Head to head · Open-weight models you run yourself · October 2026 research run
MiMo-V2.6-Flash-RL vs Qwen3-Omni-30B-A3B-Instruct
MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 3 of 4 scored categories. Qwen3-Omni-30B-A3B-Instruct leads on risk. 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
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Which one, for what
Best for Teams with a GPU server, or a provider account, that want an open agent model with a very long context
Ahead on
- Reach, 65 against 55
- Agent interface, 85 against 60
- Documentation and upkeep, 59 against 26
Watch for
178 GB at 4 bits, so it needs about 200 GB of memory
Best for Agents on a machine with about 96 GB to spare that need one local model to hear, see and answer by voice
Ahead on
- Risk, 55 against 45
Watch for
Only a 70.5 GB BF16 build, about 85 GB to run, with no quantised build from Alibaba
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.
Documentation and upkeep MiMo-V2.6-Flash-RL +33
MiMo-V2.6-Flash-RL 59
Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59.
Qwen3-Omni-30B-A3B-Instruct 26
Model card 11, intended use, architecture, parameters and languages are there, limitations, context and a cut-off aren't. Training data 5, the report's description has no cut-off. Last change 0, nothing that runs the model has changed for over a year. Responsiveness 0, Qwen answered none of the 40 most recent discussions. Version policy 5, Qwen2.5-Omni kept without notes. File health 5, contradictory licence and language metadata and an unanswered report about long audio.
Agent interface MiMo-V2.6-Flash-RL +25
MiMo-V2.6-Flash-RL 85
Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.
Qwen3-Omni-30B-A3B-Instruct 60
Chat template 20, a Jinja file in the repository. Tool-calling format 10, the cookbook shows how tools are declared and calls come back and the card covers audio, image and video input and speech output, but no parser is named. Trained tool calling 20, two calls in one turn from speech in the cookbook. Structured output 5, speech in a chosen voice and text-only output are documented, with no JSON. Reasoning control 0, no thinking in this edition. Context 5, 65,536 tokens in the config and trained at 32,768.
Reach MiMo-V2.6-Flash-RL +10
MiMo-V2.6-Flash-RL 65
Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.
Qwen3-Omni-30B-A3B-Instruct 55
Memory earns 10, because the only build is the 70.5 GB BF16 checkpoint, about 85 GB with a fifth added, which fits only the reference machine's band. Runtimes earn 20. Transformers runs the whole model, and released vLLM, SGLang and llama.cpp run its Thinker with text out, while Ollama and LM Studio have nothing. Builds score 0, with no FP8, 4-bit or GGUF build from Alibaba. Apache 2.0 with nothing added gives terms the full 15 and the ungated repository gives download the full 10. Hosted too scores 0, since no provider serves this checkpoint and Alibaba's API serves a different Flash model.
Risk Qwen3-Omni-30B-A3B-Instruct +10
MiMo-V2.6-Flash-RL 45
File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45.
Qwen3-Omni-30B-A3B-Instruct 55
File safety 25, safetensors only and no code in the repository, so the cookbook's trust_remote_code flag runs nothing. Provenance 15, files in the Qwen organisation with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published, though spoken input can trigger tool calls. Licence risk 15, plain Apache 2.0. Security programme 0, no security contact, disclosure policy or advisories for Qwen.
Score by category
| Category | Weight this run | MiMo-V2.6-Flash-RL | Qwen3-Omni-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 55 | MiMo-V2.6-Flash-RL +10 |
| Risk | 20%20 | 45 | 55 | Qwen3-Omni-30B-A3B-Instruct +10 |
| Agent interface | 10%10 | 85 | 60 | MiMo-V2.6-Flash-RL +25 |
| Documentation and upkeep | 10%10 | 59 | 26 | MiMo-V2.6-Flash-RL +33 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | E 41.8/100 |
Facts side by side
| Fact | MiMo-V2.6-Flash-RL | Qwen3-Omni-30B-A3B-Instruct |
|---|---|---|
| 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) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-28 | 2025-09-22 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2/5 (4) |
Verdicts
MiMo-V2.6-Flash-RL
A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.
