# GLM-4.7-Flash vs Granite 4.2 3B

> GLM-4.7-Flash scores 63 (B) to Granite 4.2 3B's 59.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-3b
- Markdown: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-3b.md (~6,150 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-3b.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-4.7-Flash scores 63 (B) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 1 of 5 scored categories. Granite 4.2 3B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.

- GLM-4.7-Flash: grade B, 63/100, rank #26 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json
- Granite 4.2 3B: grade C, 59.8/100, rank #57 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.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

### GLM-4.7-Flash (B)

Good for: Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime

Ahead on:
- Capability, 59 against 21

Watch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January

### Granite 4.2 3B (C)

Good for: Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off

Ahead on:
- Reach, 95 against 80
- Documentation and upkeep, 92 against 28

Also in its favour:
- Rated higher by the reviewer agents, 3.8 against 2.8 out of 5

Watch for: No safety results published for 4.2, and only general safety cautions


## Score by category

| Category | Weight | GLM-4.7-Flash | Granite 4.2 3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 59 | 21 | GLM-4.7-Flash +38 |
| Reach | 25% (25 this run) | 80 | 95 | Granite 4.2 3B +15 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 85 | 85 | even |
| Documentation and upkeep | 10% (10 this run) | 28 | 92 | Granite 4.2 3B +64 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63 · B** | **59.8 · C** | |

## Facts side by side

| Fact | GLM-4.7-Flash | Granite 4.2 3B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | IBM |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-01-29 | 2026-10-20 |
| 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 | 2.8/5 (4) | 3.8/5 (4) |

## Verdicts

**GLM-4.7-Flash.** A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.

**Granite 4.2 3B.** A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.

## Before you call either

### GLM-4.7-Flash

1. On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`
2. Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build
3. Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text
4. Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026
5. Pass `chat_template_kwargs: {"enable_thinking": false}` for short tasks, since thinking is on by default
6. Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench
7. Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000

### Granite 4.2 3B

1. Start it with `ollama run granite4.2:3b`; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072
2. With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice`; that release has the parser built in, so the card's `--reasoning-parser-plugin ./granite_thinking_parser.py` isn't needed
3. With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content
4. Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires
5. Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server
6. Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history
7. For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2
8. Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide

## Questions

### Which is better for AI agents, GLM-4.7-Flash or Granite 4.2 3B?

GLM-4.7-Flash scores 63 (B) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 1 of 5 scored categories. Granite 4.2 3B leads on reach and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-granite-4-2-3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-7-flash", "b": "granite-4-2-3b"}`. From a terminal: `anchor compare glm-4-7-flash granite-4-2-3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json and https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json

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- [Granite 4.2 3B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-super-120b-a12b.md)
- [Granite 4.2 3B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-ultra-550b-a55b.md)
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- [Granite 4.2 3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-397b.md)
- [Granite 4.2 3B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-9b.md)
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- [Granite 4.2 3B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-0-8b.md)
- [Granite 4.2 3B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-122b-a10b.md)
- [Granite 4.2 3B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-2b.md)
- [Granite 4.2 3B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-5-397b-a17b.md)
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- [Granite 4.2 3B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-6-27b.md)
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- [Granite 4.2 3B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-8-27b.md)
- [Granite 4.2 3B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-8-flash-next.md)
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- [Granite 4.2 3B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-2b-instruct.md)
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- [Granite 4.2 3B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-32b-instruct.md)
- [Granite 4.2 3B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-4b-instruct.md)
- [Granite 4.2 3B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-vl-8b-instruct.md)
- [Granite 4.2 3B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwopus3-8-27b-flash-v2.md)
- [Granite 4.2 3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-smollm3-3b.md)
- [Granite 4.2 3B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-spark-x2-6-4b.md)
- [Granite 4.2 3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-27b.md)
- [Granite 4.2 3B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Granite 4.2 3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-thinkingcap-qwen3-8-27b.md)

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