Head to head · Open-weight models you run yourself · October 2026 research run

MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-9B

MiMo-V2.6 Distill Qwen 9B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons

Which one, for what

MiMo-V2.6 Distill Qwen 9B C

Best 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 34

Watch for

Only BF16 from Xiaomi, about 23 GB to run

Qwen3.5-9B C

Best for Agents that want a small multimodal tool-calling cousin on a laptop or a 16 GB GPU, under a plain licence

Ahead on

  • Reach, 85 against 70

Watch for

No quantised build from Qwen itself

Score by category

CategoryWeight this runMiMo-V2.6 Distill Qwen 9BQwen3.5-9BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%257085Qwen3.5-9B +15
Risk20%206055MiMo-V2.6 Distill Qwen 9B +5
Agent interface10%107070even
Documentation and upkeep10%104034MiMo-V2.6 Distill Qwen 9B +6
Negative events≤1500
TotalC 61/100C 60.5/100

Facts side by side

FactMiMo-V2.6 Distill Qwen 9BQwen3.5-9B
KindOpen-weight modelOpen-weight model
VendorXiaomi (MiMo team)Alibaba (Qwen team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-09-212026-03-02
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno 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 reviews2.3/5 (4)2.5/5 (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.

Qwen3.5-9B

A small dense vision-language cousin with trained tool calling, switchable thinking and a 262,144-token context, for local agent subtasks. Ollama's 4-bit build needs about 8 GB and the BF16 files about 24 GB, in every major runtime. The main risk is neglect, with no file changes since March, no replies from Qwen to reports 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

Qwen3.5-9B

  1. Smallest start is ollama run qwen3.5:9b (6.6 GB, q4_k_m)
  2. On vLLM run vllm serve Qwen/Qwen3.5-9B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, or tool calls come back as text
  3. Tool calls use the XML style <tool_call><function=name><parameter=x>, not Hermes JSON, so use the qwen3_coder parser and not hermes
  4. Thinking is on by default; pass chat_template_kwargs with enable_thinking false for short tasks, since /nothink isn't supported
  5. There's no generation_config.json, so pass sampling yourself, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5 when thinking, or 0.7, 0.8, 20, 1.5 without
  6. Keep at least 128K of context when thinking, and turn on YaRN only beyond 262,144 tokens
  7. Validate JSON output yourself or use the runtime's guided decoding

Questions

Which is better for AI agents, MiMo-V2.6 Distill Qwen 9B or Qwen3.5-9B?

MiMo-V2.6 Distill Qwen 9B and Qwen3.5-9B score within a point of each other on agent readiness, 61 (C) and 60.5 (C). Qwen3.5-9B leads on reach.

Other comparisons with MiMo-V2.6 Distill Qwen 9B or Qwen3.5-9B

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