# LFM3-Audio-2B vs Swift 1.5 Qwen3.8-27B

> LFM3-Audio-2B and Swift 1.5 Qwen3.8-27B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-swift-1-5-qwen3-8-27b
- Markdown: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-swift-1-5-qwen3-8-27b.md (~9,650 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

LFM3-Audio-2B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on risk. Both do open-weight models you run yourself.

- LFM3-Audio-2B: grade C, 61/100, rank #70 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-audio-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.json
- Swift 1.5 Qwen3.8-27B: grade C, 61/100, rank #57 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### LFM3-Audio-2B (C)

Good 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 60
- Documentation and upkeep, 52 against 43

Watch for: Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang

### Swift 1.5 Qwen3.8-27B (C)

Good for: Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.

Ahead on:
- Risk, 50 against 40

Watch for: Commercial use above US$1M gross annual revenue needs a separate licence


## Why the edge

### 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 Qwen3.8-27B (60): Chat template 20, the base's Jinja file. Tool-calling format 10 of 25, the format is only in the template and the parser only in serving commands. Trained tool calling 20, several calls a turn and agentic post-training. Structured output 0, not documented. Reasoning control 0, the card mentions reasoning_effort but not how to set it, turn thinking off or read the reasoning. Context 10, 262,144 tokens native.

### Risk: Swift 1.5 Qwen3.8-27B +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.
- Swift 1.5 Qwen3.8-27B (50): File safety 25, safetensors only and nothing from the repository runs. 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 10, a revenue threshold. Security programme 0, no way to report a flaw was found.

### Documentation and upkeep: LFM3-Audio-2B +9

- 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 Qwen3.8-27B (43): Model card 8 of 20, intended use, parameters and context only. Training data 5, a general description with no cut-off. Last change 20, UkisAI's own ONNX builds of the model took a new card and licence on 8 October, 26 days ago (the main repository's last commit is the 3 October README update). Responsiveness 0, UkisAI answered none of 8 discussions. Version policy 5, Swift 1.0 stays in its own repository without a note. File health 5, reports of a broken model wait without a reply and the card's metadata claims a gate that doesn't exist.

## Score by category

| Category | Weight | LFM3-Audio-2B | Swift 1.5 Qwen3.8-27B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 80 | 80 | even |
| Risk | 20% (20 this run) | 40 | 50 | Swift 1.5 Qwen3.8-27B +10 |
| Agent interface | 10% (10 this run) | 85 | 60 | LFM3-Audio-2B +25 |
| Documentation and upkeep | 10% (10 this run) | 52 | 43 | LFM3-Audio-2B +9 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **61 · C** | |

## Facts side by side

| Fact | LFM3-Audio-2B | Swift 1.5 Qwen3.8-27B |
| --- | --- | --- |
| 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 with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base |
| 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 | 2.3/5 (4) |

## 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 Qwen3.8-27B.** A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.

## Before you call either

### LFM3-Audio-2B

1. Start it with `llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-audio`
2. llama.cpp ships examples/realtime_client.py for live speech with this model
3. Prefill the assistant turn with an open think tag to let it reason before a text reply
4. The context is 32K
5. Voice chat is English only, while transcription, synthesis and translation cover 19 languages and locales
6. Liquid doesn't recommend it for agentic coding or complex multi-step reasoning

### Swift 1.5 Qwen3.8-27B

1. Quickest start is `llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 18.4 GB with the vision projector
2. On our reference machine BF16 fits, `vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`
3. The template is Qwen3.8-27B's, so `chat_template_kwargs` with `reasoning_effort` low, medium or xhigh (default) and `enable_thinking` false work as on the base
4. UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point
5. The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat
6. Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates
7. MTP self-speculative decoding is available with the base's head, flags on the card

## Questions

### Which is better for AI agents, LFM3-Audio-2B or Swift 1.5 Qwen3.8-27B?

LFM3-Audio-2B and Swift 1.5 Qwen3.8-27B score within a point of each other on agent readiness, 61 (C) and 61 (C). Swift 1.5 Qwen3.8-27B leads on risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-swift-1-5-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-swift-1-5-qwen3-8-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lfm3-audio-2b", "b": "swift-1-5-qwen3-8-27b"}`. From a terminal: `anchor compare lfm3-audio-2b swift-1-5-qwen3-8-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-27b.json

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