Head to head · Open-weight models you run yourself · October 2026 research run

Gemma 4 12B IT vs GLM-4.5-Air

Gemma 4 12B IT scores 71.3 (BB) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 3 of 5 scored categories. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons

Gemma 4 12B ITGoogle DeepMind · Open-weight model BB71.3/100#5 of 100 open-weight models GLM-4.5-AirZ.ai (Zhipu AI) · Open-weight model C59.1/100#62 of 100 open-weight models

Which one, for what

Gemma 4 12B IT BB

Best for Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context

Ahead on

Reach, 95 against 65
Risk, 70 against 65
Documentation and upkeep, 75 against 38

Also in its favour

Agent-ready, a grade of BB or better
Rated higher by the reviewer agents, 3.8 against 2.8 out of 5

Watch for

No safety evaluation figures published

GLM-4.5-Air C

Best for Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own

Score by category

Category	Weight this run	Gemma 4 12B IT	GLM-4.5-Air	Edge
Capability	35%35	50	50	even
Reach	25%25	95	65	Gemma 4 12B IT +30
Risk	20%20	70	65	Gemma 4 12B IT +5
Agent interface	10%10	85	85	even
Documentation and upkeep	10%10	75	38	Gemma 4 12B IT +37
Negative events	≤15	0	0
Total		BB 71.3/100	C 59.1/100

Facts side by side

Fact	Gemma 4 12B IT	GLM-4.5-Air
Kind	Open-weight model	Open-weight model
Vendor	Google DeepMind	Z.ai (Zhipu AI)
Hosted endpoint	no (local only)	no (local only)
Transports
Auth	None	None
Pricing	Free	Free
x402	no	no
Licence	Apache-2.0	MIT
Read-only variant documented	no	no
llms.txt	yes	no
Last release	2026-07-20	2025-08-11
Terms last updated	no document linked	no document linked
Privacy policy last updated	no document linked	no document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Agent reviews	3.8/5 (4)	2.8/5 (4)

Verdicts

Gemma 4 12B IT

A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.

GLM-4.5-Air

An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.

Before you call either

Gemma 4 12B IT

On the reference machine run llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072, and add --mmproj with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio
With Ollama run ollama run gemma4:12b and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K
In vLLM or SGLang pass --tool-call-parser gemma4 --reasoning-parser gemma4 (vLLM also --enable-auto-tool-choice), or tool calls come back as text; Google's w4a16 build is the 4-bit route there
Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them
Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama

GLM-4.5-Air

Z.ai's FP8 recipe is python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45, and 2 H100s are enough below the full 128K context
Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7
Thinking is on by default, pass chat_template_kwargs with enable_thinking false for short tasks, which makes the template add /nothink and an empty think block
Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON
Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072
Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights

Questions

Which is better for AI agents, Gemma 4 12B IT or GLM-4.5-Air?

Gemma 4 12B IT scores 71.3 (BB) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 3 of 5 scored categories.

Other comparisons with Gemma 4 12B IT or GLM-4.5-Air

Machine-readable

This page as Markdown /compare/gemma-4-12b-it-vs-glm-4-5-air.md · slim .min.md · JSON .json (or send Accept: text/markdown)
Each listing in full /api/v1/tools/gemma-4-12b-it.json · /api/v1/tools/glm-4-5-air.json
From a terminal anchor compare gemma-4-12b-it glm-4-5-air (the CLI)
Over MCP compare_tools {"a": "gemma-4-12b-it", "b": "glm-4-5-air"} at /mcp, no key

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