# Gemma 4 E4B IT vs GLM-5.3

> GLM-5.3 scores 64.6 (B) to Gemma 4 E4B IT's 63.2 (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-e4b-it-vs-glm-5-3
- Markdown: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3.md (~5,950 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3.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

GLM-5.3 scores 64.6 (B) on agent readiness against Gemma 4 E4B IT's 63.2 (B), and leads in 1 of 5 scored categories. Gemma 4 E4B IT leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Gemma 4 E4B IT: grade B, 63.2/100, rank #24 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-e4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json
- GLM-5.3: grade B, 64.6/100, rank #17 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3.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 E4B IT (B)

Good for: Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning

Ahead on:
- Reach, 95 against 55
- Risk, 70 against 50
- Agent interface, 85 against 70
- Documentation and upkeep, 75 against 58

Also in its favour:
- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5

Watch for: No safety evaluation figures published

### GLM-5.3 (B)

Good for: Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider

Ahead on:
- Capability, 80 against 27

Watch for: Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build


## Score by category

| Category | Weight | Gemma 4 E4B IT | GLM-5.3 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 27 | 80 | GLM-5.3 +53 |
| Reach | 25% (25 this run) | 95 | 55 | Gemma 4 E4B IT +40 |
| Risk | 20% (20 this run) | 70 | 50 | Gemma 4 E4B IT +20 |
| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 E4B IT +15 |
| Documentation and upkeep | 10% (10 this run) | 75 | 58 | Gemma 4 E4B IT +17 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63.2 · B** | **64.6 · B** | |

## Facts side by side

| Fact | Gemma 4 E4B IT | GLM-5.3 |
| --- | --- | --- |
| 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 | GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators) |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-20 | 2026-10-14 |
| 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 | 3.5/5 (4) | 2.8/5 (4) |

## Verdicts

**Gemma 4 E4B IT.** A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 7 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.

**GLM-5.3.** A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.

## Before you call either

### Gemma 4 E4B IT

1. On the reference machine run `llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E4B-it-mmproj.gguf for images and audio
2. With Ollama run `ollama run gemma4:e4b` 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

### GLM-5.3

1. Start it with `vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3` (vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe)
2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text
3. Pass `chat_template_kwargs: {"reasoning_effort": "low"}` for short tasks; anything other than low or high falls back to max
4. Pass `clear_thinking: true` in chat use, since the template keeps earlier reasoning by default
5. Don't send `enable_thinking: false`; the template ignores it and always opens a think block
6. Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API
7. No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe

## Questions

### Which is better for AI agents, Gemma 4 E4B IT or GLM-5.3?

GLM-5.3 scores 64.6 (B) on agent readiness against Gemma 4 E4B IT's 63.2 (B), and leads in 1 of 5 scored categories. Gemma 4 E4B IT leads on reach, risk, agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-e4b-it", "b": "glm-5-3"}`. From a terminal: `anchor compare gemma-4-e4b-it glm-5-3`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json and https://www.anchorterminal.com/api/v1/tools/glm-5-3.json

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- [GLM-5.3 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-5-3-vs-swift-1-5-qwen3-8-flash-next.md)
- [GLM-5.3 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-5-3-vs-thinkingcap-qwen3-8-27b.md)

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