# GLM-4.5-Air vs MiMo-V2.6 Distill Qwen 9B

> MiMo-V2.6 Distill Qwen 9B scores 61 (C) to GLM-4.5-Air's 59.1 (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-4-5-air-vs-mimo-v2-6-distill-qwen-9b
- Markdown: https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-distill-qwen-9b.md (~6,500 tokens)
- Slim: https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-distill-qwen-9b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-distill-qwen-9b.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

MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 4 scored categories. GLM-4.5-Air leads on risk and agent interface. Both do open-weight models you run yourself.

- GLM-4.5-Air: grade C, 59.1/100, rank #62 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-5-air.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json
- MiMo-V2.6 Distill Qwen 9B: grade C, 61/100, rank #41 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.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-4.5-Air (C)

Good 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.

Ahead on:
- Risk, 65 against 60
- Agent interface, 85 against 70

Also in its favour:
- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5

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

### MiMo-V2.6 Distill Qwen 9B (C)

Good for: Researchers and agents that want a small coding and terminal-agent model to fine-tune further, on a 24 GB GPU or larger.

Ahead on:
- Reach, 70 against 65

Watch for: Only BF16 from Xiaomi, about 23 GB to run


## Score by category

| Category | Weight | GLM-4.5-Air | MiMo-V2.6 Distill Qwen 9B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 70 | MiMo-V2.6 Distill Qwen 9B +5 |
| Risk | 20% (20 this run) | 65 | 60 | GLM-4.5-Air +5 |
| Agent interface | 10% (10 this run) | 85 | 70 | GLM-4.5-Air +15 |
| Documentation and upkeep | 10% (10 this run) | 38 | 40 | MiMo-V2.6 Distill Qwen 9B +2 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.1 · C** | **61 · C** | |

## Facts side by side

| Fact | GLM-4.5-Air | MiMo-V2.6 Distill Qwen 9B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | Xiaomi (MiMo team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-08-11 | 2026-09-21 |
| 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) | 2.3/5 (4) |

## Verdicts

**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.

**MiMo-V2.6 Distill Qwen 9B.** A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. It needs about 23 GB in BF16, since Xiaomi ships no smaller build. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports.

## Before you call either

### GLM-4.5-Air

1. 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
2. Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7
3. 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
4. Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON
5. Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072
6. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights

### MiMo-V2.6 Distill Qwen 9B

1. Serve it as the card does, `sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo`, and add `--tool-call-parser mimo` for parsed tool calls
2. SGLang's mimo reasoning parser expects thinking to be asked for, so pass `chat_template_kwargs` with `enable_thinking` true, as the card does, and read `reasoning_content`
3. Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read
4. The generation config samples at temperature 0.6, top_k 20 and top_p 0.95
5. Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them
6. The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer
7. It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens

## Questions

### Which is better for AI agents, GLM-4.5-Air or MiMo-V2.6 Distill Qwen 9B?

MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 4 scored categories. GLM-4.5-Air leads on risk and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-distill-qwen-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-distill-qwen-9b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "glm-4-5-air", "b": "mimo-v2-6-distill-qwen-9b"}`. From a terminal: `anchor compare glm-4-5-air mimo-v2-6-distill-qwen-9b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json and https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.json

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