Head to head · Open-weight models you run yourself · October 2026 research run
Laguna S 2.1 vs MiMo-V2.6-Flash-RL
Laguna S 2.1 and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6-Flash-RL 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 Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.
Ahead on
- Reach, 75 against 65
- Risk, 55 against 45
Watch for
No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls
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 41
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 +18
Laguna S 2.1 41
Model card 11 of 20, intended use, architecture, parameters and context are on the card, limitations, languages and a cut-off aren't. Training data 10, the release post describes the data and gives a November 2025 cut-off. Last change 15, 25 August 2026, 70 days back. Responsiveness 0, Poolside answered none of the 34 discussions. Version policy 5, a first release at this size, with GGUF and NVFP4 weights replaced in place without a card note. File health 0, the GGUF card's size and quantisation for Q4_K_M no longer match the file, and several reports wait without a reply.
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.
Reach Laguna S 2.1 +10
Laguna S 2.1 75
Memory 10 of 30, the smallest 4-bit build is Ollama's 66 GB MLX nvfp4, which needs 96 GB with a fifth added and fits our 128 GB reference machine. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 5, Poolside's own API serves it and Featherless AI hosts it, though Poolside's OpenRouter endpoints have expired.
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.
Risk Laguna S 2.1 +10
Laguna S 2.1 55
File safety 15 of 25, safetensors and GGUF only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM commands 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, OpenMDW-1.1 has no use policy, threshold or revocation clause. 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.
Score by category
| Category | Weight this run | Laguna S 2.1 | MiMo-V2.6-Flash-RL | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 75 | 65 | Laguna S 2.1 +10 |
| Risk | 20%20 | 55 | 45 | Laguna S 2.1 +10 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 41 | 59 | MiMo-V2.6-Flash-RL +18 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | Laguna S 2.1 | 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 | OpenMDW-1.1 | MIT (metadata only, no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-19 | 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 | 2.3/5 (4) | none |
Verdicts
Laguna S 2.1
A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.
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 S 2.1
- Quickest start is
ollama run laguna-s-2.1, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon - On our reference machine try
llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB - With vLLM or SGLang use
--tool-call-parser poolside_v1 --reasoning-parser poolside_v1, and with TensorRT-LLM--tool_parser poolside_v1 --reasoning_parser laguna - Thinking is on by default in the template, pass
chat_template_kwargswithenable_thinkingfalse to turn it off, and keepreasoning_contentfrom earlier turns - The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add
--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192, which Poolside says can cost quality - Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M
- Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations
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 S 2.1 or MiMo-V2.6-Flash-RL?
Laguna S 2.1 and MiMo-V2.6-Flash-RL score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6-Flash-RL leads on documentation and upkeep.
Other comparisons with Laguna S 2.1 or MiMo-V2.6-Flash-RL
- Agents-A1-4B vs Laguna S 2.1
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- Apodex-1.1-mini vs Laguna S 2.1
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- Arete-27B vs Laguna S 2.1
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- BigBang-v1 vs Laguna S 2.1
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- Carbon-X vs Laguna S 2.1
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- Cola2 vs Laguna S 2.1
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- Cosmos-Reason2-2B vs Laguna S 2.1
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- DeepSeek-V3.2 vs Laguna S 2.1
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- DeepSeek V4.1 Flash vs Laguna S 2.1
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- DeepSeek-V4-Flash-0731 vs Laguna S 2.1
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- Devstral Small 2 vs Laguna S 2.1
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- DiffusionGemma 26B A4B IT vs Laguna S 2.1
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- EXAONE 4.5 33B vs Laguna S 2.1
