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Best Mobile LLM Models for Phones in 2026: Phi-4 Mini vs Gemma 3 vs SmolLM

·12 min readBy Hans Kuepper · Founder of PromptQuorum · Discovery engine for open-weight & open-source AI

For most flagship phones (8 GB+ RAM) in 2026, Phi-4 Mini (3.8B) at Q4_K_M quantisation is the smartest model that runs at usable speed (~13–18 tokens/sec on iPhone 17 Pro). For older 6 GB phones, Qwen3 1.7B or SmolLM 2 1.7B fit comfortably and produce coherent short-form responses. SmolLM 2 1.7B is the fastest tokens-per-second on every reference phone. Qwen3 1.7B is the strongest multilingual option (35+ languages, including Chinese, Japanese, Arabic). Gemma 3 4B is the balanced default if Phi-4 Mini is unavailable in your app. Gemma 3 1B is the lightweight pick for very old phones (4 GB RAM). Llama 3.2 3B is the most widely supported 3B workhorse with the broadest tool support. The best phone for local LLMs is the one with the most RAM and the newest flagship chip — 8 GB RAM is the practical minimum for the 3B–4B models, 12 GB for Q5_K_M.

Key Takeaways

  • Phi-4 Mini (3.8B) is the smartest small model in 2026. Best for flagship phones with 8 GB+ RAM — runs at ~13–18 tokens/sec on iPhone 17 Pro and ~10–15 on iPhone 16 Pro. Strongest reasoning per parameter of any sub-4B model.
  • SmolLM 2 1.7B is the fastest tokens-per-second on every reference phone. ~26–32 tok/sec on iPhone 17 Pro, ~20–28 on Galaxy S25 Ultra. Best when responsiveness matters more than answer depth (snappy chat, autocomplete-style tasks).
  • Qwen3 1.7B is the strongest multilingual mobile model. Trained on 35+ languages including Chinese, Japanese, Arabic, and German with native-quality output. Best choice for translation, non-English drafting, and travel use.
  • Gemma 3 4B is the balanced default. Slightly slower than Phi-4 Mini on the same hardware but matches it on chat and summarisation. Best when Phi-4 Mini is unavailable in your app or you want Google's training-data mix.
  • Gemma 3 1B is the lightweight pick for older phones. Fits in 4 GB RAM (iPhone SE 3rd gen, older Android). Limited multi-step reasoning but produces coherent 1–2 paragraph responses faster than any other model on weak hardware.
  • Llama 3.2 3B is the most widely supported 3B workhorse. Best tool-calling support among the six, broadest app compatibility, strongest community fine-tunes. Slightly behind Phi-4 Mini on raw quality but more reliable in edge cases.
  • Q4_K_M is the standard mobile quantisation in 2026. Preserves ~95% of original quality at one-quarter the file size. Use Q5_K_M or Q6_K only on 12 GB+ phones (iPhone 17 Pro Max) and only if the app supports it.
  • The best phone for local AI is decided by RAM first, chip second. 8 GB RAM is the practical minimum for 3B–4B models; 12 GB (iPhone 17 Pro Max, Galaxy S25 Ultra) adds Q5_K_M headroom. Sustained generation heats the phone, so a smaller model is the fix for long sessions, not a bigger one.
  • Only Gemma 3 4B among the six accepts images. The other five are text-only. SmolLM 3 (3B) and Gemma 3n (E2B/E4B) are newer models worth knowing but are not in the speed comparison because no verified on-device speed numbers exist for them yet.
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Six small language models cover almost every mobile use case in 2026: Phi-4 Mini (3.8B), Gemma 3 4B, Gemma 3 1B, SmolLM 2 1.7B, Qwen3 1.7B, and Llama 3.2 3B. They differ on tokens-per-second, memory footprint, and quality on chat, summarisation, translation, and short-form drafting. This guide compares them with indicative speed estimates for iPhone 17 Pro and Galaxy S25 Ultra (not PromptQuorum measurements), gives a per-tier verdict (flagship / mid-range / budget), shows which phone to buy or keep for local AI, lists download sizes in MB, covers newer models (SmolLM 3, Gemma 3n) and on-device image captioning, and explains why Q4_K_M quantisation is the default for mobile.

