Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 E2B IT vs LFM3-Audio-2B
LFM3-Audio-2B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk and documentation and upkeep. 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 need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning
Ahead on
- Reach, 95 against 80
- Risk, 70 against 40
- Documentation and upkeep, 75 against 52
Watch for
No safety evaluation figures published
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.
Risk Gemma 4 E2B IT +30
Gemma 4 E2B IT 70
File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were 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 Gemma 4 E2B IT +23
Gemma 4 E2B IT 75
Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google merged a fix for 233 glitch tokens in tokenizer.json on 3 November 2026, a day before 4 November. Responsiveness 10, Google replied to 9 of 24 questions and reports, the last on 2026-09-15, re-counted on 4 November. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but a report about them waits without a reply.
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.
Reach Gemma 4 E2B IT +15
Gemma 4 E2B IT 95
Memory 30, Google's own 4-bit QAT GGUF with its projector is 4.34 GB, which with a fifth on top is 5.2 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build and two mobile builds, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one.
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 | Gemma 4 E2B IT | LFM3-Audio-2B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 80 | Gemma 4 E2B IT +15 |
| Risk | 20%20 | 70 | 40 | Gemma 4 E2B IT +30 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 75 | 52 | Gemma 4 E2B IT +23 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 60.4/100 | C 61/100 |
Facts side by side
| Fact | Gemma 4 E2B IT | LFM3-Audio-2B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | 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 | yes | yes |
| Last release | 2026-11-03 | 2026-11-03 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3.5/5 (4) | none |
Verdicts
Gemma 4 E2B IT
A small open-weight model for light agent work, tool calls, short audio and images on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, and no safety results are published.
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
Gemma 4 E2B IT
- On the reference machine run
llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072, and add--mmprojwith gemma-4-E2B-it-mmproj.gguf for images and audio - With Ollama run
ollama run gemma4:e2band raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K - In vLLM or SGLang pass
--tool-call-parser gemma4 --reasoning-parser gemma4(vLLM also--enable-auto-tool-choice), or tool calls come back as text; Google's w4a16 build is the 4-bit route there - Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
- Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them
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, Gemma 4 E2B IT or LFM3-Audio-2B?
LFM3-Audio-2B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk and documentation and upkeep.
Other comparisons with Gemma 4 E2B IT or LFM3-Audio-2B
- Agents-A1-4B vs Gemma 4 E2B IT
- Agents-A1-4B vs LFM3-Audio-2B
- Apertus v1.5 8B vs Gemma 4 E2B IT
- Apertus v1.5 8B vs LFM3-Audio-2B
- Apodex-1.1-mini vs Gemma 4 E2B IT
- Apodex-1.1-mini vs LFM3-Audio-2B
- Arete-27B vs Gemma 4 E2B IT
- Arete-27B vs LFM3-Audio-2B
- BigBang-v1 vs Gemma 4 E2B IT
- BigBang-v1 vs LFM3-Audio-2B
- Carbon-X vs Gemma 4 E2B IT
- Carbon-X vs LFM3-Audio-2B
- Cosmos-Reason2-2B vs Gemma 4 E2B IT
- Cosmos-Reason2-2B vs LFM3-Audio-2B
- Darwin-36B-Opus vs Gemma 4 E2B IT
- Darwin-36B-Opus vs LFM3-Audio-2B
- DeepSeek-V3.2 vs Gemma 4 E2B IT
- DeepSeek-V3.2 vs LFM3-Audio-2B
- DeepSeek V4.1 Flash vs Gemma 4 E2B IT
- DeepSeek V4.1 Flash vs LFM3-Audio-2B
