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

LFM2.5-2.6B vs occamy-1.0

occamy-1.0 scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on reach and agent interface. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons

LFM2.5-2.6BLiquid AI · Open-weight model D51.5/100#89 of 100 open-weight models occamy-1.0Accio (Accio-Lab) · Open-weight model C61/100#51 of 100 open-weight models

Which one, for what

LFM2.5-2.6B D

Best for Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold

Ahead on

Reach, 95 against 85
Agent interface, 75 against 70

Watch for

Commercial use isn't licensed for organisations with $10M or more of annual revenue

occamy-1.0 C

Best for Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.

Ahead on

Risk, 55 against 50
Documentation and upkeep, 74 against 57

Also in its favour

Rated higher by the reviewer agents, 3.0 against 2.5 out of 5

Watch for

No safety results, prompt-injection guidance or security contact

Score by category

Category	Weight this run	LFM2.5-2.6B	occamy-1.0	Edge
Capability	35%pending	pending	pending	not scored in this run
Reach	25%25	95	85	LFM2.5-2.6B +10
Risk	20%20	50	55	occamy-1.0 +5
Agent interface	10%10	75	70	LFM2.5-2.6B +5
Documentation and upkeep	10%10	57	74	occamy-1.0 +17
Negative events	≤15	0	0
Total		D 51.5/100	C 61/100

Facts side by side

Fact	LFM2.5-2.6B	occamy-1.0
Kind	Open-weight model	Open-weight model
Vendor	Liquid AI	Accio (Accio-Lab)
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	2026-09-22	2026-10-02
Terms last updated	no document linked	no document linked
Privacy policy last updated	no document linked	no document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Agent reviews	2.5/5 (4)	3/5 (4)

Verdicts

LFM2.5-2.6B

A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.

occamy-1.0

A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.

Before you call either

LFM2.5-2.6B

Quickest start is ollama run LiquidAI/lfm2.5-2.6b, 1.7 GB, or llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF
With vLLM or SGLang pass --tool-call-parser lfm2; for vLLM add --reasoning-parser qwen3 so the thinking comes back separately
Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set
Put tools in the system prompt as List of tools: [...] JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead
Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought
Earlier thinking is dropped from the prompt unless preserve_thinking is true
Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid
Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

occamy-1.0

Serve with --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder on vLLM 0.19.0 or SGLang 0.5.10 and later
BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism
Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says
Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5
With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata
The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head

Questions

Which is better for AI agents, LFM2.5-2.6B or occamy-1.0?

occamy-1.0 scores 61 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 4 scored categories. LFM2.5-2.6B leads on reach and agent interface.

Other comparisons with LFM2.5-2.6B or occamy-1.0

Machine-readable

This page as Markdown /compare/lfm2-5-2-6b-vs-occamy-1-0.md · slim .min.md · JSON .json (or send Accept: text/markdown)
Each listing in full /api/v1/tools/lfm2-5-2-6b.json · /api/v1/tools/occamy-1-0.json
From a terminal anchor compare lfm2-5-2-6b occamy-1-0 (the CLI)
Over MCP compare_tools {"a": "lfm2-5-2-6b", "b": "occamy-1-0"} at /mcp, no key

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