Head to head · Open-weight models you run yourself · October 2026 research run
LFM3-Omni-2B vs ZAYA1-8B
LFM3-Omni-2B and ZAYA1-8B score within a point of each other on agent readiness, 61 (C) and 61 (C). ZAYA1-8B leads on risk. 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 Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools
Ahead on
- Agent interface, 70 against 60
Watch for
Only llama.cpp and Ollama run it in a release
ZAYA1-8B C
Best for People who want a small, fast reasoning model for maths and code and can live without tool calling
Ahead on
- Risk, 55 against 40
Watch for
Tool calling needs a parser that only Zyphra's vLLM fork has
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 ZAYA1-8B +15
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.
ZAYA1-8B 55
File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the Zyphra organisation, which the card ties to zyphra.com's blog post for the model (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and www.zyphra.com has no security.txt. 25 + 15 + 0 + 0 + 15 + 0 = 55.
Agent interface LFM3-Omni-2B +10
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.
ZAYA1-8B 60
Chat template 20, chat_template.jinja in the repository (10,700 characters). Tool-calling format 10, only the template and a fork say it. Trained tool calling 10, the template takes tools and renders calls, and the card recommends settings for agent use, but it doesn't say the model was trained for tool calling or for several calls in a turn, so the lower band. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch, and the card names the qwen3 reasoning parser, which returns the reasoning apart from the answer. Context 10, 131,072 positions in config.json, for an architecture Zyphra trained end to end, so the config is the model's own. 20 + 10 + 10 + 0 + 10 + 10 = 60.
Score by category
| Category | Weight this run | LFM3-Omni-2B | ZAYA1-8B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 80 | even |
| Risk | 20%20 | 40 | 55 | ZAYA1-8B +15 |
| Agent interface | 10%10 | 70 | 60 | LFM3-Omni-2B +10 |
| Documentation and upkeep | 10%10 | 49 | 50 | ZAYA1-8B +1 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | LFM3-Omni-2B | ZAYA1-8B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | Zyphra |
| 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 | 2026-11-03 | 2026-07-02 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
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.
ZAYA1-8B
A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.
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
ZAYA1-8B
- Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork
- Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in
- Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work
- Download
Zyphra/ZAYA1-8B, not the legacy repository, for the released runtimes - The quantised repositories have no licence tag. The main one is Apache 2.0
Questions
Which is better for AI agents, LFM3-Omni-2B or ZAYA1-8B?
LFM3-Omni-2B and ZAYA1-8B score within a point of each other on agent readiness, 61 (C) and 61 (C). ZAYA1-8B leads on risk.
Other comparisons with LFM3-Omni-2B or ZAYA1-8B
- Agents-A1-4B vs LFM3-Omni-2B
- Agents-A1-4B vs ZAYA1-8B
- Apertus v1.5 8B vs LFM3-Omni-2B
- Apertus v1.5 8B vs ZAYA1-8B
- Apodex-1.1-mini vs LFM3-Omni-2B
- Apodex-1.1-mini vs ZAYA1-8B
- Arete-27B vs LFM3-Omni-2B
- Arete-27B vs ZAYA1-8B
- BigBang-v1 vs LFM3-Omni-2B
- BigBang-v1 vs ZAYA1-8B
- Carbon-X vs LFM3-Omni-2B
- Carbon-X vs ZAYA1-8B
- Cola2 vs LFM3-Omni-2B
- Cola2 vs ZAYA1-8B
- Cosmos-Reason2-2B vs LFM3-Omni-2B
- Cosmos-Reason2-2B vs ZAYA1-8B
- Darwin-36B-Opus vs LFM3-Omni-2B
- Darwin-36B-Opus vs ZAYA1-8B
- DeepSeek-V3.2 vs LFM3-Omni-2B
- DeepSeek-V3.2 vs ZAYA1-8B
- DeepSeek V4.1 Flash vs LFM3-Omni-2B
- DeepSeek V4.1 Flash vs ZAYA1-8B
- DeepSeek-V4-Flash-0731 vs LFM3-Omni-2B
- DeepSeek-V4-Flash-0731 vs ZAYA1-8B
- Devstral Small 2 vs LFM3-Omni-2B
- Devstral Small 2 vs ZAYA1-8B
- DiffusionGemma 26B A4B IT vs LFM3-Omni-2B
- DiffusionGemma 26B A4B IT vs ZAYA1-8B
- EXAONE 4.5 33B vs LFM3-Omni-2B
- EXAONE 4.5 33B vs ZAYA1-8B
- Gemma 4 12B IT vs LFM3-Omni-2B
- Gemma 4 12B IT vs ZAYA1-8B
- Gemma 4 26B A4B IT vs LFM3-Omni-2B
- Gemma 4 26B A4B IT vs ZAYA1-8B
- Gemma 4 31B IT vs LFM3-Omni-2B
- Gemma 4 31B IT vs ZAYA1-8B
- Gemma 4 E2B IT vs LFM3-Omni-2B
- Gemma 4 E2B IT vs ZAYA1-8B
- Gemma 4 E4B IT vs LFM3-Omni-2B
- Gemma 4 E4B IT vs ZAYA1-8B
- GLM-4.5-Air vs LFM3-Omni-2B
- GLM-4.5-Air vs ZAYA1-8B
- GLM-4.7-Flash vs LFM3-Omni-2B
- GLM-4.7-Flash vs ZAYA1-8B
- GLM-5.3-Flash vs LFM3-Omni-2B
- GLM-5.3-Flash vs ZAYA1-8B
- GLM-5.3 vs LFM3-Omni-2B
- GLM-5.3 vs ZAYA1-8B
- gpt-oss-120b vs LFM3-Omni-2B
- gpt-oss-120b vs ZAYA1-8B
- gpt-oss-20b vs LFM3-Omni-2B
- gpt-oss-20b vs ZAYA1-8B
- Granite 4.2 30B vs LFM3-Omni-2B
- Granite 4.2 30B vs ZAYA1-8B
- Granite 4.2 3B vs LFM3-Omni-2B
- Granite 4.2 3B vs ZAYA1-8B
- Granite 4.2 8B vs LFM3-Omni-2B
- Granite 4.2 8B vs ZAYA1-8B
- Hy-Memory-30B-A3B vs LFM3-Omni-2B
- Hy-Memory-30B-A3B vs ZAYA1-8B
- Hy3 vs LFM3-Omni-2B
- Hy3 vs ZAYA1-8B
- Inkling-Small vs LFM3-Omni-2B
- Inkling-Small vs ZAYA1-8B
- Inkling vs LFM3-Omni-2B
- Inkling vs ZAYA1-8B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-Omni-2B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs ZAYA1-8B
- Kimi K3.1 Pro vs LFM3-Omni-2B
- Kimi K3.1 Pro vs ZAYA1-8B
- Kimi K3 vs LFM3-Omni-2B
- Kimi K3 vs ZAYA1-8B
- Kolibri-1 vs LFM3-Omni-2B
