Head to head · Open-weight models you run yourself · October 2026 research run
LFM3-Omni-2B vs MiniMax-M3.1-Flash
LFM3-Omni-2B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 54.3 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons
Which one, for what
Best for Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools
Ahead on
- Reach, 80 against 55
- Risk, 40 against 35
- Documentation and upkeep, 49 against 41
Watch for
Only llama.cpp and Ollama run it in a release
Best for Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
SGLang's cookbook still points to a pull request or a dev image on 4 November 2026, and vLLM's recipe still names a nightly image, though both runtimes' releases carry the model code
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.
Reach LFM3-Omni-2B +25
LFM3-Omni-2B 80
Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.29 GB projector file, and 1.39 GB × 1.2 = 1.7 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 vision and audio projectors and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example), and Ollama's library has an lfm3-omni entry. 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-omni-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.
MiniMax-M3.1-Flash 55
Memory earns nothing. MiniMax's only build is its 250 GB NVFP4 and MXFP8 release, about 300 GB with the fifth added, above 256 GB, and no runtime library has a local build. Runtimes earn 20. llama.cpp from b11861 runs it, Ollama v0.40.14 pins llama.cpp b11963 with the minimax-m3 architecture, vLLM v0.33.0 (tagged 1 November 2026) registers the architecture and meets its recipe's minimum version, and SGLang v0.5.23 (tagged 29 October) registers it with M3.1 processor code and tests, though its cookbook still points to a pull request. Transformers support is in progress per the card and LM Studio has no entry. Builds earn 10 for one publisher precision. Terms earn 10, since commercial use is free with attribution and a notice below $20M of yearly revenue. Download earns 10, with no gate. Hosted earns 5, since MiniMax's API and Novita serve it. 0 + 20 + 10 + 10 + 10 + 5 = 55.
Documentation and upkeep LFM3-Omni-2B +8
LFM3-Omni-2B 49
Model card 14, Intended use (one on-device model in place of separate speech recognition, vision, language and speech models), architecture, parameters (2.17B), context (32K) and the text and speech languages. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 28 October, documented video frame rate and frame limits. Responsiveness 0, none of the 12 discussions that aren't pull requests (27 October to 9 November 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 14 + 5 + 20 + 0 + 5 + 5 = 49.
MiniMax-M3.1-Flash 41
The model card earns 11, missing limitations, languages and a knowledge cut-off. Training data earns nothing, as it isn't described. The last change earns 20, an evaluation-results commit of 30 October 2026 after the release commit of 23 October. Responsiveness earns nothing, with no MiniMax reply in nine discussions opened from 23 to 31 October. Version policy earns 5 for a first release in its own repository. File health earns 5, for the tokenizer's 40,960,000-token model_max_length against the 1M context, the Transformers library tag against the card, and an unanswered vLLM streaming report. 11 + 0 + 20 + 0 + 5 + 5 = 41.
Risk LFM3-Omni-2B +5
LFM3-Omni-2B 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.
MiniMax-M3.1-Flash 35
File safety earns 15, safetensors only, less 10 because the SGLang route the card links passes trust_remote_code for the repository's configuration and processor code. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing; the launch post's security testing measured offensive capability, and the template's root role isn't documented as a trained hierarchy. Licence risk earns 5, after the military-use ban and the $20M threshold. The security programme earns nothing, with no security.txt, disclosure policy or advisories.
Score by category
| Category | Weight this run | LFM3-Omni-2B | MiniMax-M3.1-Flash | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 55 | LFM3-Omni-2B +25 |
| Risk | 20%20 | 40 | 35 | LFM3-Omni-2B +5 |
| Agent interface | 10%10 | 70 | 70 | even |
| Documentation and upkeep | 10%10 | 49 | 41 | LFM3-Omni-2B +8 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 54.3/100 |
Facts side by side
| Fact | LFM3-Omni-2B | MiniMax-M3.1-Flash |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | MiniMax |
| 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) | MiniMax Community License (custom; commercial use needs a "Built with MiniMax M3.1-Flash" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue) |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-10-23 |
| 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-Omni-2B
No score change since it was listed.
Verdicts
LFM3-Omni-2B
One 2.17B model that sees, listens and talks on an 8 GB machine and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, it doesn't reason, Liquid hasn't answered a question on the repository, and there are no safety results or data sources.
MiniMax-M3.1-Flash
A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights. vLLM 0.33.0 and SGLang 0.5.23 carry its code. Commercial use needs attribution and notice.
Before you call either
LFM3-Omni-2B
- Start it with
llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --jinjaorollama run lfm3-omni - Greedy decoding for text. For speech use audio_temperature 0.4 and audio_repetition_penalty 1.2, per the card
- The context is 32K, and llama.cpp samples video at 2 frames a second up to 384 frames
- Parse tool calls yourself from the XML it returns
- It doesn't reason, so don't pass enable_thinking
- Validate JSON output yourself
MiniMax-M3.1-Flash
- Check the use case first; commercial products must show "Built with MiniMax M3.1-Flash" and notify MiniMax once, or get written authorisation above $20M yearly revenue
- On 8 H100 or H200 GPUs use vLLM 0.33.0 or later, or the recipe's image vllm/vllm-openai:minimax-m31-flash, with --tensor-parallel-size 8 --block-size 128 --kv-cache-dtype fp8 --moe-backend marlin --max-model-len 262144
- Pass --reasoning-parser minimax_m3 --tool-call-parser minimax_m3 --enable-auto-tool-choice in vLLM, or minimax-m3 for both in SGLang with --trust-remote-code
- --block-size 128 is mandatory in vLLM, since MSA's sparse block size is 128
- Set effort per request with chat_template_kwargs reasoning_effort (low, medium, high, xhigh, max); sample at temperature 1.0 and top_p 0.95
- On Blackwell or MI355X, 4 GPUs serve it with the DSpark draft from dspark/; vLLM's Hopper command leaves DSpark off and turns video input off
- For GGUF builds by others, use llama.cpp b11861 or later; earlier builds mis-scale the NVFP4 experts and produce garbage
- Without the hardware, call MiniMax-M3.1-Flash on MiniMax's API (minimax-api), whose thinking defaults to max effort
Questions
Which is better for AI agents, LFM3-Omni-2B or MiniMax-M3.1-Flash?
LFM3-Omni-2B scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 54.3 (C), and leads in 3 of 4 scored categories.
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Machine-readable
- This page as Markdown
/compare/lfm3-omni-2b-vs-minimax-m3-1-flash.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm3-omni-2b.json·/api/v1/tools/minimax-m3-1-flash.json - From a terminal
anchor compare lfm3-omni-2b minimax-m3-1-flash(the CLI) - Over MCP
compare_tools {"a": "lfm3-omni-2b", "b": "minimax-m3-1-flash"}at/mcp, no key