Head to head · Open-weight models you run yourself · October 2026 research run
LFM3-1.7B vs MiMo-V2.6 Distill Qwen 9B
LFM3-1.7B and MiMo-V2.6 Distill Qwen 9B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6 Distill Qwen 9B 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
Which one, for what
Best for Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits
Ahead on
- Reach, 85 against 80
- Agent interface, 100 against 70
- Documentation and upkeep, 67 against 40
Watch for
vLLM and SGLang have no released support, though the card lists both
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 40
Watch for
Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's
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.
Agent interface LFM3-1.7B +30
LFM3-1.7B 100
Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, tools, deferred tools, tool results and the thinking switch. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through tools= in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card says the model was trained on tool calls and agentic traces and with reinforcement learning inside agent harnesses. Structured output 15, Liquid's API guide documents response_format with a JSON Schema, with lfm3-1.7b as the model in its examples, and says the schema is checked before generation starts. Reasoning control 10, the card documents enable_thinking=False in the chat template to turn reasoning off per request, with reasoning on by default inside think tags, and preserve_thinking for earlier turns. Context 10, 256K tokens per the card, which says the mid-training stage extended the context window to 256K, and max_position_embeddings 262144 in config.json. 20 + 25 + 20 + 15 + 10 + 10 = 100.
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.
Documentation and upkeep LFM3-1.7B +27
LFM3-1.7B 67
Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), a limitation (not recommended for agentic coding or complex multi-step reasoning), architecture (24 layers: 13 gated short convolutions, 5 sliding-window and 6 global attention), parameters (1.73B), context (256K) and 16 languages. Training data 5, A general description: 22T tokens of pre-training in two stages with the kinds of data added since LFM2, 1.75T tokens of supervised fine-tuning, then mixed reinforcement learning, distillation from six specialist teachers and agentic reinforcement learning. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 10, Of the 22 discussions that aren't pull requests (27 October to 8 November 2026), 7 have a reply from a LiquidAI member, which is some. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3 architecture, the 262,144-token context and the thinking switch. 17 + 5 + 20 + 10 + 5 + 10 = 67.
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.
Risk MiMo-V2.6 Distill Qwen 9B +25
LFM3-1.7B 40
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.
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.
Reach LFM3-1.7B +5
LFM3-1.7B 85
Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB, and 1.10 GB × 1.2 = 1.3 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture (src/models/lfm3.cpp at b12242), Ollama's library has an lfm3 entry, and LM Studio's catalogue runs the architecture (its liquid/lfm3-vl-2b entry; lmstudio-community publishes a GGUF of this model). Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-1.7b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.
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.
Score by category
| Category | Weight this run | LFM3-1.7B | MiMo-V2.6 Distill Qwen 9B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 80 | LFM3-1.7B +5 |
| Risk | 20%20 | 40 | 65 | MiMo-V2.6 Distill Qwen 9B +25 |
| Agent interface | 10%10 | 100 | 70 | LFM3-1.7B +30 |
| Documentation and upkeep | 10%10 | 67 | 40 | LFM3-1.7B +27 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | LFM3-1.7B | MiMo-V2.6 Distill Qwen 9B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | Xiaomi (MiMo team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-09 | 2026-09-21 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.3/5 (4) |
How the scores have moved
LFM3-1.7B
No score change since it was listed.
Verdicts
LFM3-1.7B
A 1.73B model that fits an 8 GB machine with room to spare, with a documented tool-call format, deferred tools, a thinking switch and a 256K context. It runs today in llama.cpp, Ollama and LM Studio only, loads in Transformers through repository code, and has no safety results, security contact or data sources.
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.
Before you call either
LFM3-1.7B
- Start it with
llama-server -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --jinjaorollama run lfm3 - Use temperature 0.1, top_k 50 and repetition penalty 1.1 for chat, tools and agents, per the card
- Pass
enable_thinking=Falsefor direct answers, and expect it to reason anyway on hard tasks - For a large tool catalogue mark tools
defer_loadingand give it a search tool - Don't plan on vLLM or SGLang until a release carries LFM3
- Liquid doesn't recommend it for agentic coding or complex multi-step reasoning
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
Questions
Which is better for AI agents, LFM3-1.7B or MiMo-V2.6 Distill Qwen 9B?