- Kolibri-1 vs ZAYA1-8B
- Kronos vs LFM3-Omni-2B
- Kronos vs ZAYA1-8B
- Laguna M.1 vs LFM3-Omni-2B
- Laguna M.1 vs ZAYA1-8B
- Laguna S 2.1 vs LFM3-Omni-2B
- Laguna S 2.1 vs ZAYA1-8B
- Laguna XS 2.1 vs LFM3-Omni-2B
- Laguna XS 2.1 vs ZAYA1-8B
- LBS-27B vs LFM3-Omni-2B
- LBS-27B vs ZAYA1-8B
- LFM2.5-2.6B vs LFM3-Omni-2B
- LFM2.5-2.6B vs ZAYA1-8B
- LFM3-1.7B vs LFM3-Omni-2B
- LFM3-1.7B vs ZAYA1-8B
- LFM3-Audio-2B vs LFM3-Omni-2B
- LFM3-Audio-2B vs ZAYA1-8B
- LFM3-Omni-2B vs LFM3-VL-2B
- LFM3-Omni-2B vs Ling-3.0-tiny
- LFM3-Omni-2B vs Ling-3.1-flash-1019
- LFM3-Omni-2B vs LongCat-2.5
- LFM3-Omni-2B vs MiMo-V2.6 Distill Qwen 9B
- LFM3-Omni-2B vs MiMo-V2.6-Flash-RL
- LFM3-Omni-2B vs MiniCPM-o 4.5
- LFM3-Omni-2B vs MiniCPM-V 4.7 1B
- LFM3-Omni-2B vs MiniCPM-V 4.7 35B-A3B
- LFM3-Omni-2B vs MiniCPM5-2B
- LFM3-Omni-2B vs MiniMax-M2.5
- LFM3-Omni-2B vs MiniMax-M2.7
- LFM3-Omni-2B vs MiniMax-M3.1-Flash
- LFM3-Omni-2B vs Mistral Large 4
- LFM3-Omni-2B vs Muse Glimmer 30B
- LFM3-Omni-2B vs Nanbeige4.2-3B
- LFM3-Omni-2B vs Nanbeige4.3-33B
- LFM3-Omni-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- LFM3-Omni-2B vs Nemotron 3.5 Lightning 30B A3B
- LFM3-Omni-2B vs Nemotron 3 Nano 30B A3B
- LFM3-Omni-2B vs Nemotron 3 Nano 4B
- LFM3-Omni-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- LFM3-Omni-2B vs Nemotron 3 Super 120B A12B
- LFM3-Omni-2B vs Nemotron 3 Ultra 550B A55B
- LFM3-Omni-2B vs North Mini Code 1.0
- LFM3-Omni-2B vs occamy-1.0
- LFM3-Omni-2B vs Olmo 3 7B Think
- LFM3-Omni-2B vs Ornith-1.5-122B
- LFM3-Omni-2B vs Ornith-1.5-35B-A3B
- LFM3-Omni-2B vs Ornith-1.5-397B
- LFM3-Omni-2B vs Ornith-1.5-9B
- LFM3-Omni-2B vs OTel-2.0-LLM-31B-IT
- LFM3-Omni-2B vs Qwen-AgentWorld-35B-A3B
- LFM3-Omni-2B vs Qwen3-30B-A3B-Instruct-2507
- LFM3-Omni-2B vs Qwen3-4B-Instruct-2507
- LFM3-Omni-2B vs Qwen3.5-0.8B
- LFM3-Omni-2B vs Qwen3.5-122B-A10B
- LFM3-Omni-2B vs Qwen3.5-2B
- LFM3-Omni-2B vs Qwen3.5-397B-A17B
- LFM3-Omni-2B vs Qwen3.5-4B
- LFM3-Omni-2B vs Qwen3.5-9B
- LFM3-Omni-2B vs Qwen3.6-27B
- LFM3-Omni-2B vs Qwen3.6-35B-A3B
- LFM3-Omni-2B vs Qwen3.8-27B
- LFM3-Omni-2B vs Qwen3.8-Flash-Next
- LFM3-Omni-2B vs Qwen3-Coder-30B-A3B-Instruct
- LFM3-Omni-2B vs Qwen3-Coder-Next
- LFM3-Omni-2B vs Qwen3-Omni-30B-A3B-Instruct
- LFM3-Omni-2B vs Qwen3-VL-2B-Instruct
- LFM3-Omni-2B vs Qwen3-VL-30B-A3B-Instruct
- LFM3-Omni-2B vs Qwen3-VL-32B-Instruct
- LFM3-Omni-2B vs Qwen3-VL-4B-Instruct
- LFM3-Omni-2B vs Qwen3-VL-8B-Instruct
- LFM3-Omni-2B vs Qwopus3.8-27B-Flash-V2
- LFM3-Omni-2B vs Recursive Flint
- LFM3-Omni-2B vs Ripple-65B Preview
- LFM3-Omni-2B vs SenseNova-Loop-9B-Preview
- LFM3-Omni-2B vs SmolLM3-3B
- LFM3-Omni-2B vs Spark-X2.6-4B
- LFM3-Omni-2B vs Step 3.7 Flash
- LFM3-Omni-2B vs Swift 1.5 Qwen3.8-27B
