# Gemma 4 12B IT vs GLM-5.3-Flash

> Gemma 4 12B IT scores 71.3 (BB) to GLM-5.3-Flash's 65.1 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash
- Markdown: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.md (~6,150 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Gemma 4 12B IT scores 71.3 (BB) on agent readiness against GLM-5.3-Flash's 65.1 (B), and leads in 4 of 5 scored categories. GLM-5.3-Flash leads on capability. Both do open-weight models you run yourself.

- Gemma 4 12B IT: grade BB, 71.3/100, rank #5 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-12b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json
- GLM-5.3-Flash: grade B, 65.1/100, rank #16 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Gemma 4 12B IT (BB)

Good 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 60
- Risk, 70 against 55
- Agent interface, 85 against 70
- Documentation and upkeep, 75 against 58

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-5.3-Flash (B)

Good for: Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider

Ahead on:
- Capability, 75 against 50

Watch for: Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists


## Score by category

| Category | Weight | Gemma 4 12B IT | GLM-5.3-Flash | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 50 | 75 | GLM-5.3-Flash +25 |
| Reach | 25% (25 this run) | 95 | 60 | Gemma 4 12B IT +35 |
| Risk | 20% (20 this run) | 70 | 55 | Gemma 4 12B IT +15 |
| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 12B IT +15 |
| Documentation and upkeep | 10% (10 this run) | 75 | 58 | Gemma 4 12B IT +17 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **71.3 · BB** | **65.1 · B** | |

## Facts side by side

| Fact | Gemma 4 12B IT | GLM-5.3-Flash |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Z.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 | 2026-08-26 |
| 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-5.3-Flash.** An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.

## Before you call either

### Gemma 4 12B IT

1. 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
2. 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
3. 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
4. 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
5. 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
6. 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-5.3-Flash

1. Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)
2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs
3. Pass `chat_template_kwargs: {"reasoning_effort": "low"}` for short tasks; anything other than low or high falls back to max
4. Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block
5. Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt
6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai

## Questions

### Which is better for AI agents, Gemma 4 12B IT or GLM-5.3-Flash?

Gemma 4 12B IT scores 71.3 (BB) on agent readiness against GLM-5.3-Flash's 65.1 (B), and leads in 4 of 5 scored categories. GLM-5.3-Flash leads on capability.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-12b-it", "b": "glm-5-3-flash"}`. From a terminal: `anchor compare gemma-4-12b-it glm-5-3-flash`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json and https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json

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