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

MiMo-V2.6 Distill Qwen 9B vs ZAYA1-8B

MiMo-V2.6 Distill Qwen 9B and ZAYA1-8B score within a point of each other on agent readiness, 61 (C) and 61 (C). ZAYA1-8B 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

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 to run at 4-bit through Ollama in about 8 GB.

Ahead on

  • Risk, 65 against 55
  • Agent interface, 70 against 60

Watch for

Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's

ZAYA1-8B C

Best for People who want a small, fast reasoning model for maths and code and can live without tool calling

Ahead on

  • Documentation and upkeep, 50 against 40

Watch for

Tool calling needs a parser that only Zyphra's vLLM fork has

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.

Risk MiMo-V2.6 Distill Qwen 9B +10

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.

ZAYA1-8B 55

File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the Zyphra organisation, which the card ties to zyphra.com's blog post for the model (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and www.zyphra.com has no security.txt. 25 + 15 + 0 + 0 + 15 + 0 = 55.

Agent interface MiMo-V2.6 Distill Qwen 9B +10

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.

ZAYA1-8B 60

Chat template 20, chat_template.jinja in the repository (10,700 characters). Tool-calling format 10, only the template and a fork say it. Trained tool calling 10, the template takes tools and renders calls, and the card recommends settings for agent use, but it doesn't say the model was trained for tool calling or for several calls in a turn, so the lower band. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch, and the card names the qwen3 reasoning parser, which returns the reasoning apart from the answer. Context 10, 131,072 positions in config.json, for an architecture Zyphra trained end to end, so the config is the model's own. 20 + 10 + 10 + 0 + 10 + 10 = 60.

Documentation and upkeep ZAYA1-8B +10

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.

ZAYA1-8B 50

Model card 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50.

Score by category

CategoryWeight this runMiMo-V2.6 Distill Qwen 9BZAYA1-8BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258080even
Risk20%206555MiMo-V2.6 Distill Qwen 9B +10
Agent interface10%107060MiMo-V2.6 Distill Qwen 9B +10
Documentation and upkeep10%104050ZAYA1-8B +10
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

FactMiMo-V2.6 Distill Qwen 9BZAYA1-8B
KindOpen-weight modelOpen-weight model
VendorXiaomi (MiMo team)Zyphra
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 release2026-09-212026-07-02
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews2.3/5 (4)none

How the scores have moved

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. 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.

ZAYA1-8B

A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.

Before you call either

MiMo-V2.6 Distill Qwen 9B

  1. Quickest start is ollama run mimo-v2.6:9b, Ollama's 6.6 GB q4_K_M build with text and image input
  2. 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
  3. 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
  4. Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read
  5. The generation config samples at temperature 0.6, top_k 20 and top_p 0.95
  6. Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them
  7. The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer
  8. It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens
  9. 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

ZAYA1-8B

  1. Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork
  2. Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in
  3. Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work
  4. Download Zyphra/ZAYA1-8B, not the legacy repository, for the released runtimes
  5. The quantised repositories have no licence tag. The main one is Apache 2.0

Questions

Which is better for AI agents, MiMo-V2.6 Distill Qwen 9B or ZAYA1-8B?

MiMo-V2.6 Distill Qwen 9B and ZAYA1-8B score within a point of each other on agent readiness, 61 (C) and 61 (C). ZAYA1-8B leads on documentation and upkeep.

Other comparisons with MiMo-V2.6 Distill Qwen 9B or ZAYA1-8B

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