- LFM3-Omni-2B vs Swift 1.5 Qwen3.8-Flash-Next
- LFM3-Omni-2B vs ThinkingCap (Qwen3.8 27B)
- LFM3-Omni-2B vs Baidu Unlimited-OCR-2
- LFM3-Omni-2B vs WeVisOne-VL-2B
- LFM3-Omni-2B vs WeVisOne-VL-4B
- LFM3-VL-2B vs ZAYA1-8B
- Ling-3.0-tiny vs ZAYA1-8B
- Ling-3.1-flash-1019 vs ZAYA1-8B
- LongCat-2.5 vs ZAYA1-8B
- MiMo-V2.6 Distill Qwen 9B vs ZAYA1-8B
- MiMo-V2.6-Flash-RL vs ZAYA1-8B
- MiniCPM-o 4.5 vs ZAYA1-8B
- MiniCPM-V 4.7 1B vs ZAYA1-8B
- MiniCPM-V 4.7 35B-A3B vs ZAYA1-8B
- MiniCPM5-2B vs ZAYA1-8B
- MiniMax-M2.5 vs ZAYA1-8B
- MiniMax-M2.7 vs ZAYA1-8B
- MiniMax-M3.1-Flash vs ZAYA1-8B
- Mistral Large 4 vs ZAYA1-8B
- Muse Glimmer 30B vs ZAYA1-8B
- Nanbeige4.2-3B vs ZAYA1-8B
- Nanbeige4.3-33B vs ZAYA1-8B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs ZAYA1-8B
- Nemotron 3.5 Lightning 30B A3B vs ZAYA1-8B
- Nemotron 3 Nano 30B A3B vs ZAYA1-8B
- Nemotron 3 Nano 4B vs ZAYA1-8B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs ZAYA1-8B
- Nemotron 3 Super 120B A12B vs ZAYA1-8B
- Nemotron 3 Ultra 550B A55B vs ZAYA1-8B
- North Mini Code 1.0 vs ZAYA1-8B
- occamy-1.0 vs ZAYA1-8B
- Olmo 3 7B Think vs ZAYA1-8B
- Ornith-1.5-122B vs ZAYA1-8B
- Ornith-1.5-35B-A3B vs ZAYA1-8B
- Ornith-1.5-397B vs ZAYA1-8B
- Ornith-1.5-9B vs ZAYA1-8B
- OTel-2.0-LLM-31B-IT vs ZAYA1-8B
- Qwen-AgentWorld-35B-A3B vs ZAYA1-8B
- Qwen3-30B-A3B-Instruct-2507 vs ZAYA1-8B
- Qwen3-4B-Instruct-2507 vs ZAYA1-8B
- Qwen3.5-0.8B vs ZAYA1-8B
- Qwen3.5-122B-A10B vs ZAYA1-8B
- Qwen3.5-2B vs ZAYA1-8B
- Qwen3.5-397B-A17B vs ZAYA1-8B
- Qwen3.5-4B vs ZAYA1-8B
- Qwen3.5-9B vs ZAYA1-8B
- Qwen3.6-27B vs ZAYA1-8B
- Qwen3.6-35B-A3B vs ZAYA1-8B
- Qwen3.8-27B vs ZAYA1-8B
- Qwen3.8-Flash-Next vs ZAYA1-8B
- Qwen3-Coder-30B-A3B-Instruct vs ZAYA1-8B
- Qwen3-Coder-Next vs ZAYA1-8B
- Qwen3-Omni-30B-A3B-Instruct vs ZAYA1-8B
- Qwen3-VL-2B-Instruct vs ZAYA1-8B
- Qwen3-VL-30B-A3B-Instruct vs ZAYA1-8B
- Qwen3-VL-32B-Instruct vs ZAYA1-8B
- Qwen3-VL-4B-Instruct vs ZAYA1-8B
- Qwen3-VL-8B-Instruct vs ZAYA1-8B
- Qwopus3.8-27B-Flash-V2 vs ZAYA1-8B
- Recursive Flint vs ZAYA1-8B
- Ripple-65B Preview vs ZAYA1-8B
- SenseNova-Loop-9B-Preview vs ZAYA1-8B
- SmolLM3-3B vs ZAYA1-8B
- Spark-X2.6-4B vs ZAYA1-8B
- Step 3.7 Flash vs ZAYA1-8B
- Swift 1.5 Qwen3.8-27B vs ZAYA1-8B
- Swift 1.5 Qwen3.8-Flash-Next vs ZAYA1-8B
- ThinkingCap (Qwen3.8 27B) vs ZAYA1-8B
- Baidu Unlimited-OCR-2 vs ZAYA1-8B
- WeVisOne-VL-2B vs ZAYA1-8B
- WeVisOne-VL-4B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/lfm3-omni-2b-vs-zaya1-8b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm3-omni-2b.json·/api/v1/tools/zaya1-8b.json - From a terminal
anchor compare lfm3-omni-2b zaya1-8b(the CLI) - Over MCP
compare_tools {"a": "lfm3-omni-2b", "b": "zaya1-8b"}at/mcp, no key