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- Gemma 4 12B IT vs Laguna S 2.1
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- Gemma 4 26B A4B IT vs Laguna S 2.1
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- Gemma 4 E2B IT vs Laguna S 2.1
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- Gemma 4 E4B IT vs Laguna S 2.1
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- GLM-4.5-Air vs Laguna S 2.1
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- GLM-5.3-Flash vs Laguna S 2.1
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- gpt-oss-120b vs Laguna S 2.1
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- gpt-oss-20b vs Laguna S 2.1
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- Granite 4.2 3B vs Laguna S 2.1
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- Granite 4.2 8B vs Laguna S 2.1
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- Inkling-Small vs Laguna S 2.1
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- Inkling vs Laguna S 2.1
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- Laguna S 2.1 vs LBS-27B
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- Laguna S 2.1 vs LFM3-1.7B
- Laguna S 2.1 vs LFM3-Audio-2B
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- Laguna S 2.1 vs LongCat-2.5
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- Laguna S 2.1 vs MiniCPM-V 4.7 1B
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- Laguna S 2.1 vs MiniCPM5-2B
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- Laguna S 2.1 vs MiniMax-M2.7
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- Laguna S 2.1 vs Mistral Large 4
- Laguna S 2.1 vs Muse Glimmer 30B
- Laguna S 2.1 vs Nanbeige4.2-3B
- Laguna S 2.1 vs Nanbeige4.3-33B
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- Laguna S 2.1 vs Nemotron 3 Ultra 550B A55B
- Laguna S 2.1 vs North Mini Code 1.0
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- Laguna S 2.1 vs Qwen3-VL-32B-Instruct
- Laguna S 2.1 vs Qwen3-VL-4B-Instruct
- Laguna S 2.1 vs Qwen3-VL-8B-Instruct
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- Laguna S 2.1 vs SmolLM3-3B
- Laguna S 2.1 vs Spark-X2.6-4B
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- Laguna S 2.1 vs WeVisOne-VL-2B
- Laguna S 2.1 vs WeVisOne-VL-4B
- Laguna S 2.1 vs ZAYA1-8B
- Laguna XS 2.1 vs MiMo-V2.6-Flash-RL
- LBS-27B vs MiMo-V2.6-Flash-RL
- LFM2.5-2.6B vs MiMo-V2.6-Flash-RL
- LFM3-1.7B vs MiMo-V2.6-Flash-RL
- LFM3-Audio-2B vs MiMo-V2.6-Flash-RL
- LFM3-Omni-2B vs MiMo-V2.6-Flash-RL
- LFM3-VL-2B vs MiMo-V2.6-Flash-RL
- Ling-3.0-tiny vs MiMo-V2.6-Flash-RL
- Ling-3.1-flash-1019 vs MiMo-V2.6-Flash-RL
- LongCat-2.5 vs MiMo-V2.6-Flash-RL
- MiMo-V2.6 Distill Qwen 9B vs MiMo-V2.6-Flash-RL
- MiMo-V2.6-Flash-RL vs MiniCPM-o 4.5
- MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 1B
- MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 35B-A3B
- MiMo-V2.6-Flash-RL vs MiniCPM5-2B
- MiMo-V2.6-Flash-RL vs MiniMax-M2.5
- MiMo-V2.6-Flash-RL vs MiniMax-M2.7
- MiMo-V2.6-Flash-RL vs MiniMax-M3.1-Flash
- MiMo-V2.6-Flash-RL vs Mistral Large 4
- MiMo-V2.6-Flash-RL vs Muse Glimmer 30B
- MiMo-V2.6-Flash-RL vs Nanbeige4.2-3B
- MiMo-V2.6-Flash-RL vs Nanbeige4.3-33B
- MiMo-V2.6-Flash-RL vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- MiMo-V2.6-Flash-RL vs Nemotron 3.5 Lightning 30B A3B
- MiMo-V2.6-Flash-RL vs Nemotron 3 Nano 30B A3B
- MiMo-V2.6-Flash-RL vs Nemotron 3 Nano 4B
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- MiMo-V2.6-Flash-RL vs Nemotron 3 Super 120B A12B
- MiMo-V2.6-Flash-RL vs Nemotron 3 Ultra 550B A55B
- MiMo-V2.6-Flash-RL vs North Mini Code 1.0
- MiMo-V2.6-Flash-RL vs occamy-1.0
- MiMo-V2.6-Flash-RL vs Olmo 3 7B Think
- MiMo-V2.6-Flash-RL vs Ornith-1.5-122B
- MiMo-V2.6-Flash-RL vs Ornith-1.5-35B-A3B
- MiMo-V2.6-Flash-RL vs Ornith-1.5-397B
- MiMo-V2.6-Flash-RL vs Ornith-1.5-9B
- MiMo-V2.6-Flash-RL vs OTel-2.0-LLM-31B-IT
- MiMo-V2.6-Flash-RL vs Qwen-AgentWorld-35B-A3B
- MiMo-V2.6-Flash-RL vs Qwen3-30B-A3B-Instruct-2507
- MiMo-V2.6-Flash-RL vs Qwen3-4B-Instruct-2507
- MiMo-V2.6-Flash-RL vs Qwen3.5-0.8B
- MiMo-V2.6-Flash-RL vs Qwen3.5-122B-A10B
- MiMo-V2.6-Flash-RL vs Qwen3.5-2B
- MiMo-V2.6-Flash-RL vs Qwen3.5-397B-A17B
- MiMo-V2.6-Flash-RL vs Qwen3.5-4B
- 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
- MiMo-V2.6-Flash-RL vs Qwen3-Coder-30B-A3B-Instruct
- 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-s-2-1-vs-mimo-v2-6-flash-rl.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/laguna-s-2-1.json·/api/v1/tools/mimo-v2-6-flash-rl.json - From a terminal
anchor compare laguna-s-2-1 mimo-v2-6-flash-rl(the CLI) - Over MCP
compare_tools {"a": "laguna-s-2-1", "b": "mimo-v2-6-flash-rl"}at/mcp, no key