Best Mobile LLM Models for Phones in 2026: Phi-4 Mini vs Gemma 3 vs SmolLM

Quick Facts

  • Models compared: Phi-4 Mini 3.8B, Gemma 3 4B, Gemma 3 1B, SmolLM 2 1.7B, Qwen3 1.7B, Llama 3.2 3B (all Q4_K_M GGUF).
  • Reference devices: iPhone 17 Pro (A19 Pro), iPhone 16 Pro (A18 Pro, 8 GB), Galaxy S25 Ultra (Snapdragon 8 Elite), Pixel 9 Pro (Tensor G4), OnePlus 13 (Snapdragon 8 Elite). Speed figures on this page are indicative estimates, not measurements made by PromptQuorum; real speed varies with the app, runtime, quantisation, phone temperature and settings.
  • Inference engines: llama.cpp via PocketPal AI / LLM Farm (default), MLC LLM via MLC Chat (Metal-accelerated on iPhone), Ollama via Termux (Android).
  • Memory footprint (Q4_K_M): Phi-4 Mini ~2.7 GB, Gemma 3 4B ~2.9 GB, Llama 3.2 3B ~2.2 GB, Qwen3 1.7B ~1.1 GB, SmolLM 2 1.7B ~1.1 GB, Gemma 3 1B ~720 MB.
  • Minimum RAM (active): 6 GB phone for 1.7B models; 8 GB phone for 3B–4B models; 4 GB phone for Gemma 3 1B only.
  • Fastest tokens/sec on iPhone 17 Pro: Gemma 3 1B ~35–45, SmolLM 2 ~26–32, Qwen3 1.7B ~26–35, Llama 3.2 3B ~16–22, Phi-4 Mini ~13–18, Gemma 3 4B ~10–13.
  • Source quantisation: all six available as Q4_K_M GGUF on Hugging Face and via PocketPal AI / MLC Chat / LM Studio.
  • Download size (Q4_K_M, MB): Gemma 3 1B ~720, Qwen3 1.7B ~1,100, SmolLM 2 ~1,100, Llama 3.2 3B ~2,200, Phi-4 Mini ~2,700, Gemma 3 4B ~2,900 — see the size table below.
  • Newer models not in the speed comparison: SmolLM 3 (3B, Hugging Face, July 2025) and Gemma 3n (E2B / E4B, Google). Image input: Gemma 3 4B only among the six.

Which Mobile Model Should You Pick?

For most flagship phones (iPhone 16 Pro / 17 Pro, Galaxy S25 Ultra, OnePlus 13), pick Phi-4 Mini (3.8B Q4_K_M). It is the smartest sub-4B model and runs at usable conversational speed. Pick a different model only when you have a specific need it does not cover — speed (SmolLM 2), multilingual (Qwen3), or older-phone compatibility (Gemma 3 1B).

📍 In One Sentence

Pick Phi-4 Mini for flagship 8 GB+ phones (smartest), SmolLM 2 1.7B for speed, Qwen3 1.7B for multilingual, Gemma 3 1B for 4 GB phones, Llama 3.2 3B for tool calling, and Gemma 3 4B as the balanced default when Phi-4 Mini is unavailable.

💬 In Plain Terms

There is no single best mobile model — the right pick depends on your phone and what you do with it. If your phone is from the last two years and has 8 GB or more RAM, install Phi-4 Mini. If you mostly chat in a non-English language, install Qwen3. If you want the fastest replies even at the cost of some quality, install SmolLM 2. If your phone is older or has only 4 GB RAM, install Gemma 3 1B. The differences are real but small enough that any of these will produce coherent answers — none are cloud-quality.

Decision: Which Mobile Model?

Use a local LLM if:

  • •Flagship phone with 8 GB+ RAM (iPhone 16 Pro/17 Pro, Galaxy S25 Ultra, OnePlus 13) → Phi-4 Mini 3.8B
  • •Need fastest tokens/sec on any phone → SmolLM 2 1.7B
  • •Non-English use (translation, multilingual chat) → Qwen3 1.7B
  • •Need broad app compatibility, tool calling, or RAG → Llama 3.2 3B
  • •Older phone with 4 GB RAM → Gemma 3 1B
  • •Phi-4 Mini unavailable in your app, need 4B-class quality → Gemma 3 4B

Use a cloud model if:

  • •Multi-step reasoning, complex code generation, or long-document analysis → use cloud or remote-connect to a home machine running 70B+
  • •Dense OCR or accuracy-critical image analysis → cloud apps; simple image captions can run on-device with Gemma 3 4B (see Multimodal on Phones below)
  • •Long-form creative writing where coherence over 3,000+ tokens matters → cloud or 8B+ on a desktop

