Head to head · Open-weight models you run yourself · October 2026 research run
Kimi K3.1 Pro vs MiMo-V2.6 Distill Qwen 9B
MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best for Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API
Ahead on
- Documentation and upkeep, 51 against 40
Watch for
1,561 GB of files, so it needs a node of eight large accelerators at least
Best for Researchers and agents that want a small coding and terminal-agent model to fine-tune further on a 24 GB GPU, or to run at 4-bit through Ollama in about 8 GB.
Ahead on
- Reach, 80 against 40
- Risk, 65 against 50
- Agent interface, 70 against 65
Watch for
Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach MiMo-V2.6 Distill Qwen 9B +40
Kimi K3.1 Pro 40
Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40.
MiMo-V2.6 Distill Qwen 9B 80
Memory 30 of 30, Ollama's library build at q4_K_M is 6.6 GB, 7.9 GB with a fifth added, while Xiaomi's only build is BF16 at 18.85 GB. Runtimes 25, it keeps Qwen3.5-9B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, and Ollama's own library carries this model (entry updated 3 November 2026; the 21 October check found none). Builds 0, Xiaomi publishes no quantised build. Terms 15, MIT on the card over an Apache 2.0 base, both with nothing added. Download 10, not gated. Hosted 0, no provider serves this distill.
Risk MiMo-V2.6 Distill Qwen 9B +15
Kimi K3.1 Pro 50
File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50.
MiMo-V2.6 Distill Qwen 9B 65
File safety 25, safetensors only and no repository code runs. Provenance 15, the files are in Xiaomi's MiMo organisation, which mimo.xiaomi.com links to, with a checksum on every file. Safety evaluation 0, the technical report has no safety results. Agent guidance 5, the card's Use Restrictions section (28 October 2026) gives general cautions, with nothing on prompt injection, refusals or safeguards. Licence risk 15, MIT and Apache 2.0 with no conditions, and the card says its restrictions add none. Security programme 5 of 10, Xiaomi's company security centre takes reports, but nothing links it to MiMo and no advisories were found.
Documentation and upkeep Kimi K3.1 Pro +11
Kimi K3.1 Pro 51
Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51.
MiMo-V2.6 Distill Qwen 9B 40
Model card 5 of 20, intended use and parameters only. Training data 5, a domain mix with token counts but no sources or cut-off. Last change 20, README updates on 28 October 2026. Responsiveness 0, Xiaomi answered none of the 14 discussions. Version policy 5, a first release. File health 5, no LICENSE file, a config that lists an MTP layer the weights lack, and template and tool-call reports waiting without a reply.
Agent interface MiMo-V2.6 Distill Qwen 9B +5
Kimi K3.1 Pro 65
Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65.
MiMo-V2.6 Distill Qwen 9B 70
Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format is only in the template and the card names no tool parser. Trained tool calling 20, several calls a turn and agentic fine-tuning. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 262,144 tokens from the base's native length, though the card doesn't say.
Score by category
| Category | Weight this run | Kimi K3.1 Pro | MiMo-V2.6 Distill Qwen 9B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 40 | 80 | MiMo-V2.6 Distill Qwen 9B +40 |
| Risk | 20%20 | 50 | 65 | MiMo-V2.6 Distill Qwen 9B +15 |
| Agent interface | 10%10 | 65 | 70 | MiMo-V2.6 Distill Qwen 9B +5 |
| Documentation and upkeep | 10%10 | 51 | 40 | Kimi K3.1 Pro +11 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 48.6/100 | C 61/100 |
Facts side by side
| Fact | Kimi K3.1 Pro | MiMo-V2.6 Distill Qwen 9B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Moonshot AI | Xiaomi (MiMo team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Kimi K3.1 Pro License (modified 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 | 2026-11-05 | 2026-09-21 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.3/5 (4) |
How the scores have moved
Kimi K3.1 Pro
No score change since it was listed.
Verdicts
Kimi K3.1 Pro
Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.
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. Ollama's library build needs about 8 GB at 4-bit, while Xiaomi ships only BF16. 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
Kimi K3.1 Pro
- Use Moonshot's API or Together unless you have a node of eight B300-class accelerators
- With vLLM use 0.34.0 or later and the recipe's flags,
--tool-call-parser kimi_k3 --reasoning-parser kimi_k3 - Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires
- Set
reasoning_effortto low, high or max per request. Max is the default and thinking can't be turned off - Kimi K3's model code doesn't load it, because the attention layers changed
- Validate JSON output yourself, since the card doesn't document structured output for the weights
MiMo-V2.6 Distill Qwen 9B
- Quickest start is
ollama run mimo-v2.6:9b, Ollama's 6.6 GB q4_K_M build with text and image input - 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 mimofor parsed tool calls - SGLang's mimo reasoning parser expects thinking to be asked for, so pass
chat_template_kwargswithenable_thinkingtrue, as the card does, and readreasoning_content - Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read
- The generation config samples at temperature 0.6, top_k 20 and top_p 0.95
- Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them
- The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer
- It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens
- If you redistribute the weights, include the Apache 2.0 text of Qwen3.5-9B, which section 4(a) asks for and the repository lacks
Questions
Which is better for AI agents, Kimi K3.1 Pro or MiMo-V2.6 Distill Qwen 9B?
MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on documentation and upkeep.
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Machine-readable
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anchor compare kimi-k3-1-pro mimo-v2-6-distill-qwen-9b(the CLI) - Over MCP
compare_tools {"a": "kimi-k3-1-pro", "b": "mimo-v2-6-distill-qwen-9b"}at/mcp, no key