LFM3-1.7B and MiMo-V2.6 Distill Qwen 9B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6 Distill Qwen 9B leads on risk.
Other comparisons with LFM3-1.7B or MiMo-V2.6 Distill Qwen 9B
- Agents-A1-4B vs LFM3-1.7B
- Agents-A1-4B vs MiMo-V2.6 Distill Qwen 9B
- Apertus v1.5 8B vs LFM3-1.7B
- Apertus v1.5 8B vs MiMo-V2.6 Distill Qwen 9B
- Apodex-1.1-mini vs LFM3-1.7B
- Apodex-1.1-mini vs MiMo-V2.6 Distill Qwen 9B
- Arete-27B vs LFM3-1.7B
- Arete-27B vs MiMo-V2.6 Distill Qwen 9B
- BigBang-v1 vs LFM3-1.7B
- BigBang-v1 vs MiMo-V2.6 Distill Qwen 9B
- Carbon-X vs LFM3-1.7B
- Carbon-X vs MiMo-V2.6 Distill Qwen 9B
- Cola2 vs LFM3-1.7B
- Cola2 vs MiMo-V2.6 Distill Qwen 9B
- Cosmos-Reason2-2B vs LFM3-1.7B
- Cosmos-Reason2-2B vs MiMo-V2.6 Distill Qwen 9B
- Darwin-36B-Opus vs LFM3-1.7B
- Darwin-36B-Opus vs MiMo-V2.6 Distill Qwen 9B
- DeepSeek-V3.2 vs LFM3-1.7B
- DeepSeek-V3.2 vs MiMo-V2.6 Distill Qwen 9B
- DeepSeek V4.1 Flash vs LFM3-1.7B
- DeepSeek V4.1 Flash vs MiMo-V2.6 Distill Qwen 9B
- DeepSeek-V4-Flash-0731 vs LFM3-1.7B
- DeepSeek-V4-Flash-0731 vs MiMo-V2.6 Distill Qwen 9B
- Devstral Small 2 vs LFM3-1.7B
- Devstral Small 2 vs MiMo-V2.6 Distill Qwen 9B
- DiffusionGemma 26B A4B IT vs LFM3-1.7B
- DiffusionGemma 26B A4B IT vs MiMo-V2.6 Distill Qwen 9B
- EXAONE 4.5 33B vs LFM3-1.7B
- EXAONE 4.5 33B vs MiMo-V2.6 Distill Qwen 9B
- Gemma 4 12B IT vs LFM3-1.7B
- Gemma 4 12B IT vs MiMo-V2.6 Distill Qwen 9B
- Gemma 4 26B A4B IT vs LFM3-1.7B
- Gemma 4 26B A4B IT vs MiMo-V2.6 Distill Qwen 9B
- Gemma 4 31B IT vs LFM3-1.7B
- Gemma 4 31B IT vs MiMo-V2.6 Distill Qwen 9B
- Gemma 4 E2B IT vs LFM3-1.7B
- Gemma 4 E2B IT vs MiMo-V2.6 Distill Qwen 9B
- Gemma 4 E4B IT vs LFM3-1.7B
- Gemma 4 E4B IT vs MiMo-V2.6 Distill Qwen 9B
- GLM-4.5-Air vs LFM3-1.7B
- GLM-4.5-Air vs MiMo-V2.6 Distill Qwen 9B
- GLM-4.7-Flash vs LFM3-1.7B
- GLM-4.7-Flash vs MiMo-V2.6 Distill Qwen 9B
- GLM-5.3-Flash vs LFM3-1.7B
- GLM-5.3-Flash vs MiMo-V2.6 Distill Qwen 9B
- GLM-5.3 vs LFM3-1.7B
- GLM-5.3 vs MiMo-V2.6 Distill Qwen 9B
- gpt-oss-120b vs LFM3-1.7B
- gpt-oss-120b vs MiMo-V2.6 Distill Qwen 9B
- gpt-oss-20b vs LFM3-1.7B
- gpt-oss-20b vs MiMo-V2.6 Distill Qwen 9B
- Granite 4.2 30B vs LFM3-1.7B
- Granite 4.2 30B vs MiMo-V2.6 Distill Qwen 9B
- Granite 4.2 3B vs LFM3-1.7B
- Granite 4.2 3B vs MiMo-V2.6 Distill Qwen 9B
- Granite 4.2 8B vs LFM3-1.7B
- Granite 4.2 8B vs MiMo-V2.6 Distill Qwen 9B
- Hy-Memory-30B-A3B vs LFM3-1.7B
- Hy-Memory-30B-A3B vs MiMo-V2.6 Distill Qwen 9B