Quick decision:

  • →Default for most users: Phi-4 Mini 3.8B
  • →Fastest on every device: SmolLM 2 1.7B
  • →Best multilingual: Qwen3 1.7B
4-step model selection guide for mobile LLMs: check phone RAM, match phone tier (4 GB to 8 GB+), pick use case (speed, multilingual, or quality), then install Q4_K_M GGUF via PocketPal AI or LM Studio.
4-step model selection guide for mobile LLMs: check phone RAM, match phone tier (4 GB to 8 GB+), pick use case (speed, multilingual, or quality), then install Q4_K_M GGUF via PocketPal AI or LM Studio.

💡Tip: If unsure, start with Phi-4 Mini on a flagship phone or SmolLM 2 1.7B on a mid-range phone — both download in under 5 minutes on a fast connection and are reversible. Try one prompt you actually care about (a real email to summarise, a real question to answer). If the quality feels acceptable, you have your default. If not, swap to a sibling model in 30 seconds via PocketPal AI or LM Studio.

Mobile Model Comparison Table

The four-column table below is the fast extraction layer — pick a row by phone tier or use case. Tokens/sec figures are indicative estimates (not PromptQuorum measurements) that assume Q4_K_M quantisation on iPhone 17 Pro using PocketPal AI (llama.cpp). Numbers are 15–25% lower on iPhone 16 Pro and roughly 10–20% lower on Galaxy S25 Ultra running the same Q4_K_M GGUF via MLC Chat or Termux+Ollama.

📍 In One Sentence

Phi-4 Mini is the smartest, SmolLM 2 1.7B is the fastest, Qwen3 1.7B is the best multilingual, Gemma 3 1B is the smallest viable, Llama 3.2 3B is the strongest 3B workhorse, and Gemma 3 4B is the balanced default.

💬 In Plain Terms

Read this table top-to-bottom in size order, or jump to the row that matches your phone tier. The "Best for" column is what to optimise for — pick the row whose strength matters most to you and ignore the others.

Model
Size
Tokens/sec (17 Pro)
Best for
Phi-4 Mini3.8B~13–18Smartest small model — flagship default
Gemma 3 4B4B~10–13Balanced default when Phi-4 Mini unavailable
Gemma 3 1B1B~35–45Older phones (4 GB RAM)
SmolLM 21.7B~26–32Fastest tokens/sec, snappy chat
Qwen31.7B~24–32Best multilingual (35+ languages)
Llama 3.23B~16–22Strongest 3B option, tool calling, RAG

Note on speed-quality trade-off: Tokens/sec scales inversely with parameter count when running on the same chip — a 1B model is roughly 3–4× faster than a 3.8B model on identical hardware. Quality scales with parameters but not linearly: Phi-4 Mini (3.8B) reasoning quality is closer to a 7B model than a 1.7B model thanks to Microsoft's training-data mix. Use the table to balance: faster model = quicker reply, smarter model = better answer for hard questions.

Six mobile LLM models compared at Q4_K_M, with indicative speeds for iPhone 17 Pro: Phi-4 Mini (3.8B, ~13–18 tok/sec) is the smartest; SmolLM 2 (1.7B, ~26–32 tok/sec) is the fastest; Qwen3 (1.7B) is the best multilingual; Gemma 3 1B (~35–45 tok/sec) works on 4 GB phones.
Six mobile LLM models compared at Q4_K_M, with indicative speeds for iPhone 17 Pro: Phi-4 Mini (3.8B, ~13–18 tok/sec) is the smartest; SmolLM 2 (1.7B, ~26–32 tok/sec) is the fastest; Qwen3 (1.7B) is the best multilingual; Gemma 3 1B (~35–45 tok/sec) works on 4 GB phones.

💡Tip: iPhone 16 Pro tokens/sec is roughly 15–25% lower than iPhone 17 Pro for every model in this table — A18 Pro vs A19 Pro Neural Engine difference. Galaxy S25 Ultra (Snapdragon 8 Elite) is roughly 10–20% lower than iPhone 17 Pro on the same Q4_K_M GGUF, mostly because Termux+Ollama on Android does not yet leverage the Snapdragon Hexagon NPU the way MLC Chat leverages Apple Metal.

