Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 30B vs LFM3-Audio-2B
Granite 4.2 30B and LFM3-Audio-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). 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 documented, signed, Apache 2.0 reasoning model with tool calling on a 24 GB card
Ahead on
- Reach, 90 against 80
- Risk, 55 against 40
- Documentation and upkeep, 87 against 52
Watch for
No safety results published for it
Best for Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model
No category where it leads by five points or more, and no fact that sets it apart.
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.
Documentation and upkeep Granite 4.2 30B +35
Granite 4.2 30B 87
Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (30B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 30B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 15, the last commit to the repository was on 4 September 2026, 67 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (28 August to 3 November 2026), 3 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 30B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 15 + 20 + 5 + 10 = 87.
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.
Risk Granite 4.2 30B +15
Granite 4.2 30B 55
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs and the GitHub README link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and the card's licence link resolves. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the 3B listing on 4 November 2026 (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.
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.
Reach Granite 4.2 30B +10
Granite 4.2 30B 90
Memory 25, IBM's own 4-bit MLX build granite-4.2-30b-q4-mlx is 16.47 GB, and 16.47 GB × 1.2 = 19.8 GB, which fits 24 GB but not 16 GB. Its Q4_0 GGUF is 16.58 GB and Ollama's library build granite4.2:30b 18 GB, the same band. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 30b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 25 + 25 + 15 + 15 + 10 + 0 = 90.
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.
Score by category
| Category | Weight this run | Granite 4.2 30B | LFM3-Audio-2B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 80 | Granite 4.2 30B +10 |
| Risk | 20%20 | 55 | 40 | Granite 4.2 30B +15 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 87 | 52 | Granite 4.2 30B +35 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | Granite 4.2 30B | LFM3-Audio-2B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | Liquid AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | LFM Open License v1.0 (free commercial use below $10M annual revenue) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-09-04 | 2026-11-03 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
Verdicts
Granite 4.2 30B
IBM's largest reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 24 GB card, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented.
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
Granite 4.2 30B
- Start it with
ollama run granite4.2:30b, orllama-server -hf ibm-granite/granite-4.2-30b-GGUF:Q4_K_M --jinja - With vLLM 0.32.0 or later leave out
--reasoning-parser-plugin, since the parser is built in, and use--tool-call-parser qwen3_coder - Thinking is on by default. Pass
enable_thinkingfalse to turn it off orlow_efforttrue for short reasoning - The context is 131,072 tokens. The card's 512K extension isn't documented
- Validate JSON output yourself, since structured output isn't documented
- IBM recommends Granite Guardian beside it to flag risky inputs and outputs
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, Granite 4.2 30B or LFM3-Audio-2B?
Granite 4.2 30B and LFM3-Audio-2B score within a point of each other on agent readiness, 61 (C) and 61 (C).
Other comparisons with Granite 4.2 30B or LFM3-Audio-2B
- Agents-A1-4B vs Granite 4.2 30B
- Agents-A1-4B vs LFM3-Audio-2B
- Apertus v1.5 8B vs Granite 4.2 30B
- Apertus v1.5 8B vs LFM3-Audio-2B
- Apodex-1.1-mini vs Granite 4.2 30B
- Apodex-1.1-mini vs LFM3-Audio-2B
- Arete-27B vs Granite 4.2 30B
- Arete-27B vs LFM3-Audio-2B
- BigBang-v1 vs Granite 4.2 30B
- BigBang-v1 vs LFM3-Audio-2B
- Carbon-X vs Granite 4.2 30B
- Carbon-X vs LFM3-Audio-2B
- Cosmos-Reason2-2B vs Granite 4.2 30B
- Cosmos-Reason2-2B vs LFM3-Audio-2B
- Darwin-36B-Opus vs Granite 4.2 30B
- Darwin-36B-Opus vs LFM3-Audio-2B
