Head to head · Open-weight models you run yourself · October 2026 research run
MiMo-V2.6 Distill Qwen 9B vs Step 3.7 Flash
MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories. Step 3.7 Flash leads on agent interface. 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 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 70
- Risk, 65 against 45
Watch for
Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's
Best for Teams with a GPU server or a provider account that want an open, long-context agent model with vision
Ahead on
- Agent interface, 85 against 70
Watch for
About 131 GB to run at 4 bits with images, over a 128 GB machine's share
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 +20
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.
Step 3.7 Flash 45
File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. 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 our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45.
Agent interface Step 3.7 Flash +15
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.
Step 3.7 Flash 85
Chat template 20, chat_template.jinja in the repository (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.
Reach MiMo-V2.6 Distill Qwen 9B +10
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.
Step 3.7 Flash 70
Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70.
Score by category
| Category | Weight this run | MiMo-V2.6 Distill Qwen 9B | Step 3.7 Flash | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 70 | MiMo-V2.6 Distill Qwen 9B +10 |
| Risk | 20%20 | 65 | 45 | MiMo-V2.6 Distill Qwen 9B +20 |
| Agent interface | 10%10 | 70 | 85 | Step 3.7 Flash +15 |
| Documentation and upkeep | 10%10 | 40 | 39 | MiMo-V2.6 Distill Qwen 9B +1 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 59.8/100 |
Facts side by side
| Fact | MiMo-V2.6 Distill Qwen 9B | Step 3.7 Flash |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Xiaomi (MiMo team) | StepFun |
| 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 (metadata and card; no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-09-21 | 2026-06-03 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.3/5 (4) | none |
How the scores have moved
Step 3.7 Flash
No score change since it was listed.
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.
Step 3.7 Flash
A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.
Before you call either
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
Step 3.7 Flash
- On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server
- On vLLM or SGLang pass
--tool-call-parser step3p5 --reasoning-parser step3p5 - The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code
- For llama.cpp load the mmproj file from StepFun's GGUF repository for image input
- StepFun's API has two hosts. Use the one where your key was issued,
api.stepfun.aiorapi.stepfun.com - The same model is on our StepFun API listing, with prices and terms
Questions
Which is better for AI agents, MiMo-V2.6 Distill Qwen 9B or Step 3.7 Flash?
MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories. Step 3.7 Flash leads on agent interface.
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Machine-readable
- This page as Markdown
/compare/mimo-v2-6-distill-qwen-9b-vs-step-3-7-flash.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/mimo-v2-6-distill-qwen-9b.json·/api/v1/tools/step-3-7-flash.json - From a terminal
anchor compare mimo-v2-6-distill-qwen-9b step-3-7-flash(the CLI) - Over MCP
compare_tools {"a": "mimo-v2-6-distill-qwen-9b", "b": "step-3-7-flash"}at/mcp, no key