- DeepSeek-V4-Flash-0731 vs Gemma 4 E2B IT
- DeepSeek-V4-Flash-0731 vs LFM3-Audio-2B
- DiffusionGemma 26B A4B IT vs Gemma 4 E2B IT
- DiffusionGemma 26B A4B IT vs LFM3-Audio-2B
- Gemma 4 12B IT vs Gemma 4 E2B IT
- Gemma 4 12B IT vs LFM3-Audio-2B
- Gemma 4 26B A4B IT vs Gemma 4 E2B IT
- Gemma 4 26B A4B IT vs LFM3-Audio-2B
- Gemma 4 31B IT vs Gemma 4 E2B IT
- Gemma 4 31B IT vs LFM3-Audio-2B
- Gemma 4 E2B IT vs Gemma 4 E4B IT
- Gemma 4 E2B IT vs GLM-4.5-Air
- Gemma 4 E2B IT vs GLM-4.7-Flash
- Gemma 4 E2B IT vs GLM-5.3
- Gemma 4 E2B IT vs GLM-5.3-Flash
- Gemma 4 E2B IT vs gpt-oss-120b
- Gemma 4 E2B IT vs gpt-oss-20b
- Gemma 4 E2B IT vs Granite 4.2 30B
- Gemma 4 E2B IT vs Granite 4.2 3B
- Gemma 4 E2B IT vs Granite 4.2 8B
- Gemma 4 E2B IT vs Hy-Memory-30B-A3B
- Gemma 4 E2B IT vs Hy3
- Gemma 4 E2B IT vs Inkling
- Gemma 4 E2B IT vs Inkling-Small
- Gemma 4 E2B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Gemma 4 E2B IT vs Kimi K3
- Gemma 4 E2B IT vs Kimi K3.1 Pro
- Gemma 4 E2B IT vs Kolibri-1
- Gemma 4 E2B IT vs Kronos
- Gemma 4 E2B IT vs Laguna M.1
- Gemma 4 E2B IT vs Laguna S 2.1
- Gemma 4 E2B IT vs Laguna XS 2.1
- Gemma 4 E2B IT vs LBS-27B
- Gemma 4 E2B IT vs LFM2.5-2.6B
- Gemma 4 E2B IT vs LFM3-1.7B
- Gemma 4 E2B IT vs LFM3-Omni-2B
- Gemma 4 E2B IT vs LFM3-VL-2B
- Gemma 4 E2B IT vs Ling-3.0-tiny
- Gemma 4 E2B IT vs Ling-3.1-flash-1019
- Gemma 4 E2B IT vs MiMo-V2.6 Distill Qwen 9B
- Gemma 4 E2B IT vs MiniCPM-o 4.5
- Gemma 4 E2B IT vs MiniCPM-V 4.7 1B
- Gemma 4 E2B IT vs MiniCPM-V 4.7 35B-A3B
- Gemma 4 E2B IT vs MiniCPM5-2B
- Gemma 4 E2B IT vs MiniMax-M2.5
- Gemma 4 E2B IT vs MiniMax-M2.7
- Gemma 4 E2B IT vs MiniMax-M3.1-Flash
- Gemma 4 E2B IT vs Mistral Large 4
- Gemma 4 E2B IT vs Muse Glimmer 30B
- Gemma 4 E2B IT vs Nanbeige4.3-33B
- Gemma 4 E2B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Gemma 4 E2B IT vs Nemotron 3.5 Lightning 30B A3B
- Gemma 4 E2B IT vs Nemotron 3 Nano 30B A3B
- Gemma 4 E2B IT vs Nemotron 3 Nano 4B
- Gemma 4 E2B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Gemma 4 E2B IT vs Nemotron 3 Super 120B A12B
- Gemma 4 E2B IT vs Nemotron 3 Ultra 550B A55B
- Gemma 4 E2B IT vs occamy-1.0
- Gemma 4 E2B IT vs Olmo 3 7B Think
- Gemma 4 E2B IT vs Ornith-1.5-122B
- Gemma 4 E2B IT vs Ornith-1.5-35B-A3B
- Gemma 4 E2B IT vs Ornith-1.5-397B
- Gemma 4 E2B IT vs Ornith-1.5-9B
- Gemma 4 E2B IT vs Qwen-AgentWorld-35B-A3B
- Gemma 4 E2B IT vs Qwen3-30B-A3B-Instruct-2507
- Gemma 4 E2B IT vs Qwen3-4B-Instruct-2507
- Gemma 4 E2B IT vs Qwen3.5-0.8B
- Gemma 4 E2B IT vs Qwen3.5-122B-A10B
- Gemma 4 E2B IT vs Qwen3.5-2B
- Gemma 4 E2B IT vs Qwen3.5-397B-A17B
- Gemma 4 E2B IT vs Qwen3.5-4B
- Gemma 4 E2B IT vs Qwen3.5-9B
- Gemma 4 E2B IT vs Qwen3.6-27B
- Gemma 4 E2B IT vs Qwen3.6-35B-A3B
- Gemma 4 E2B IT vs Qwen3.8-27B
- Gemma 4 E2B IT vs Qwen3.8-Flash-Next
- Gemma 4 E2B IT vs Qwen3-Coder-30B-A3B-Instruct
- Gemma 4 E2B IT vs Qwen3-Coder-Next
- Gemma 4 E2B IT vs Qwen3-Omni-30B-A3B-Instruct
- Gemma 4 E2B IT vs Qwen3-VL-2B-Instruct
- Gemma 4 E2B IT vs Qwen3-VL-30B-A3B-Instruct
- Gemma 4 E2B IT vs Qwen3-VL-32B-Instruct
- Gemma 4 E2B IT vs Qwen3-VL-4B-Instruct
- Gemma 4 E2B IT vs Qwen3-VL-8B-Instruct
- Gemma 4 E2B IT vs Qwopus3.8-27B-Flash-V2
- Gemma 4 E2B IT vs Recursive Flint
- Gemma 4 E2B IT vs Ripple-65B Preview
- Gemma 4 E2B IT vs SmolLM3-3B
- Gemma 4 E2B IT vs Spark-X2.6-4B
- Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-Flash-Next
- Gemma 4 E2B IT vs ThinkingCap (Qwen3.8 27B)
- Gemma 4 E2B IT vs Baidu Unlimited-OCR-2
- 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/gemma-4-e2b-it-vs-lfm3-audio-2b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gemma-4-e2b-it.json·/api/v1/tools/lfm3-audio-2b.json - From a terminal
anchor compare gemma-4-e2b-it lfm3-audio-2b(the CLI) - Over MCP
compare_tools {"a": "gemma-4-e2b-it", "b": "lfm3-audio-2b"}at/mcp, no key