- DeepSeek-V3.2 vs Granite 4.2 30B
- DeepSeek-V3.2 vs LFM3-Audio-2B
- DeepSeek V4.1 Flash vs Granite 4.2 30B
- DeepSeek V4.1 Flash vs LFM3-Audio-2B
- DeepSeek-V4-Flash-0731 vs Granite 4.2 30B
- DeepSeek-V4-Flash-0731 vs LFM3-Audio-2B
- DiffusionGemma 26B A4B IT vs Granite 4.2 30B
- DiffusionGemma 26B A4B IT vs LFM3-Audio-2B
- Gemma 4 12B IT vs Granite 4.2 30B
- Gemma 4 12B IT vs LFM3-Audio-2B
- Gemma 4 26B A4B IT vs Granite 4.2 30B
- Gemma 4 26B A4B IT vs LFM3-Audio-2B
- Gemma 4 31B IT vs Granite 4.2 30B
- Gemma 4 31B IT vs LFM3-Audio-2B
- Gemma 4 E2B IT vs Granite 4.2 30B
- Gemma 4 E2B IT vs LFM3-Audio-2B
- Gemma 4 E4B IT vs Granite 4.2 30B
- Gemma 4 E4B IT vs LFM3-Audio-2B
- GLM-4.5-Air vs Granite 4.2 30B
- GLM-4.5-Air vs LFM3-Audio-2B
- GLM-4.7-Flash vs Granite 4.2 30B
- GLM-4.7-Flash vs LFM3-Audio-2B
- GLM-5.3-Flash vs Granite 4.2 30B
- GLM-5.3-Flash vs LFM3-Audio-2B
- GLM-5.3 vs Granite 4.2 30B
- GLM-5.3 vs LFM3-Audio-2B
- gpt-oss-120b vs Granite 4.2 30B
- gpt-oss-120b vs LFM3-Audio-2B
- gpt-oss-20b vs Granite 4.2 30B
- gpt-oss-20b vs LFM3-Audio-2B
- Granite 4.2 30B vs Granite 4.2 3B
- Granite 4.2 30B vs Granite 4.2 8B
- Granite 4.2 30B vs Hy-Memory-30B-A3B
- Granite 4.2 30B vs Hy3
- Granite 4.2 30B vs Inkling
- Granite 4.2 30B vs Inkling-Small
- Granite 4.2 30B vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Granite 4.2 30B vs Kimi K3
- Granite 4.2 30B vs Kimi K3.1 Pro
- Granite 4.2 30B vs Kolibri-1
- Granite 4.2 30B vs Kronos
- Granite 4.2 30B vs Laguna M.1
- Granite 4.2 30B vs Laguna S 2.1
- Granite 4.2 30B vs Laguna XS 2.1
- Granite 4.2 30B vs LBS-27B
- Granite 4.2 30B vs LFM2.5-2.6B
- Granite 4.2 30B vs LFM3-1.7B
- Granite 4.2 30B vs LFM3-Omni-2B
- Granite 4.2 30B vs LFM3-VL-2B
- Granite 4.2 30B vs Ling-3.0-tiny
- Granite 4.2 30B vs Ling-3.1-flash-1019
- Granite 4.2 30B vs MiMo-V2.6 Distill Qwen 9B
- Granite 4.2 30B vs MiniCPM-o 4.5
- Granite 4.2 30B vs MiniCPM-V 4.7 1B
- Granite 4.2 30B vs MiniCPM-V 4.7 35B-A3B
- Granite 4.2 30B vs MiniCPM5-2B
- Granite 4.2 30B vs MiniMax-M2.5
- Granite 4.2 30B vs MiniMax-M2.7
- Granite 4.2 30B vs MiniMax-M3.1-Flash
- Granite 4.2 30B vs Mistral Large 4
- Granite 4.2 30B vs Muse Glimmer 30B
- Granite 4.2 30B vs Nanbeige4.3-33B
- Granite 4.2 30B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Granite 4.2 30B vs Nemotron 3.5 Lightning 30B A3B
- Granite 4.2 30B vs Nemotron 3 Nano 30B A3B
- Granite 4.2 30B vs Nemotron 3 Nano 4B
- Granite 4.2 30B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Granite 4.2 30B vs Nemotron 3 Super 120B A12B
- Granite 4.2 30B vs Nemotron 3 Ultra 550B A55B
- Granite 4.2 30B vs occamy-1.0
- Granite 4.2 30B vs Olmo 3 7B Think
- Granite 4.2 30B vs Ornith-1.5-122B
- Granite 4.2 30B vs Ornith-1.5-35B-A3B
- Granite 4.2 30B vs Ornith-1.5-397B
- Granite 4.2 30B vs Ornith-1.5-9B
- Granite 4.2 30B vs Qwen-AgentWorld-35B-A3B
- Granite 4.2 30B vs Qwen3-30B-A3B-Instruct-2507
- Granite 4.2 30B vs Qwen3-4B-Instruct-2507
- Granite 4.2 30B vs Qwen3.5-0.8B
- Granite 4.2 30B vs Qwen3.5-122B-A10B
- Granite 4.2 30B vs Qwen3.5-2B
- Granite 4.2 30B vs Qwen3.5-397B-A17B
- Granite 4.2 30B vs Qwen3.5-4B
- Granite 4.2 30B vs Qwen3.5-9B
- Granite 4.2 30B vs Qwen3.6-27B
- Granite 4.2 30B vs Qwen3.6-35B-A3B
- Granite 4.2 30B vs Qwen3.8-27B
- Granite 4.2 30B vs Qwen3.8-Flash-Next
- Granite 4.2 30B vs Qwen3-Coder-30B-A3B-Instruct
- Granite 4.2 30B vs Qwen3-Coder-Next
- Granite 4.2 30B vs Qwen3-Omni-30B-A3B-Instruct
- Granite 4.2 30B vs Qwen3-VL-2B-Instruct
- Granite 4.2 30B vs Qwen3-VL-30B-A3B-Instruct
- Granite 4.2 30B vs Qwen3-VL-32B-Instruct
- Granite 4.2 30B vs Qwen3-VL-4B-Instruct
- Granite 4.2 30B vs Qwen3-VL-8B-Instruct
- Granite 4.2 30B vs Qwopus3.8-27B-Flash-V2
- Granite 4.2 30B vs Recursive Flint
- Granite 4.2 30B vs Ripple-65B Preview
- Granite 4.2 30B vs SmolLM3-3B
- Granite 4.2 30B vs Spark-X2.6-4B
- Granite 4.2 30B vs Swift 1.5 Qwen3.8-27B
- Granite 4.2 30B vs Swift 1.5 Qwen3.8-Flash-Next
- Granite 4.2 30B vs ThinkingCap (Qwen3.8 27B)
- Granite 4.2 30B vs Baidu Unlimited-OCR-2
- 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/granite-4-2-30b-vs-lfm3-audio-2b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/granite-4-2-30b.json·/api/v1/tools/lfm3-audio-2b.json - From a terminal
anchor compare granite-4-2-30b lfm3-audio-2b(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-30b", "b": "lfm3-audio-2b"}at/mcp, no key