AutoGLM Phone 9B Multilingual vs GLM-4.6V

It is close. Our weighted score puts them within 2 points (AutoGLM Phone 9B Multilingual 60/100, GLM-4.6V 58/100), so choose by what matters most for your work: AutoGLM Phone 9B Multilingual on price and GLM-4.6V for long inputs. 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). GLM-4.6V costs $0.30 input / $0.90 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 $0.45 for GLM-4.6V (7.4× as much).

There is no independent benchmark that covers both models yet. AutoGLM Phone 9B Multilingual has not been scored yet and GLM-4.6V has not been scored yet.

There are no published SWE-bench Verified results for AutoGLM Phone 9B Multilingual and GLM-4.6V 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-4.6V has the largest context window at 128,000 tokens, against 65,536 for AutoGLM Phone 9B Multilingual. Maximum output per response: AutoGLM Phone 9B Multilingual up to 65,536, GLM-4.6V up to 32,768 tokens.

AutoGLM Phone 9B Multilingual accepts text and images; GLM-4.6V accepts text, images and video. GLM-4.6V handles the widest range of inputs.

Yes, both publish their weights (MIT), so you can self-host them.

AutoGLM Phone 9B Multilingual is the newest, released Dec 10, 2025. GLM-4.6V came out Dec 8, 2025. Knowledge cutoff: GLM-4.6V 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.