Head to head · Open-weight models you run yourself · October 2026 research run
LFM3-Audio-2B vs Spark-X2.6-4B
LFM3-Audio-2B and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on reach and 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
Best for Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model
Ahead on
- Agent interface, 85 against 70
Watch for
Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang
Best for Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.
Ahead on
- Reach, 85 against 80
- Risk, 50 against 40
Watch for
No safety results and no agent or prompt-injection guidance
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.
Agent interface LFM3-Audio-2B +15
LFM3-Audio-2B 85
Chat template 20, chat_template.jinja in the repository (9,328 characters), with roles, 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 names turning spoken requests into tool calls as one of its four uses. Structured output 5, the card says the model is designed for structured outputs without saying how to ask for JSON. Liquid's API guide documents response_format with lfm3-1.7b as its example. Reasoning control 10, the card documents reasoning per request for text replies: the caller prefills the assistant turn with an open think tag to let the model reason, or a closed one for a direct answer, with a use_thinking example. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 5 + 10 + 5 = 85.
Spark-X2.6-4B 70
Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the spark25 parser name only in the SGLang command. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request, a low or high effort is set in the template and the qwen3 parser returns the reasoning apart. Context 10, 1,048,576 tokens with no scaling.
Risk Spark-X2.6-4B +10
LFM3-Audio-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.
Spark-X2.6-4B 50
File safety 15 of 25, safetensors and GGUF only, but the card's Transformers and vLLM routes run the repository's Python. Provenance 15, the files are in SparkLLM's own Hub organisation, which its GitHub docs name, and every file has a checksum, though neither organisation is verified and no company site was found. Safety evaluation 0 and agent guidance 0, none published. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 5 of 10, a private reporting route and email in SparkLLM's community repository, with no public advisories.
Reach Spark-X2.6-4B +5
LFM3-Audio-2B 80
Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.17 GB projector file, and 1.27 GB × 1.2 = 1.5 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 audio projector and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example that starts this model), and Ollama's library has an lfm3-audio entry. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-audio-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.
Spark-X2.6-4B 85
Memory 30, SparkLLM's Q4_K_M GGUF is 3.19 GB, about 3.8 GB with a fifth added, which fits 8 GB. Runtimes 15 of 25, released llama.cpp, SGLang (to v0.5.23) and Ollama run the spark2_5 architecture X2.6 keeps, while vLLM needs SparkLLM's plugin, Transformers needs the repository's code and LM Studio has no catalogue entry. Builds 15, SparkLLM publishes FP8, INT8, INT4 and GGUF builds. Terms 15, Apache 2.0 with nothing added, on its own base. Download 10, nothing is gated. Hosted 0, no provider or SparkLLM API serves it.
Score by category
| Category | Weight this run | LFM3-Audio-2B | Spark-X2.6-4B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 85 | Spark-X2.6-4B +5 |
| Risk | 20%20 | 40 | 50 | Spark-X2.6-4B +10 |
| Agent interface | 10%10 | 85 | 70 | LFM3-Audio-2B +15 |
| Documentation and upkeep | 10%10 | 52 | 51 | LFM3-Audio-2B +1 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | LFM3-Audio-2B | Spark-X2.6-4B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | SparkLLM (XHToken) |
| 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 | 2026-10-20 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3/5 (4) |
How the scores have moved
LFM3-Audio-2B
No score change since it was listed.
Verdicts
LFM3-Audio-2B
A 2B model that listens and speaks on an 8 GB machine, transcribes and translates 19 languages and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, voice chat is English only, Liquid hasn't answered the repository's questions, and there are no safety results or data sources.
Spark-X2.6-4B
A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin card with no training data.
Before you call either
LFM3-Audio-2B
- Start it with
llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinjaorollama run lfm3-audio - llama.cpp ships examples/realtime_client.py for live speech with this model
- Prefill the assistant turn with an open think tag to let it reason before a text reply
- The context is 32K
- Voice chat is English only, while transcription, synthesis and translation cover 19 languages and locales
- Liquid doesn't recommend it for agentic coding or complex multi-step reasoning
Spark-X2.6-4B
- Quickest start is
llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja, which needs llama.cpp b10828 or later, orollama run SparkLLM/Spark-X2.6-4B(8.2 GB, Ollama 0.34.1 or later) - For parsed tool calls serve it with SGLang 0.5.22 or later,
--tool-call-parser spark25 --reasoning-parser qwen3, since the calls come back as tool_call blocks with arg_key and arg_value pairs - Thinking is on by default. Pass
chat_template_kwargswithenable_thinkingfalse to turn it off, orreasoning_effortlow for short thinking; the template raises an error on any effort other than high or low - SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1
- The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list
- The template raises an error if tool_call arguments are a JSON string, so pass them as an object
- Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights
Questions
Which is better for AI agents, LFM3-Audio-2B or Spark-X2.6-4B?
