# MiMo-V2.6 Distill Qwen 9B vs Qwen3-4B-Instruct-2507

> MiMo-V2.6 Distill Qwen 9B scores 61 (C) to Qwen3-4B-Instruct-2507's 55.7 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-4b-instruct-2507
- Markdown: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-4b-instruct-2507.md (~6,900 tokens)
- Slim: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-4b-instruct-2507.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-4b-instruct-2507.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 Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 2 of 4 scored categories. Qwen3-4B-Instruct-2507 leads on reach and agent interface. Both do open-weight models you run yourself.

- 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
- Qwen3-4B-Instruct-2507: grade C, 55.7/100, rank #77 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-4b-instruct-2507.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

### 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:
- Risk, 60 against 55
- Documentation and upkeep, 40 against 26

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

### Qwen3-4B-Instruct-2507 (C)

Good for: Agents that want a capable small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context.

Ahead on:
- Reach, 95 against 70
- Agent interface, 75 against 70

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.3 out of 5

Watch for: No safety results, prompt-injection guidance or security contact


## Score by category

| Category | Weight | MiMo-V2.6 Distill Qwen 9B | Qwen3-4B-Instruct-2507 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 70 | 95 | Qwen3-4B-Instruct-2507 +25 |
| Risk | 20% (20 this run) | 60 | 55 | MiMo-V2.6 Distill Qwen 9B +5 |
| Agent interface | 10% (10 this run) | 70 | 75 | Qwen3-4B-Instruct-2507 +5 |
| Documentation and upkeep | 10% (10 this run) | 40 | 26 | MiMo-V2.6 Distill Qwen 9B +14 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **55.7 · C** | |

## Facts side by side

| Fact | MiMo-V2.6 Distill Qwen 9B | Qwen3-4B-Instruct-2507 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Xiaomi (MiMo team) | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-09-21 | 2025-09-17 |
| 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 | 2.3/5 (4) | 3/5 (4) |

## Verdicts

**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.

**Qwen3-4B-Instruct-2507.** A small, fast cousin for instruction following, short tool-calling jobs and long documents on modest hardware, with no thinking step. It runs in about 3 GB at 4 bits on every common runtime, and two providers host it. The main risk is neglect, with no file changes in over a year, no answers from Alibaba and no safety results.

## Before you call either

### 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

### Qwen3-4B-Instruct-2507

1. Quickest start is `ollama run qwen3:4b-instruct`, 2.5 GB. Plain `qwen3:4b` is the Thinking-2507 edition, not this one
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes`
3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
6. Drop the context to 32,768 tokens if memory runs out, as the card suggests
7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, MiMo-V2.6 Distill Qwen 9B or Qwen3-4B-Instruct-2507?

MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 2 of 4 scored categories. Qwen3-4B-Instruct-2507 leads on reach and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-4b-instruct-2507.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-qwen3-4b-instruct-2507.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "mimo-v2-6-distill-qwen-9b", "b": "qwen3-4b-instruct-2507"}`. From a terminal: `anchor compare mimo-v2-6-distill-qwen-9b qwen3-4b-instruct-2507`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-4b-instruct-2507.json

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- [Ornith-1.5-35B-A3B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-4b-instruct-2507.md)
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