- Hy3 vs LFM3-1.7B
- Hy3 vs MiMo-V2.6 Distill Qwen 9B
- Inkling-Small vs LFM3-1.7B
- Inkling-Small vs MiMo-V2.6 Distill Qwen 9B
- Inkling vs LFM3-1.7B
- Inkling vs MiMo-V2.6 Distill Qwen 9B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-1.7B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiMo-V2.6 Distill Qwen 9B
- Kimi K3.1 Pro vs LFM3-1.7B
- Kimi K3.1 Pro vs MiMo-V2.6 Distill Qwen 9B
- Kimi K3 vs LFM3-1.7B
- Kimi K3 vs MiMo-V2.6 Distill Qwen 9B
- Kolibri-1 vs LFM3-1.7B
- Kolibri-1 vs MiMo-V2.6 Distill Qwen 9B
- Kronos vs LFM3-1.7B
- Kronos vs MiMo-V2.6 Distill Qwen 9B
- Laguna M.1 vs LFM3-1.7B
- Laguna M.1 vs MiMo-V2.6 Distill Qwen 9B
- Laguna S 2.1 vs LFM3-1.7B
- Laguna S 2.1 vs MiMo-V2.6 Distill Qwen 9B
- Laguna XS 2.1 vs LFM3-1.7B
- Laguna XS 2.1 vs MiMo-V2.6 Distill Qwen 9B
- LBS-27B vs LFM3-1.7B
- LBS-27B vs MiMo-V2.6 Distill Qwen 9B
- LFM2.5-2.6B vs LFM3-1.7B
- LFM2.5-2.6B vs MiMo-V2.6 Distill Qwen 9B
- LFM3-1.7B vs LFM3-Audio-2B
- LFM3-1.7B vs LFM3-Omni-2B
- LFM3-1.7B vs LFM3-VL-2B
- LFM3-1.7B vs Ling-3.0-tiny
- LFM3-1.7B vs Ling-3.1-flash-1019
- LFM3-1.7B vs LongCat-2.5
- LFM3-1.7B vs MiMo-V2.6-Flash-RL
- LFM3-1.7B vs MiniCPM-o 4.5
- LFM3-1.7B vs MiniCPM-V 4.7 1B
- LFM3-1.7B vs MiniCPM-V 4.7 35B-A3B
- LFM3-1.7B vs MiniCPM5-2B
- LFM3-1.7B vs MiniMax-M2.5
- LFM3-1.7B vs MiniMax-M2.7
- LFM3-1.7B vs MiniMax-M3.1-Flash
- LFM3-1.7B vs Mistral Large 4
- LFM3-1.7B vs Muse Glimmer 30B
- LFM3-1.7B vs Nanbeige4.2-3B
- LFM3-1.7B vs Nanbeige4.3-33B
- LFM3-1.7B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- LFM3-1.7B vs Nemotron 3.5 Lightning 30B A3B
- LFM3-1.7B vs Nemotron 3 Nano 30B A3B
- LFM3-1.7B vs Nemotron 3 Nano 4B
- LFM3-1.7B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- LFM3-1.7B vs Nemotron 3 Super 120B A12B
- LFM3-1.7B vs Nemotron 3 Ultra 550B A55B
- LFM3-1.7B vs North Mini Code 1.0
- LFM3-1.7B vs occamy-1.0
- LFM3-1.7B vs Olmo 3 7B Think
- LFM3-1.7B vs Ornith-1.5-122B
- LFM3-1.7B vs Ornith-1.5-35B-A3B
- LFM3-1.7B vs Ornith-1.5-397B
- LFM3-1.7B vs Ornith-1.5-9B
- LFM3-1.7B vs OTel-2.0-LLM-31B-IT
- LFM3-1.7B vs Qwen-AgentWorld-35B-A3B
- LFM3-1.7B vs Qwen3-30B-A3B-Instruct-2507
- LFM3-1.7B vs Qwen3-4B-Instruct-2507
- LFM3-1.7B vs Qwen3.5-0.8B
- LFM3-1.7B vs Qwen3.5-122B-A10B
- LFM3-1.7B vs Qwen3.5-2B
- LFM3-1.7B vs Qwen3.5-397B-A17B
- LFM3-1.7B vs Qwen3.5-4B
- LFM3-1.7B vs Qwen3.5-9B
- LFM3-1.7B vs Qwen3.6-27B
- LFM3-1.7B vs Qwen3.6-35B-A3B
- LFM3-1.7B vs Qwen3.8-27B
- LFM3-1.7B vs Qwen3.8-Flash-Next