LFM3-Audio-2B and Spark-X2.6-4B score within a point of each other on agent readiness, 61 (C) and 61 (C). Spark-X2.6-4B leads on reach and risk.
Other comparisons with LFM3-Audio-2B or Spark-X2.6-4B
- Agents-A1-4B vs LFM3-Audio-2B
- Agents-A1-4B vs Spark-X2.6-4B
- Apertus v1.5 8B vs LFM3-Audio-2B
- Apertus v1.5 8B vs Spark-X2.6-4B
- Apodex-1.1-mini vs LFM3-Audio-2B
- Apodex-1.1-mini vs Spark-X2.6-4B
- Arete-27B vs LFM3-Audio-2B
- Arete-27B vs Spark-X2.6-4B
- BigBang-v1 vs LFM3-Audio-2B
- BigBang-v1 vs Spark-X2.6-4B
- Carbon-X vs LFM3-Audio-2B
- Carbon-X vs Spark-X2.6-4B
- Cosmos-Reason2-2B vs LFM3-Audio-2B
- Cosmos-Reason2-2B vs Spark-X2.6-4B
- Darwin-36B-Opus vs LFM3-Audio-2B
- Darwin-36B-Opus vs Spark-X2.6-4B
- DeepSeek-V3.2 vs LFM3-Audio-2B
- DeepSeek-V3.2 vs Spark-X2.6-4B
- DeepSeek V4.1 Flash vs LFM3-Audio-2B
- DeepSeek V4.1 Flash vs Spark-X2.6-4B
- DeepSeek-V4-Flash-0731 vs LFM3-Audio-2B
- DeepSeek-V4-Flash-0731 vs Spark-X2.6-4B
- DiffusionGemma 26B A4B IT vs LFM3-Audio-2B
- DiffusionGemma 26B A4B IT vs Spark-X2.6-4B
- Gemma 4 12B IT vs LFM3-Audio-2B
- Gemma 4 12B IT vs Spark-X2.6-4B
- Gemma 4 26B A4B IT vs LFM3-Audio-2B
- Gemma 4 26B A4B IT vs Spark-X2.6-4B
- Gemma 4 31B IT vs LFM3-Audio-2B
- Gemma 4 31B IT vs Spark-X2.6-4B
- Gemma 4 E2B IT vs LFM3-Audio-2B
- Gemma 4 E2B IT vs Spark-X2.6-4B
- Gemma 4 E4B IT vs LFM3-Audio-2B
- Gemma 4 E4B IT vs Spark-X2.6-4B
- GLM-4.5-Air vs LFM3-Audio-2B
- GLM-4.5-Air vs Spark-X2.6-4B
- GLM-4.7-Flash vs LFM3-Audio-2B
- GLM-4.7-Flash vs Spark-X2.6-4B
- GLM-5.3-Flash vs LFM3-Audio-2B
- GLM-5.3-Flash vs Spark-X2.6-4B
- GLM-5.3 vs LFM3-Audio-2B
- GLM-5.3 vs Spark-X2.6-4B
- gpt-oss-120b vs LFM3-Audio-2B
- gpt-oss-120b vs Spark-X2.6-4B
- gpt-oss-20b vs LFM3-Audio-2B
- gpt-oss-20b vs Spark-X2.6-4B
- Granite 4.2 30B vs LFM3-Audio-2B
- Granite 4.2 30B vs Spark-X2.6-4B
- Granite 4.2 3B vs LFM3-Audio-2B
- Granite 4.2 3B vs Spark-X2.6-4B
- Granite 4.2 8B vs LFM3-Audio-2B
- Granite 4.2 8B vs Spark-X2.6-4B
- Hy-Memory-30B-A3B vs LFM3-Audio-2B
- Hy-Memory-30B-A3B vs Spark-X2.6-4B
- Hy3 vs LFM3-Audio-2B
- Hy3 vs Spark-X2.6-4B
- Inkling-Small vs LFM3-Audio-2B
- Inkling-Small vs Spark-X2.6-4B
- Inkling vs LFM3-Audio-2B
- Inkling vs Spark-X2.6-4B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-Audio-2B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Spark-X2.6-4B
- Kimi K3.1 Pro vs LFM3-Audio-2B
- Kimi K3.1 Pro vs Spark-X2.6-4B
- Kimi K3 vs LFM3-Audio-2B
- Kimi K3 vs Spark-X2.6-4B
- Kolibri-1 vs LFM3-Audio-2B
- Kolibri-1 vs Spark-X2.6-4B
- Kronos vs LFM3-Audio-2B
- Kronos vs Spark-X2.6-4B
