Head to head · Open-weight models you run yourself · October 2026 research run
Apertus v1.5 8B vs LFM3-Audio-2B
Apertus v1.5 8B and LFM3-Audio-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-Audio-2B leads on reach. 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 Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.
Ahead on
- Risk, 75 against 40
- Documentation and upkeep, 84 against 52
Watch for
No released runtime supports it, only Swiss AI's forks of vLLM and Transformers
Best for Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model
Ahead on
- Reach, 80 against 40
Watch for
Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang
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.
Reach LFM3-Audio-2B +40
Apertus v1.5 8B 40
Memory 25 of 30, the BF16 files are 18.4 GB, or 22.1 GB with a fifth added, which fits 24 GB. Runtimes 0, no released vLLM (to 0.33.0), SGLang (to 0.5.23), Transformers (to 5.22.1), llama.cpp, Ollama or LM Studio runs version 1.5 yet, only Swiss AI's forks. Builds 0, Swiss AI publishes no quantised build. Terms 10 of 15, Apache 2.0 with an acceptable use policy added at the gate. Download 5, a click-through that needs an account and your contact details. Hosted 0, no inference provider or publisher API was found, and Swiss AI's documentation page names none on 3 November 2026.
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.
Risk Apertus v1.5 8B +35
Apertus v1.5 8B 75
File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the swiss-ai organisation the report links, with a checksum on every file. Safety evaluation 10 of 20, published safety benchmarks run with Swiss AI's open harnesses and early access for three outside evaluators, but the manual evaluation is Swiss AI's own and can't be rerun. Agent guidance 10, the report says what the refusals cover, how far jailbreaks get and that capability limits are the main defence, with a personal-data filter to apply, but nothing on prompt injection for deployers. Licence risk 10, 5 off for the use policy. Security programme 5, ETH Zurich's disclosure policy, with no advisories found.
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.
Documentation and upkeep Apertus v1.5 8B +32
Apertus v1.5 8B 84
Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 20, every dataset is public and listed with versions, licences and counts. Last change 20, the card changed on 14 October 2026, 20 days before 3 November, and Swiss AI added its own evaluation-results files on 26 October and 3 November. Responsiveness 20, Swiss AI answered all three questions, unchanged on 3 November 2026. Version policy 5, earlier models stay in their own repositories without a dated note. File health 5, the gated config couldn't be checked against the card, and the card and the policy disagree on the output filter.
LFM3-Audio-2B 52
Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), limitations (not recommended for agentic coding or complex multi-step reasoning, voice chat in English only), an architecture section, parameters (2B), context (32K) and 19 speech languages and locales. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 31 October, edited the card. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (27 and 31 October 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 17 + 5 + 20 + 0 + 5 + 5 = 52.
Score by category
| Category | Weight this run | Apertus v1.5 8B | LFM3-Audio-2B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 40 | 80 | LFM3-Audio-2B +40 |
| Risk | 20%20 | 75 | 40 | Apertus v1.5 8B +35 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 84 | 52 | Apertus v1.5 8B +32 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | Apertus v1.5 8B | LFM3-Audio-2B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | Liquid AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | LFM Open License v1.0 (free commercial use below $10M annual revenue) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-10-14 | 2026-11-03 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.8/5 (4) | none |
Verdicts
Apertus v1.5 8B
A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.
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.
Before you call either
Apertus v1.5 8B
- Accept the terms on the card and set HF_TOKEN before downloading
- Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet
- Serve with
--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertusfor tools, and leave thinking off when you need tools - For thinking, set
--reasoning-parser apertusand--default-chat-template-kwargs.enable_thinking true, and budget max tokens generously - Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames
- Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32
- If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent
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
Questions
Which is better for AI agents, Apertus v1.5 8B or LFM3-Audio-2B?
Apertus v1.5 8B and LFM3-Audio-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-Audio-2B leads on reach.
