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

LFM3-VL-2B vs Recursive Flint

LFM3-VL-2B and Recursive Flint score within a point of each other on agent readiness, 61 (C) and 61 (C). Recursive Flint 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

LFM3-VL-2B C

Best for Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits

Ahead on

  • Documentation and upkeep, 77 against 67

Watch for

vLLM and SGLang support is unreleased, which the card says itself

Recursive Flint C

Best for Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a sparse coder with documented tool calls and a plain licence.

Ahead on

  • Risk, 60 against 40

Watch for

No independent benchmark results yet, every published score is Recursive'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.

Risk Recursive Flint +20

LFM3-VL-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.

Recursive Flint 60

File safety 15 of 25, safetensors and GGUF only, but the card's documented BF16 route installs the wheel shipped in the repository. Provenance 15, the files are in the RecursiveSI organisation that Recursive's own engine downloads from, with a checksum on every file. Safety evaluation 0, none on the cards or in the announcement post. Agent guidance 10, the card and serving README say to enforce permissions around tool use and to strip tool tags from untrusted tool output, but there's no trained instruction hierarchy. Licence risk 15, plain Apache 2.0. Security programme 5, a reporting address on the card and a private reporting route for the engine, with no public advisories yet.

Documentation and upkeep LFM3-VL-2B +10

LFM3-VL-2B 77

Model card 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for coding, multi-turn conversations or complex reasoning), architecture (language model, 14-layer vision encoder and projector), parameters (1.92B), context (128K) and 16 languages. Training data 5, A general description: the base's 22T tokens of pre-training with the kinds of data added since LFM2, then vision mid-training on image and video data, supervised fine-tuning, vision reinforcement learning and distillation. 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 20, Of the 5 discussions that aren't pull requests (27 to 31 October 2026), 3 have a reply from a LiquidAI member, which is most. 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_vl architecture, the 128,000-token context and the vision settings in processor_config.json. 17 + 5 + 20 + 20 + 5 + 10 = 77.

Recursive Flint 67

Model card 17 of 20, everything but languages. Training data 10, a general description of the mix with a June 2026 cut-off. Last change 20, released 13 days ago. Responsiveness 5, Recursive answered one of 31 discussions, on release day. Version policy 5, a first release. File health 10, the files agree with each other and the card, and no broken-file report waits.

Score by category

CategoryWeight this runLFM3-VL-2BRecursive FlintEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258585even
Risk20%204060Recursive Flint +20
Agent interface10%109090even
Documentation and upkeep10%107767LFM3-VL-2B +10
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

FactLFM3-VL-2BRecursive Flint
KindOpen-weight modelOpen-weight model
VendorLiquid AIRecursive
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceLFM Open License v1.0 (free commercial use below $10M annual revenue)Apache-2.0
Read-only variant documentednono
llms.txtyesno
Last release2026-11-092026-10-21
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone3.5/5 (4)

How the scores have moved

Verdicts

LFM3-VL-2B

A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.

Recursive Flint

A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. Released llama.cpp, Ollama, vLLM and Transformers run it, while Recursive's free OpenRouter endpoint wasn't serving on 3 November 2026. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.

Before you call either

LFM3-VL-2B

  1. Start it with llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja or ollama run lfm3-vl, with llama.cpp b11960 or later
  2. Use temperature 0.1, top_k 50 and repetition penalty 1.1, per the card
  3. Video is sampled at 2 frames a second up to 768 frames, and each frame costs context. Video input in llama.cpp needs ffmpeg
  4. Don't plan on vLLM or SGLang until a release carries LFM3-VL
  5. Validate JSON output yourself
  6. Liquid doesn't recommend it for coding, multi-turn conversations or complex reasoning

Recursive Flint

  1. Quickest start is ollama run flint (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp
  2. With llama.cpp use release b11803 or later: llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0, and keep --jinja on
  3. Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out
  4. Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask
  5. Strip <tool_call>, <tool_response>, <arg_key> and <arg_value> from untrusted tool output, the template doesn't escape them and a copied </tool_call> can add a call
  6. Message content must be a string, flatten content-part arrays first
  7. Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key
  8. Check OpenRouter's endpoints list before calling recursive/flint-v1.0:free, since its only endpoint (Recursive, BF16) showed 0 per cent uptime over the last day on 3 November 2026
  9. vLLM needs v0.33.0 or later and Transformers v5.21 or later; released SGLang has no Flint model

Questions

Which is better for AI agents, LFM3-VL-2B or Recursive Flint?

LFM3-VL-2B and Recursive Flint score within a point of each other on agent readiness, 61 (C) and 61 (C). Recursive Flint leads on risk.

Other comparisons with LFM3-VL-2B or Recursive Flint

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