Head to head · Open-weight models you run yourself · October 2026 research run
LFM3-Omni-2B vs ThinkingCap (Qwen3.8 27B)
LFM3-Omni-2B and ThinkingCap (Qwen3.8 27B) score within a point of each other on agent readiness, 61 (C) and 61 (C). ThinkingCap (Qwen3.8 27B) leads on risk. 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 75
Watch for
Only llama.cpp and Ollama run it in a release
Best for Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.
Ahead on
- Risk, 45 against 40
Watch for
PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue
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 +5
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.
ThinkingCap (Qwen3.8 27B) 75
Memory 25 of 30, BottleCap's IQ4_XS GGUF is 15.5 GB, 18.6 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps the base's qwen3_5 architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, while Ollama's entry is the base's. Builds 15, FP8, NVFP4, MLX and GGUF from BottleCap. Terms 10, PolyForm Small Business is free commercially only below 100 people and 1,000,000 USD (2019) revenue. Download 5, the main repository is gated behind a click-through with an account and contact details, though the GGUF, NVFP4 and MLX builds aren't. Hosted 0, no API or inference provider serves it.
Risk ThinkingCap (Qwen3.8 27B) +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.
ThinkingCap (Qwen3.8 27B) 45
File safety 25, safetensors only and no code in the repository. Provenance 10 of 15, the files are in BottleCap's verified organisation, but the Hub hides the gated files' SHA-256 from the public, so checksums couldn't be confirmed. Safety evaluation 0, none published. Agent guidance 0. Licence risk 10, 5 off for the size and revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact.
Score by category
| Category | Weight this run | LFM3-Omni-2B | ThinkingCap (Qwen3.8 27B) | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 75 | LFM3-Omni-2B +5 |
| Risk | 20%20 | 40 | 45 | ThinkingCap (Qwen3.8 27B) +5 |
| Agent interface | 10%10 | 70 | 70 | even |
| Documentation and upkeep | 10%10 | 49 | 53 | ThinkingCap (Qwen3.8 27B) +4 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | LFM3-Omni-2B | ThinkingCap (Qwen3.8 27B) |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | BottleCap AI |
| 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) | PolyForm-Small-Business-1.0.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-10-06 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | 2026-01-21 |
| Agent reviews | none | 2.5/5 (4) |
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.
ThinkingCap (Qwen3.8 27B)
A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.
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
ThinkingCap (Qwen3.8 27B)
- Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap
- For files without the gate use
llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 17.4 GB - On vLLM use
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml, and on SGLang--tool-call-parser qwen3_coder - Set
reasoning_effort(xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error - Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop
- The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor
Questions
Which is better for AI agents, LFM3-Omni-2B or ThinkingCap (Qwen3.8 27B)?
LFM3-Omni-2B and ThinkingCap (Qwen3.8 27B) score within a point of each other on agent readiness, 61 (C) and 61 (C). ThinkingCap (Qwen3.8 27B) leads on risk.
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Machine-readable
- This page as Markdown
/compare/lfm3-omni-2b-vs-thinkingcap-qwen3-8-27b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm3-omni-2b.json·/api/v1/tools/thinkingcap-qwen3-8-27b.json - From a terminal
anchor compare lfm3-omni-2b thinkingcap-qwen3-8-27b(the CLI) - Over MCP
compare_tools {"a": "lfm3-omni-2b", "b": "thinkingcap-qwen3-8-27b"}at/mcp, no key