0.60 input / $2.20 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.061 per million tokens for AutoGLM Phone 9B Multilingual versus $0.071 for Trinity Mini (1.2× as much) and $1.00 for GLM-4.7 (16× as much).

There is no independent benchmark that covers all three models yet. AutoGLM Phone 9B Multilingual has not been scored yet, GLM-4.7 has an ECI of 143.7 and Trinity Mini has not been scored yet.

There are no published SWE-bench Verified results for AutoGLM Phone 9B Multilingual, GLM-4.7 and Trinity Mini yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that AutoGLM Phone 9B Multilingual does not support tool calling, which most coding agents need.

GLM-4.7 has the largest context window at 204,800 tokens, against 131,072 for Trinity Mini and 65,536 for AutoGLM Phone 9B Multilingual. Maximum output per response: AutoGLM Phone 9B Multilingual up to 65,536, GLM-4.7 up to 131,072, Trinity Mini up to 131,072 tokens.

AutoGLM Phone 9B Multilingual accepts text and images; GLM-4.7 accepts text; Trinity Mini accepts text. AutoGLM Phone 9B Multilingual handles the widest range of inputs.

Yes, all three publish their weights (MIT and OpenMDW-1.1), so you can self-host them.

GLM-4.7 is the newest, released Dec 22, 2025. AutoGLM Phone 9B Multilingual came out Dec 10, 2025; Trinity Mini came out Dec 1, 2025. Knowledge cutoff: GLM-4.7 Apr 2025.

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.