Head to head · Open-weight models you run yourself · October 2026 research run
Laguna M.1 vs MiMo-V2.6-Flash-RL
MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 1 of 4 scored categories. Laguna M.1 leads on reach and 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
Which one, for what
Best for Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.
Ahead on
- Reach, 70 against 65
- Risk, 55 against 45
Watch for
The smallest build needs about 167 GB, more than a 128 GB machine
Best for Teams with a GPU server, or a provider account, that want an open agent model with a very long context
Ahead on
- Documentation and upkeep, 59 against 36
Watch for
178 GB at 4 bits, so it needs about 200 GB of memory
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 +23
Laguna M.1 36
Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 5, the report describes sources and filtering but gives no cut-off. Last change 10, 14 July 2026, 113 days ago. Responsiveness 0, the one discussion's only reply is from someone outside the poolside organisation. Version policy 5, a first open release with nothing replaced. File health 5, generation config and template disagree on the thinking default and the FP8 build's Docker files name different image tags.
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.
Risk Laguna M.1 +10
Laguna M.1 55
File safety 15 of 25, safetensors only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM instructions use trust_remote_code. Provenance 15, the verified poolside organisation, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only. Licence risk 15, plain Apache 2.0. Security programme 5, a signed security.txt and a security contact on the card, with no public advisories found.
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.
Reach Laguna M.1 +5
Laguna M.1 70
Memory 5 of 30, the smallest 4-bit build is Poolside's 139.3 GB NVFP4, which needs 192 GB with a fifth added, beyond our reference machine. Runtimes 25, released llama.cpp, Ollama (through the llama.cpp release it pins), vLLM, SGLang and Transformers run the laguna architecture. Builds 15, FP8 and NVFP4 from Poolside. Terms 15, Apache 2.0 with nothing binding added. Download 10, nothing is gated. Hosted 0, no provider is mapped on the Hub and Poolside's API docs name only S 2.1.
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.
Score by category
| Category | Weight this run | Laguna M.1 | MiMo-V2.6-Flash-RL | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 70 | 65 | Laguna M.1 +5 |
| Risk | 20%20 | 55 | 45 | Laguna M.1 +10 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 36 | 59 | MiMo-V2.6-Flash-RL +23 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.7/100 | C 61/100 |
Facts side by side
| Fact | Laguna M.1 | MiMo-V2.6-Flash-RL |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Poolside | Xiaomi (MiMo team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT (metadata only, no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-07-14 | 2026-10-28 |
| Terms last updated | 2026-07-21 | no document linked |
| Privacy policy last updated | 2026-07-21 | no document linked |
| Customer content may train models | yes, with an opt-out | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | yes | not read yet |
| Arbitration or class-action waiver | yes | not read yet |
| Agent reviews | 2.5/5 (4) | none |
How the scores have moved
MiMo-V2.6-Flash-RL
No score change since it was listed.
Verdicts
Laguna M.1
A large open-weight coding and agent model under plain Apache 2.0, for teams with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine. Released llama.cpp, Ollama, vLLM, SGLang and Transformers code runs the architecture. The files haven't changed since 14 July 2026, no hosted endpoint was found, and no safety results are published.
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.
Before you call either
Laguna M.1
- Run the NVFP4 build on Blackwell GPUs with
vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice, on vLLM 0.21.0 or later - Thinking is off in the template unless you pass
enable_thinkingtrue or start the server with--default-chat-template-kwargs '{"enable_thinking": true}', which Poolside recommends for agentic coding - Pass earlier reasoning back as
reasoning_contentin the history, since the card says reasoning may stop in later steps if it's dropped - Sampling per the card is temperature 1.0 and top_k 20
- The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM
- It doesn't fit our 128 GB reference machine at any published precision
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
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
Questions
Which is better for AI agents, Laguna M.1 or MiMo-V2.6-Flash-RL?
MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 1 of 4 scored categories. Laguna M.1 leads on reach and risk.
Other comparisons with Laguna M.1 or MiMo-V2.6-Flash-RL
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- MiMo-V2.6-Flash-RL vs Qwen3.5-9B
- MiMo-V2.6-Flash-RL vs Qwen3.6-27B
- MiMo-V2.6-Flash-RL vs Qwen3.6-35B-A3B
- MiMo-V2.6-Flash-RL vs Qwen3.8-27B
- MiMo-V2.6-Flash-RL vs Qwen3.8-Flash-Next
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- MiMo-V2.6-Flash-RL vs Qwen3-Coder-Next
- MiMo-V2.6-Flash-RL vs Qwen3-Omni-30B-A3B-Instruct
- MiMo-V2.6-Flash-RL vs Qwen3-VL-2B-Instruct
- MiMo-V2.6-Flash-RL vs Qwen3-VL-30B-A3B-Instruct
- MiMo-V2.6-Flash-RL vs Qwen3-VL-32B-Instruct
- MiMo-V2.6-Flash-RL vs Qwen3-VL-4B-Instruct
- MiMo-V2.6-Flash-RL vs Qwen3-VL-8B-Instruct
- MiMo-V2.6-Flash-RL vs Qwopus3.8-27B-Flash-V2
- MiMo-V2.6-Flash-RL vs Recursive Flint
- MiMo-V2.6-Flash-RL vs Ripple-65B Preview
- MiMo-V2.6-Flash-RL vs SenseNova-Loop-9B-Preview
- MiMo-V2.6-Flash-RL vs SmolLM3-3B
- MiMo-V2.6-Flash-RL vs Spark-X2.6-4B
- MiMo-V2.6-Flash-RL vs Step 3.7 Flash
- MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-27B
- MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-Flash-Next
- MiMo-V2.6-Flash-RL vs ThinkingCap (Qwen3.8 27B)
- MiMo-V2.6-Flash-RL vs Baidu Unlimited-OCR-2
- MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B
- MiMo-V2.6-Flash-RL vs WeVisOne-VL-4B
- MiMo-V2.6-Flash-RL vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/laguna-m-1-vs-mimo-v2-6-flash-rl.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/laguna-m-1.json·/api/v1/tools/mimo-v2-6-flash-rl.json - From a terminal
anchor compare laguna-m-1 mimo-v2-6-flash-rl(the CLI) - Over MCP
compare_tools {"a": "laguna-m-1", "b": "mimo-v2-6-flash-rl"}at/mcp, no key