- Laguna M.1 vs LFM3-Audio-2B
- Laguna M.1 vs Spark-X2.6-4B
- Laguna S 2.1 vs LFM3-Audio-2B
- Laguna S 2.1 vs Spark-X2.6-4B
- Laguna XS 2.1 vs LFM3-Audio-2B
- Laguna XS 2.1 vs Spark-X2.6-4B
- LBS-27B vs LFM3-Audio-2B
- LBS-27B vs Spark-X2.6-4B
- LFM2.5-2.6B vs LFM3-Audio-2B
- LFM2.5-2.6B vs Spark-X2.6-4B
- LFM3-1.7B vs LFM3-Audio-2B
- LFM3-1.7B vs Spark-X2.6-4B
- LFM3-Audio-2B vs LFM3-Omni-2B
- LFM3-Audio-2B vs LFM3-VL-2B
- LFM3-Audio-2B vs Ling-3.0-tiny
- LFM3-Audio-2B vs Ling-3.1-flash-1019
- LFM3-Audio-2B vs MiMo-V2.6 Distill Qwen 9B
- LFM3-Audio-2B vs MiniCPM-o 4.5
- LFM3-Audio-2B vs MiniCPM-V 4.7 1B
- LFM3-Audio-2B vs MiniCPM-V 4.7 35B-A3B
- LFM3-Audio-2B vs MiniCPM5-2B
- LFM3-Audio-2B vs MiniMax-M2.5
- LFM3-Audio-2B vs MiniMax-M2.7
- LFM3-Audio-2B vs MiniMax-M3.1-Flash
- LFM3-Audio-2B vs Mistral Large 4
- LFM3-Audio-2B vs Muse Glimmer 30B
- LFM3-Audio-2B vs Nanbeige4.3-33B
- LFM3-Audio-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- LFM3-Audio-2B vs Nemotron 3.5 Lightning 30B A3B
- LFM3-Audio-2B vs Nemotron 3 Nano 30B A3B
- LFM3-Audio-2B vs Nemotron 3 Nano 4B
- LFM3-Audio-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- LFM3-Audio-2B vs Nemotron 3 Super 120B A12B
- LFM3-Audio-2B vs Nemotron 3 Ultra 550B A55B
- LFM3-Audio-2B vs occamy-1.0
- LFM3-Audio-2B vs Olmo 3 7B Think
- LFM3-Audio-2B vs Ornith-1.5-122B
- LFM3-Audio-2B vs Ornith-1.5-35B-A3B
- LFM3-Audio-2B vs Ornith-1.5-397B
- LFM3-Audio-2B vs Ornith-1.5-9B
- LFM3-Audio-2B vs Qwen-AgentWorld-35B-A3B
- LFM3-Audio-2B vs Qwen3-30B-A3B-Instruct-2507
- LFM3-Audio-2B vs Qwen3-4B-Instruct-2507
- LFM3-Audio-2B vs Qwen3.5-0.8B
- LFM3-Audio-2B vs Qwen3.5-122B-A10B
- LFM3-Audio-2B vs Qwen3.5-2B
- LFM3-Audio-2B vs Qwen3.5-397B-A17B
- LFM3-Audio-2B vs Qwen3.5-4B
- LFM3-Audio-2B vs Qwen3.5-9B
- LFM3-Audio-2B vs Qwen3.6-27B
- LFM3-Audio-2B vs Qwen3.6-35B-A3B
- LFM3-Audio-2B vs Qwen3.8-27B
- LFM3-Audio-2B vs Qwen3.8-Flash-Next
- LFM3-Audio-2B vs Qwen3-Coder-30B-A3B-Instruct
- LFM3-Audio-2B vs Qwen3-Coder-Next
- LFM3-Audio-2B vs Qwen3-Omni-30B-A3B-Instruct
- LFM3-Audio-2B vs Qwen3-VL-2B-Instruct
- LFM3-Audio-2B vs Qwen3-VL-30B-A3B-Instruct
- LFM3-Audio-2B vs Qwen3-VL-32B-Instruct
- LFM3-Audio-2B vs Qwen3-VL-4B-Instruct
- LFM3-Audio-2B vs Qwen3-VL-8B-Instruct
- LFM3-Audio-2B vs Qwopus3.8-27B-Flash-V2
- LFM3-Audio-2B vs Recursive Flint
- LFM3-Audio-2B vs Ripple-65B Preview
- LFM3-Audio-2B vs SmolLM3-3B
- LFM3-Audio-2B vs Swift 1.5 Qwen3.8-27B
- LFM3-Audio-2B vs Swift 1.5 Qwen3.8-Flash-Next
- LFM3-Audio-2B vs ThinkingCap (Qwen3.8 27B)
- LFM3-Audio-2B vs Baidu Unlimited-OCR-2