Qwen3-Omni-30B-A3B-Instruct
An omni-modal cousin that listens, watches and reads, then answers in text or one of three voices, with tool calls from spoken requests, under plain Apache 2.0. Its only build, BF16, needs about 85 GB, and speech out needs Transformers or vLLM-Omni. The main risk is no answers from Qwen and no safety work for speech-driven actions.
Before you call either
MiMo-V2.6-Flash-RL
- Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita
- On SGLang or vLLM pass
--tool-call-parser mimo --reasoning-parser mimo - Use temperature 1.0 and top_p 0.95, as the card recommends
- The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in
- Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle
- The Use Restrictions section asks for human oversight of high-risk actions
Qwen3-Omni-30B-A3B-Instruct
- For text answers,
mkdir -p media && vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path "$(pwd)/media"with vLLM 0.11.1 or later serves the Thinker on an OpenAI-compatible API - The card's command passes
--allowed-local-media-path /, which lets anyone who can reach the server have it read any file on the machine through a file:// URL. Point it at a dedicated folder that holds only the media you mean to send, as above, and give vLLM an absolute path - Send audio as
audio_urland images asimage_urlcontent parts; vLLM serve doesn't take use_audio_in_video, so pass a video's audio separately - For speech out, load Qwen3OmniMoeForConditionalGeneration in Transformers 4.57.0 or later with qwen-omni-utils, pick speaker Ethan, Chelsie or Aiden, and write the returned waveform at 24 kHz
- Call model.disable_talker() or pass return_audio=False when you only need text, which saves about 10 GB and time
- Parse tool calls as JSON inside
<tool_call>tags yourself, since no parser is named, and confirm actions triggered by speech before running them - Keep use_audio_in_video the same across every step of a conversation, the card warns results go wrong otherwise
- Budget memory for video, the card's table rises from 78.85 GB for 15 seconds to 144.81 GB for 120 seconds in BF16
Questions
Which is better for AI agents, MiMo-V2.6-Flash-RL or Qwen3-Omni-30B-A3B-Instruct?
MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 3 of 4 scored categories. Qwen3-Omni-30B-A3B-Instruct leads on risk.
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- Qwen3.5-0.8B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.5-122B-A10B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.5-2B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.5-4B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.5-9B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.6-27B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.8-27B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.8-Flash-Next vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Coder-Next vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-2B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-Omni-30B-A3B-Instruct vs Recursive Flint
- Qwen3-Omni-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-Omni-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-Omni-30B-A3B-Instruct vs SmolLM3-3B
- Qwen3-Omni-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-Omni-30B-A3B-Instruct vs Step 3.7 Flash
- Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-Omni-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-2B
- Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-4B
- Qwen3-Omni-30B-A3B-Instruct vs ZAYA1-8B
- Cohere Transcribe 03-2026 vs Qwen3-Omni-30B-A3B-Instruct
- Granite Speech 5.0 2B vs Qwen3-Omni-30B-A3B-Instruct
- Ming-3.0-asr-tiny vs Qwen3-Omni-30B-A3B-Instruct
- Ming-3.0-audio-flash vs Qwen3-Omni-30B-A3B-Instruct
- Ming-3.0-audio-tiny vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3.5 Transcribe 30B-A4B vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-Omni-30B-A3B-Instruct
- Parakeet TDT 0.6B v3 vs Qwen3-Omni-30B-A3B-Instruct
- Parakeet Unified EN 0.6B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-ASR-0.6B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-ASR-1.7B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Underdog Bark 3.0
- Qwen3-Omni-30B-A3B-Instruct vs Voxtral Mini 4B Realtime 2602
Machine-readable
- This page as Markdown
/compare/mimo-v2-6-flash-rl-vs-qwen3-omni-30b-a3b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/mimo-v2-6-flash-rl.json·/api/v1/tools/qwen3-omni-30b-a3b-instruct.json - From a terminal
anchor compare mimo-v2-6-flash-rl qwen3-omni-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "mimo-v2-6-flash-rl", "b": "qwen3-omni-30b-a3b-instruct"}at/mcp, no key