Other comparisons with Apertus v1.5 8B or LFM3-Audio-2B
- Agents-A1-4B vs Apertus v1.5 8B
- Agents-A1-4B vs LFM3-Audio-2B
- Apertus v1.5 8B vs Apodex-1.1-mini
- Apertus v1.5 8B vs Arete-27B
- Apertus v1.5 8B vs BigBang-v1
- Apertus v1.5 8B vs Carbon-X
- Apertus v1.5 8B vs Cosmos-Reason2-2B
- Apertus v1.5 8B vs Darwin-36B-Opus
- Apertus v1.5 8B vs DeepSeek-V3.2
- Apertus v1.5 8B vs DeepSeek V4.1 Flash
- Apertus v1.5 8B vs DeepSeek-V4-Flash-0731
- Apertus v1.5 8B vs DiffusionGemma 26B A4B IT
- Apertus v1.5 8B vs Gemma 4 12B IT
- Apertus v1.5 8B vs Gemma 4 26B A4B IT
- Apertus v1.5 8B vs Gemma 4 31B IT
- Apertus v1.5 8B vs Gemma 4 E2B IT
- Apertus v1.5 8B vs Gemma 4 E4B IT
- Apertus v1.5 8B vs GLM-4.5-Air
- Apertus v1.5 8B vs GLM-4.7-Flash
- Apertus v1.5 8B vs GLM-5.3
- Apertus v1.5 8B vs GLM-5.3-Flash
- Apertus v1.5 8B vs gpt-oss-120b
- Apertus v1.5 8B vs gpt-oss-20b
- Apertus v1.5 8B vs Granite 4.2 30B
- Apertus v1.5 8B vs Granite 4.2 3B
- Apertus v1.5 8B vs Granite 4.2 8B
- Apertus v1.5 8B vs Hy-Memory-30B-A3B
- Apertus v1.5 8B vs Hy3
- Apertus v1.5 8B vs Inkling
- Apertus v1.5 8B vs Inkling-Small
- Apertus v1.5 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Apertus v1.5 8B vs Kimi K3
- Apertus v1.5 8B vs Kimi K3.1 Pro
- Apertus v1.5 8B vs Kolibri-1
- Apertus v1.5 8B vs Kronos
- Apertus v1.5 8B vs Laguna M.1
- Apertus v1.5 8B vs Laguna S 2.1
- Apertus v1.5 8B vs Laguna XS 2.1
- Apertus v1.5 8B vs LBS-27B
- Apertus v1.5 8B vs LFM2.5-2.6B
- Apertus v1.5 8B vs LFM3-1.7B
- Apertus v1.5 8B vs LFM3-Omni-2B
- Apertus v1.5 8B vs LFM3-VL-2B
- Apertus v1.5 8B vs Ling-3.0-tiny
- Apertus v1.5 8B vs Ling-3.1-flash-1019
- Apertus v1.5 8B vs MiMo-V2.6 Distill Qwen 9B
- Apertus v1.5 8B vs MiniCPM-o 4.5
- Apertus v1.5 8B vs MiniCPM-V 4.7 1B
- Apertus v1.5 8B vs MiniCPM-V 4.7 35B-A3B
- Apertus v1.5 8B vs MiniCPM5-2B
- Apertus v1.5 8B vs MiniMax-M2.5
- Apertus v1.5 8B vs MiniMax-M2.7
- Apertus v1.5 8B vs MiniMax-M3.1-Flash
- Apertus v1.5 8B vs Mistral Large 4
- Apertus v1.5 8B vs Muse Glimmer 30B
- Apertus v1.5 8B vs Nanbeige4.3-33B
- Apertus v1.5 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Apertus v1.5 8B vs Nemotron 3.5 Lightning 30B A3B
- Apertus v1.5 8B vs Nemotron 3 Nano 30B A3B
- Apertus v1.5 8B vs Nemotron 3 Nano 4B
- Apertus v1.5 8B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Apertus v1.5 8B vs Nemotron 3 Super 120B A12B
- Apertus v1.5 8B vs Nemotron 3 Ultra 550B A55B
- Apertus v1.5 8B vs occamy-1.0
- Apertus v1.5 8B vs Olmo 3 7B Think
- Apertus v1.5 8B vs Ornith-1.5-122B
- Apertus v1.5 8B vs Ornith-1.5-35B-A3B
- Apertus v1.5 8B vs Ornith-1.5-397B
- Apertus v1.5 8B vs Ornith-1.5-9B
- Apertus v1.5 8B vs Qwen-AgentWorld-35B-A3B
- Apertus v1.5 8B vs Qwen3-30B-A3B-Instruct-2507
- Apertus v1.5 8B vs Qwen3-4B-Instruct-2507
- Apertus v1.5 8B vs Qwen3.5-0.8B