- LFM3-Omni-2B vs Spark-X2.6-4B
- LFM3-VL-2B vs Spark-X2.6-4B
- Ling-3.0-tiny vs Spark-X2.6-4B
- Ling-3.1-flash-1019 vs Spark-X2.6-4B
- MiMo-V2.6 Distill Qwen 9B vs Spark-X2.6-4B
- MiniCPM-o 4.5 vs Spark-X2.6-4B
- MiniCPM-V 4.7 1B vs Spark-X2.6-4B
- MiniCPM-V 4.7 35B-A3B vs Spark-X2.6-4B
- MiniCPM5-2B vs Spark-X2.6-4B
- MiniMax-M2.5 vs Spark-X2.6-4B
- MiniMax-M2.7 vs Spark-X2.6-4B
- MiniMax-M3.1-Flash vs Spark-X2.6-4B
- Mistral Large 4 vs Spark-X2.6-4B
- Muse Glimmer 30B vs Spark-X2.6-4B
- Nanbeige4.3-33B vs Spark-X2.6-4B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Spark-X2.6-4B
- Nemotron 3.5 Lightning 30B A3B vs Spark-X2.6-4B
- Nemotron 3 Nano 30B A3B vs Spark-X2.6-4B
- Nemotron 3 Nano 4B vs Spark-X2.6-4B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Spark-X2.6-4B
- Nemotron 3 Super 120B A12B vs Spark-X2.6-4B
- Nemotron 3 Ultra 550B A55B vs Spark-X2.6-4B
- occamy-1.0 vs Spark-X2.6-4B
- Olmo 3 7B Think vs Spark-X2.6-4B
- Ornith-1.5-122B vs Spark-X2.6-4B
- Ornith-1.5-35B-A3B vs Spark-X2.6-4B
- Ornith-1.5-397B vs Spark-X2.6-4B
- Ornith-1.5-9B vs Spark-X2.6-4B
- Qwen-AgentWorld-35B-A3B vs Spark-X2.6-4B
- Qwen3-30B-A3B-Instruct-2507 vs Spark-X2.6-4B
- Qwen3-4B-Instruct-2507 vs Spark-X2.6-4B
- Qwen3.5-0.8B vs Spark-X2.6-4B
- Qwen3.5-122B-A10B vs Spark-X2.6-4B
- Qwen3.5-2B vs Spark-X2.6-4B
- Qwen3.5-397B-A17B vs Spark-X2.6-4B
- Qwen3.5-4B vs Spark-X2.6-4B
- Qwen3.5-9B vs Spark-X2.6-4B
- Qwen3.6-27B vs Spark-X2.6-4B
- Qwen3.6-35B-A3B vs Spark-X2.6-4B
- Qwen3.8-27B vs Spark-X2.6-4B
- Qwen3.8-Flash-Next vs Spark-X2.6-4B
- Qwen3-Coder-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-Coder-Next vs Spark-X2.6-4B
- Qwen3-Omni-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-2B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-32B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-4B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-8B-Instruct vs Spark-X2.6-4B
- Qwopus3.8-27B-Flash-V2 vs Spark-X2.6-4B
- Recursive Flint vs Spark-X2.6-4B
- Ripple-65B Preview vs Spark-X2.6-4B
- SmolLM3-3B vs Spark-X2.6-4B
- Spark-X2.6-4B vs Swift 1.5 Qwen3.8-27B
- Spark-X2.6-4B vs Swift 1.5 Qwen3.8-Flash-Next
- Spark-X2.6-4B vs ThinkingCap (Qwen3.8 27B)
- Spark-X2.6-4B vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/lfm3-audio-2b-vs-spark-x2-6-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm3-audio-2b.json·/api/v1/tools/spark-x2-6-4b.json - From a terminal
anchor compare lfm3-audio-2b spark-x2-6-4b(the CLI) - Over MCP
compare_tools {"a": "lfm3-audio-2b", "b": "spark-x2-6-4b"}at/mcp, no key