- LFM3-1.7B vs Qwen3-Coder-30B-A3B-Instruct
- LFM3-1.7B vs Qwen3-Coder-Next
- LFM3-1.7B vs Qwen3-Omni-30B-A3B-Instruct
- LFM3-1.7B vs Qwen3-VL-2B-Instruct
- LFM3-1.7B vs Qwen3-VL-30B-A3B-Instruct
- LFM3-1.7B vs Qwen3-VL-32B-Instruct
- LFM3-1.7B vs Qwen3-VL-4B-Instruct
- LFM3-1.7B vs Qwen3-VL-8B-Instruct
- LFM3-1.7B vs Qwopus3.8-27B-Flash-V2
- LFM3-1.7B vs Recursive Flint
- LFM3-1.7B vs Ripple-65B Preview
- LFM3-1.7B vs SenseNova-Loop-9B-Preview
- LFM3-1.7B vs SmolLM3-3B
- LFM3-1.7B vs Spark-X2.6-4B
- LFM3-1.7B vs Step 3.7 Flash
- LFM3-1.7B vs Swift 1.5 Qwen3.8-27B
- LFM3-1.7B vs Swift 1.5 Qwen3.8-Flash-Next
- LFM3-1.7B vs ThinkingCap (Qwen3.8 27B)
- LFM3-1.7B vs Baidu Unlimited-OCR-2
- LFM3-1.7B vs WeVisOne-VL-2B
- LFM3-1.7B vs WeVisOne-VL-4B
- LFM3-1.7B vs ZAYA1-8B
- LFM3-Audio-2B vs MiMo-V2.6 Distill Qwen 9B
- LFM3-Omni-2B vs MiMo-V2.6 Distill Qwen 9B
- LFM3-VL-2B vs MiMo-V2.6 Distill Qwen 9B
- Ling-3.0-tiny vs MiMo-V2.6 Distill Qwen 9B
- Ling-3.1-flash-1019 vs MiMo-V2.6 Distill Qwen 9B
- LongCat-2.5 vs MiMo-V2.6 Distill Qwen 9B
- MiMo-V2.6 Distill Qwen 9B vs MiMo-V2.6-Flash-RL
- MiMo-V2.6 Distill Qwen 9B vs MiniCPM-o 4.5
- MiMo-V2.6 Distill Qwen 9B vs MiniCPM-V 4.7 1B
- MiMo-V2.6 Distill Qwen 9B vs MiniCPM-V 4.7 35B-A3B
- MiMo-V2.6 Distill Qwen 9B vs MiniCPM5-2B
- MiMo-V2.6 Distill Qwen 9B vs MiniMax-M2.5
- MiMo-V2.6 Distill Qwen 9B vs MiniMax-M2.7
- MiMo-V2.6 Distill Qwen 9B vs MiniMax-M3.1-Flash
- MiMo-V2.6 Distill Qwen 9B vs Mistral Large 4
- MiMo-V2.6 Distill Qwen 9B vs Muse Glimmer 30B
- MiMo-V2.6 Distill Qwen 9B vs Nanbeige4.2-3B
- MiMo-V2.6 Distill Qwen 9B vs Nanbeige4.3-33B
- MiMo-V2.6 Distill Qwen 9B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- MiMo-V2.6 Distill Qwen 9B vs Nemotron 3.5 Lightning 30B A3B
- MiMo-V2.6 Distill Qwen 9B vs Nemotron 3 Nano 30B A3B
- MiMo-V2.6 Distill Qwen 9B vs Nemotron 3 Nano 4B
- MiMo-V2.6 Distill Qwen 9B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- MiMo-V2.6 Distill Qwen 9B vs Nemotron 3 Super 120B A12B
- MiMo-V2.6 Distill Qwen 9B vs Nemotron 3 Ultra 550B A55B
- MiMo-V2.6 Distill Qwen 9B vs North Mini Code 1.0
- MiMo-V2.6 Distill Qwen 9B vs occamy-1.0
- MiMo-V2.6 Distill Qwen 9B vs Olmo 3 7B Think
- MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-122B
- MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-35B-A3B
- MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-397B
- MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-9B