- Apertus v1.5 8B vs Qwen3.5-122B-A10B
- Apertus v1.5 8B vs Qwen3.5-2B
- Apertus v1.5 8B vs Qwen3.5-397B-A17B
- Apertus v1.5 8B vs Qwen3.5-4B
- Apertus v1.5 8B vs Qwen3.5-9B
- Apertus v1.5 8B vs Qwen3.6-27B
- Apertus v1.5 8B vs Qwen3.6-35B-A3B
- Apertus v1.5 8B vs Qwen3.8-27B
- Apertus v1.5 8B vs Qwen3.8-Flash-Next
- Apertus v1.5 8B vs Qwen3-Coder-30B-A3B-Instruct
- Apertus v1.5 8B vs Qwen3-Coder-Next
- Apertus v1.5 8B vs Qwen3-Omni-30B-A3B-Instruct
- Apertus v1.5 8B vs Qwen3-VL-2B-Instruct
- Apertus v1.5 8B vs Qwen3-VL-30B-A3B-Instruct
- Apertus v1.5 8B vs Qwen3-VL-32B-Instruct
- Apertus v1.5 8B vs Qwen3-VL-4B-Instruct
- Apertus v1.5 8B vs Qwen3-VL-8B-Instruct
- Apertus v1.5 8B vs Qwopus3.8-27B-Flash-V2
- Apertus v1.5 8B vs Recursive Flint
- Apertus v1.5 8B vs Ripple-65B Preview
- Apertus v1.5 8B vs SmolLM3-3B
- Apertus v1.5 8B vs Spark-X2.6-4B
- Apertus v1.5 8B vs Swift 1.5 Qwen3.8-27B
- Apertus v1.5 8B vs Swift 1.5 Qwen3.8-Flash-Next
- Apertus v1.5 8B vs ThinkingCap (Qwen3.8 27B)
- Apertus v1.5 8B vs Baidu Unlimited-OCR-2
- Apodex-1.1-mini vs LFM3-Audio-2B
- Arete-27B vs LFM3-Audio-2B
- BigBang-v1 vs LFM3-Audio-2B
- Carbon-X vs LFM3-Audio-2B
- Cosmos-Reason2-2B vs LFM3-Audio-2B
- Darwin-36B-Opus vs LFM3-Audio-2B
- DeepSeek-V3.2 vs LFM3-Audio-2B
- DeepSeek V4.1 Flash vs LFM3-Audio-2B
- DeepSeek-V4-Flash-0731 vs LFM3-Audio-2B
- DiffusionGemma 26B A4B IT vs LFM3-Audio-2B
- Gemma 4 12B IT vs LFM3-Audio-2B
- Gemma 4 26B A4B IT vs LFM3-Audio-2B
- Gemma 4 31B IT vs LFM3-Audio-2B
- Gemma 4 E2B IT vs LFM3-Audio-2B
- Gemma 4 E4B IT vs LFM3-Audio-2B
- GLM-4.5-Air vs LFM3-Audio-2B
- GLM-4.7-Flash vs LFM3-Audio-2B
- GLM-5.3-Flash vs LFM3-Audio-2B
- GLM-5.3 vs LFM3-Audio-2B
- gpt-oss-120b vs LFM3-Audio-2B
- gpt-oss-20b vs LFM3-Audio-2B
- Granite 4.2 30B vs LFM3-Audio-2B
- Granite 4.2 3B vs LFM3-Audio-2B
- Granite 4.2 8B vs LFM3-Audio-2B
- Hy-Memory-30B-A3B vs LFM3-Audio-2B
- Hy3 vs LFM3-Audio-2B
- Inkling-Small vs LFM3-Audio-2B
- Inkling vs LFM3-Audio-2B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-Audio-2B
- Kimi K3.1 Pro vs LFM3-Audio-2B
- Kimi K3 vs LFM3-Audio-2B
- Kolibri-1 vs LFM3-Audio-2B
- Kronos vs LFM3-Audio-2B
- Laguna M.1 vs LFM3-Audio-2B
- Laguna S 2.1 vs LFM3-Audio-2B
- Laguna XS 2.1 vs LFM3-Audio-2B
- LBS-27B vs LFM3-Audio-2B
- LFM2.5-2.6B vs LFM3-Audio-2B
- LFM3-1.7B vs LFM3-Audio-2B
- 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 Spark-X2.6-4B
- 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
Machine-readable
- This page as Markdown
/compare/apertus-v1-5-8b-vs-lfm3-audio-2b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/apertus-v1-5-8b.json·/api/v1/tools/lfm3-audio-2b.json - From a terminal
anchor compare apertus-v1-5-8b lfm3-audio-2b(the CLI) - Over MCP
compare_tools {"a": "apertus-v1-5-8b", "b": "lfm3-audio-2b"}at/mcp, no key