Head to head · Open-weight models you run yourself · October 2026 research run
LFM3-1.7B vs SmolLM3-3B
LFM3-1.7B and SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B leads on risk and documentation and upkeep. 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
- Agent interface, 100 against 80
Watch for
vLLM and SGLang have no released support, though the card lists both
Best for Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device
Ahead on
- Risk, 65 against 40
- Documentation and upkeep, 74 against 67
Watch for
65,536 tokens trained; 131,072 needs a YaRN edit to config.json
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 SmolLM3-3B +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.
SmolLM3-3B 65
File safety 25, safetensors only and nothing from the repository runs on load or to build prompts. Provenance 15, the HuggingFaceTB organisation linked from Hugging Face's launch post, with a SHA-256 for each shard. Safety evaluation 0, none published. Agent guidance 5, only the general cautions in the card's Limitations section. Licence risk 15, Apache 2.0 with nothing added and a working licence link. Security programme 5, huggingface.co has a valid security.txt and Transformers has a security policy; whether advisories are handled in public couldn't be checked from our session, so that half is scored as absent.
Agent interface LFM3-1.7B +20
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.
SmolLM3-3B 80
Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools are declared, how calls come back and which vLLM parser to use. Trained tool calling 20, the template invites one or more calls per turn, each in its own block. Structured output 0, nothing documents asking the weights for JSON. Reasoning control 10, thinking turns off per request through the kwarg or a system-prompt flag, and the reasoning comes back in think tags. Context 5, trained at 65,536 tokens, with 131,072 only through YaRN.
Documentation and upkeep SmolLM3-3B +7
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.
SmolLM3-3B 74
Model card 14, it covers limitations, architecture, parameters, context and languages but has no intended-use section and leaves the June 2025 cut-off to the chat template. Training data 20, the datasets, mixture weights and post-training set are published with sources and sizes. Last change 20, Hugging Face's ONNX build changed its tokenizer config on 28 October 2026, though the main repository hasn't changed since 10 September 2025. Responsiveness 10, staff answered 14 of 36 discussions, none since November 2025. Version policy 5, a first release never replaced, with no dated notes. File health 5, template and tokenizer reports wait with no reply.
Score by category
| Category | Weight this run | LFM3-1.7B | SmolLM3-3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 85 | even |
| Risk | 20%20 | 40 | 65 | SmolLM3-3B +25 |
| Agent interface | 10%10 | 100 | 80 | LFM3-1.7B +20 |
| Documentation and upkeep | 10%10 | 67 | 74 | SmolLM3-3B +7 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | LFM3-1.7B | SmolLM3-3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | Hugging Face |
| 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) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-09 | 2025-09-10 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3.3/5 (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.
SmolLM3-3B
A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build. The main risks are a 64K native context, no safety results and no staff reply in its discussions since November 2025.
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
SmolLM3-3B
- Start it with
vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes, or tool calls come back as text - Pass
chat_template_kwargswithenable_thinkingfalse for short tasks, since thinking is on by default; a/thinkor/no_thinkflag in the system prompt overrides the kwarg - Put earlier tool calls in the assistant message content, since the template ignores a
tool_callsfield when it renders history - Add the YaRN
rope_scalingblock (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens - Use temperature 0.6 and top_p 0.95, the card's recommended sampling
- Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none
- Validate JSON output yourself or use the runtime's guided decoding
Questions
Which is better for AI agents, LFM3-1.7B or SmolLM3-3B?
LFM3-1.7B and SmolLM3-3B score within a point of each other on agent readiness, 61 (C) and 61 (C). SmolLM3-3B leads on risk and documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/lfm3-1-7b-vs-smollm3-3b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm3-1-7b.json·/api/v1/tools/smollm3-3b.json - From a terminal
anchor compare lfm3-1-7b smollm3-3b(the CLI) - Over MCP
compare_tools {"a": "lfm3-1-7b", "b": "smollm3-3b"}at/mcp, no key