- MiMo-V2.6 Distill Qwen 9B vs OTel-2.0-LLM-31B-IT
- MiMo-V2.6 Distill Qwen 9B vs Qwen-AgentWorld-35B-A3B
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-30B-A3B-Instruct-2507
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-4B-Instruct-2507
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-0.8B
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-122B-A10B
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-2B
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-397B-A17B
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-4B
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-9B
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.6-27B
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.6-35B-A3B
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.8-27B
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.8-Flash-Next
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-Coder-30B-A3B-Instruct
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-Coder-Next
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-Omni-30B-A3B-Instruct
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-2B-Instruct
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-30B-A3B-Instruct
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-32B-Instruct
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-4B-Instruct
- MiMo-V2.6 Distill Qwen 9B vs Qwen3-VL-8B-Instruct
- MiMo-V2.6 Distill Qwen 9B vs Qwopus3.8-27B-Flash-V2
- MiMo-V2.6 Distill Qwen 9B vs Recursive Flint
- MiMo-V2.6 Distill Qwen 9B vs Ripple-65B Preview
- MiMo-V2.6 Distill Qwen 9B vs SenseNova-Loop-9B-Preview
- MiMo-V2.6 Distill Qwen 9B vs SmolLM3-3B
- MiMo-V2.6 Distill Qwen 9B vs Spark-X2.6-4B
- MiMo-V2.6 Distill Qwen 9B vs Step 3.7 Flash
- MiMo-V2.6 Distill Qwen 9B vs Swift 1.5 Qwen3.8-27B
- MiMo-V2.6 Distill Qwen 9B vs Swift 1.5 Qwen3.8-Flash-Next
- MiMo-V2.6 Distill Qwen 9B vs ThinkingCap (Qwen3.8 27B)
- MiMo-V2.6 Distill Qwen 9B vs Baidu Unlimited-OCR-2
- MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-2B
- MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-4B
- MiMo-V2.6 Distill Qwen 9B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/lfm3-1-7b-vs-mimo-v2-6-distill-qwen-9b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm3-1-7b.json·/api/v1/tools/mimo-v2-6-distill-qwen-9b.json - From a terminal
anchor compare lfm3-1-7b mimo-v2-6-distill-qwen-9b(the CLI) - Over MCP
compare_tools {"a": "lfm3-1-7b", "b": "mimo-v2-6-distill-qwen-9b"}at/mcp, no key