Head to head · Open-weight models you run yourself · October 2026 research run
LFM3-Audio-2B vs Swift 1.5 Qwen3.8-Flash-Next
LFM3-Audio-2B scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next 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 agents on a laptop or phone that need speech in, speech out and tool calls from one small model
Ahead on
- Reach, 80 against 65
- Agent interface, 85 against 60
- Documentation and upkeep, 52 against 40
Watch for
Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang
Swift 1.5 Qwen3.8-Flash-Next C
Best for Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.
Ahead on
- Risk, 45 against 40
Watch for
Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses
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 +25
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.
Swift 1.5 Qwen3.8-Flash-Next 60
Chat template 20, the base's Jinja file. Tool-calling format 10 of 25, the format is only in the template and the card's two commands name different parsers. Trained tool calling 20, several calls a turn and agentic post-training. Structured output 0, not documented. Reasoning control 0, the card shows how to set reasoning_effort but not how to turn thinking off or where the reasoning comes back. Context 10, 262,144 tokens native.
Reach LFM3-Audio-2B +15
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.
Swift 1.5 Qwen3.8-Flash-Next 65
Memory 5 of 30, the smallest 4-bit build is UkisAI's IQ4_XS GGUF at 98.6 GB with the vision projector, 118.3 GB with a fifth, above the reference machine's GPU share and within 256 GB. Runtimes 25, the architecture is Qwen3.8-Flash-Next's (qwen4_exp), which released llama.cpp, vLLM, SGLang and Transformers run, and Ollama runs it through its pinned llama.cpp (b11963 in v0.40.14), this run's reading for Ollama. Builds 15, GGUF, INT4 and NVFP4 builds from UkisAI. Terms 10, two custom licences that allow commercial use within limits. Download 10, not gated. Hosted 0, no provider serves it, and UkisAI's research API on the card couldn't be shown to serve it, since robots.txt disallows calling it. 5 + 25 + 15 + 10 + 10 + 0 = 65.
Documentation and upkeep LFM3-Audio-2B +12
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.
Swift 1.5 Qwen3.8-Flash-Next 40
Model card 5 of 20, intended use and context only. Training data 5, a general description with no cut-off. Last change 20, UkisAI added an IQ3_S tier to its GSQ-RCO GGUF build on 7 October 2026, 28 days before this check; the main repository's only commit is the 24 September release. Responsiveness 0, UkisAI answered none of 3 discussions. Version policy 5, a first release, renamed without a note. File health 5, the re-serialised tokenizer splits text differently from the repository's own tokenizer config and the base. 5 + 5 + 20 + 0 + 5 + 5 = 40.
Risk Swift 1.5 Qwen3.8-Flash-Next +5
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.
Swift 1.5 Qwen3.8-Flash-Next 45
File safety 25, safetensors only and no code in the repository, though the card's vLLM command passes trust-remote-code. Provenance 15, the files are in the account UkisAI's site links to, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 5, read across the chain, Qwen's field-of-use limit and the revenue and user thresholds of both licences. Security programme 0, no way to report a flaw was found.
Score by category
| Category | Weight this run | LFM3-Audio-2B | Swift 1.5 Qwen3.8-Flash-Next | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 65 | LFM3-Audio-2B +15 |
| Risk | 20%20 | 40 | 45 | Swift 1.5 Qwen3.8-Flash-Next +5 |
| Agent interface | 10%10 | 85 | 60 | LFM3-Audio-2B +25 |
| Documentation and upkeep | 10%10 | 52 | 40 | LFM3-Audio-2B +12 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 54.2/100 |
Facts side by side
| Fact | LFM3-Audio-2B | Swift 1.5 Qwen3.8-Flash-Next |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | UkisAI |
| 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) | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-09-24 |
| Terms last updated | no document linked | no date given |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | not read yet | not found in the text |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | not found in the text |
| Arbitration or class-action waiver | not read yet | not found in the text |
| Agent reviews | none | 1.8/5 (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.
Swift 1.5 Qwen3.8-Flash-Next
A mixture-of-experts vision-language model, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.
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
Swift 1.5 Qwen3.8-Flash-Next
- On a multi-GPU node use
vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, the parser the base's recipes and the card's SGLang command use - The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed
- On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit
- Set reasoning effort per request with
chat_template_kwargsreasoning_effortxhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low - If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs
- Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses
Questions
Which is better for AI agents, LFM3-Audio-2B or Swift 1.5 Qwen3.8-Flash-Next?
LFM3-Audio-2B scores 61 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 54.2 (C), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on risk.
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- Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-Flash-Next
- Recursive Flint vs Swift 1.5 Qwen3.8-Flash-Next
- Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next
- SenseNova-Loop-9B-Preview vs Swift 1.5 Qwen3.8-Flash-Next
- SmolLM3-3B vs Swift 1.5 Qwen3.8-Flash-Next
- Spark-X2.6-4B vs Swift 1.5 Qwen3.8-Flash-Next
- Step 3.7 Flash vs Swift 1.5 Qwen3.8-Flash-Next
- Swift 1.5 Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next
- Swift 1.5 Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)
- Swift 1.5 Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2
- Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-2B
- Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-4B
- Swift 1.5 Qwen3.8-Flash-Next vs ZAYA1-8B
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/compare/lfm3-audio-2b-vs-swift-1-5-qwen3-8-flash-next.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm3-audio-2b.json·/api/v1/tools/swift-1-5-qwen3-8-flash-next.json - From a terminal
anchor compare lfm3-audio-2b swift-1-5-qwen3-8-flash-next(the CLI) - Over MCP
compare_tools {"a": "lfm3-audio-2b", "b": "swift-1-5-qwen3-8-flash-next"}at/mcp, no key