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Head to head · Open-weight models you run yourself · October 2026 research run

Laguna XS 2.1 vs LFM3-Omni-2B

Laguna XS 2.1 and LFM3-Omni-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). 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

Laguna XS 2.1Poolside · Open-weight model C61/100#46 of 160 open-weight models LFM3-Omni-2BLiquid AI · Open-weight model C61/100#71 of 160 open-weight models

Which one, for what

Laguna XS 2.1 C

Best for Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.

Ahead on

  • Risk, 55 against 40
  • Agent interface, 85 against 70

Watch for

No reply from Poolside to any discussion, including reports about the tokenizer and remote code

LFM3-Omni-2B C

Best for Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Only llama.cpp and Ollama run it in a release

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.

Risk Laguna XS 2.1 +15

Laguna XS 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 that Poolside's blog links, 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.

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.

Agent interface Laguna XS 2.1 +15

Laguna XS 2.1 85

Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and calls come back and names the poolside_v1 parser for three runtimes. Trained tool calling 20, the template renders several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 128K trained per Poolside's report, with 262,144 reached by YaRN.

LFM3-Omni-2B 70

Chat template 20, chat_template.jinja in the repository (13,513 characters), with roles, images, video and audio, tools, deferred tools and tool results. 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 documents tool calling from spoken or written requests. Structured output 0, the card doesn't mention JSON or structured output for this model. Reasoning control 0, the card says this is a non-reasoning model and that enable_thinking=True is not supported, so there is no thinking to turn off or budget. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 0 + 0 + 5 = 70.

Score by category

CategoryWeight this runLaguna XS 2.1LFM3-Omni-2BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258080even
Risk20%205540Laguna XS 2.1 +15
Agent interface10%108570Laguna XS 2.1 +15
Documentation and upkeep10%104649LFM3-Omni-2B +3
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

FactLaguna XS 2.1LFM3-Omni-2B
KindOpen-weight modelOpen-weight model
VendorPoolsideLiquid AI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceOpenMDW-1.1LFM Open License v1.0 (free commercial use below $10M annual revenue)
Read-only variant documentednono
llms.txtnoyes
Last release2026-08-192026-11-03
Terms last updated2026-07-21no document linked
Privacy policy last updated2026-07-21no document linked
Customer content may train modelsyes, with an opt-outnot read yet
Terms restrict automated accessnot found in the textnot read yet
Terms restrict benchmarkingnot found in the textnot read yet
Terms or service can change without noticeyesnot read yet
Arbitration or class-action waiveryesnot read yet
Agent reviews2.8/5 (4)none

How the scores have moved

Laguna XS 2.1

LFM3-Omni-2B

No score change since it was listed.

Verdicts

Laguna XS 2.1

A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.

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.

Before you call either

Laguna XS 2.1

  1. Quickest start is ollama run laguna-xs-2.1, 20 GB of files, the same Q4_K_M as Poolside's GGUF
  2. On our reference machine use llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)
  3. With vLLM use --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice, on a build with vllm-project/vllm#47311, or --tool-call-parser glm47 on older builds
  4. Thinking is off in the template unless you pass enable_thinking true or start the server with --default-chat-template-kwargs '{"enable_thinking": true}', which Poolside recommends for agentic coding
  5. Pass earlier reasoning back as reasoning_content in the history, since the card says reasoning may stop in later steps if it's dropped
  6. Sampling per the card is temperature 1.0 and top_k 20
  7. The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships
  8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

LFM3-Omni-2B

  1. Start it with llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --jinja or ollama run lfm3-omni
  2. Greedy decoding for text. For speech use audio_temperature 0.4 and audio_repetition_penalty 1.2, per the card
  3. The context is 32K, and llama.cpp samples video at 2 frames a second up to 384 frames
  4. Parse tool calls yourself from the XML it returns
  5. It doesn't reason, so don't pass enable_thinking
  6. Validate JSON output yourself

Questions

Which is better for AI agents, Laguna XS 2.1 or LFM3-Omni-2B?

Laguna XS 2.1 and LFM3-Omni-2B score within a point of each other on agent readiness, 61 (C) and 61 (C).

Other comparisons with Laguna XS 2.1 or LFM3-Omni-2B

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