Phi-4 Mini: Smartest Small Model

Phi-4 Mini (3.8B parameters, Microsoft, December 2024) is the smartest sub-4B model in 2026 thanks to a training-data mix optimised for reasoning over breadth. It outperforms Gemma 3 4B and Llama 3.2 3B on chain-of-thought tasks despite being a similar size. Use it as the default on any phone with 8 GB+ RAM.

  • Parameters and training: 3.8B parameters; trained on a Microsoft-curated mix of high-quality web text, synthetic reasoning chains, and academic content. Architecture is a Transformer with grouped-query attention.
  • Memory footprint: ~2.7 GB at Q4_K_M, ~3.5 GB at Q5_K_M. Fits comfortably on iPhone 16 Pro / 17 Pro (8 GB) and Galaxy S25 Ultra (12 GB) with room for the OS.
  • Speed (tokens/sec): iPhone 17 Pro ~13–18, iPhone 16 Pro ~10–15, Galaxy S25 Ultra ~10–15 (Termux+Ollama), iPhone 14 Pro ~6–10 (slow but functional).
  • Quality strengths: chain-of-thought reasoning, summarisation, factual Q&A, basic code generation. Outperforms similarly-sized open models on standard benchmarks (MMLU, GSM8K).
  • Quality weaknesses: narrower world knowledge than Llama 3.2 3B (less Common Crawl exposure); shorter natural creative writing than Gemma 3 4B; weaker multilingual than Qwen3 1.7B outside English.
  • Best for: users with a flagship phone who want the smartest single-model default for English-language chat, summarisation, and reasoning.

💡Tip: Phi-4 Mini benefits from a system prompt that explicitly invokes step-by-step reasoning ("Think through this carefully before answering"). The training data was heavy on reasoning chains, so prompting in that style consistently produces better answers than terse instructions. For quick chat, no system prompt is needed; the default behaviour is already conversational.

Gemma 3 4B: Balanced Default

Gemma 3 4B (Google DeepMind, 2025) is the balanced default when Phi-4 Mini is unavailable in your app or you prefer Google's training-data mix. Slightly slower than Phi-4 Mini on identical hardware but matches it on chat and summarisation, with broader natural-language coverage.

  • Parameters and training: 4B parameters; trained on Google's curated mix of web text, code, and multilingual data. Same architecture family as Gemma 2 with extended context.
  • Memory footprint: ~2.9 GB at Q4_K_M, ~3.7 GB at Q5_K_M. Fits on 8 GB+ phones; tight on 6 GB phones (use Phi-4 Mini or smaller instead).
  • Speed (tokens/sec): iPhone 17 Pro ~10–13, iPhone 16 Pro ~7–10, Galaxy S25 Ultra ~7–10 (slightly slower than Phi-4 Mini despite similar size due to architecture differences).
  • Quality strengths: natural conversational tone, strong summarisation, broader world knowledge than Phi-4 Mini (Common Crawl exposure), decent multilingual.
  • Quality weaknesses: weaker chain-of-thought reasoning than Phi-4 Mini; slower tokens/sec on the same hardware; not always the first model added to mobile apps (lags Phi-4 Mini in PocketPal AI release timing).
  • Best for: flagship phone users who want a Google-trained model as a Phi-4 Mini alternative, particularly for everyday chat, summarisation, and short drafting.

💡Tip: Gemma 3 4B uses a different chat template than Phi-4 Mini — verify your app uses the correct Gemma template (with <start_of_turn> markers). Wrong template produces broken or repetitive output. PocketPal AI, MLC Chat, and LM Studio detect this automatically; LLM Farm requires manual selection of the Gemma template under Model Settings.

Gemma 3 1B: Lightweight Pick for Older Phones

Gemma 3 1B (Google DeepMind, 2025) is the smallest viable mobile model in 2026 — ~720 MB at Q4_K_M and runs on 4 GB phones. Quality is limited to short coherent responses (1–2 paragraphs) but it is the only option below 1 GB that produces usable output on weak hardware.

  • Parameters and training: 1B parameters; same Gemma 3 family architecture as the 4B model but with reduced training compute. Trained for efficient inference on edge devices.
  • Memory footprint: ~720 MB at Q4_K_M, ~900 MB at Q5_K_M. Runs on iPhone SE 3rd gen, iPhone 12 / 13, older Android (4 GB RAM minimum).
  • Speed (tokens/sec): iPhone 17 Pro ~35–45, iPhone 16 Pro ~28–38, iPhone 14 ~20–28, older Android (4 GB) ~10–15. Fastest model in this lineup on every device.
  • Quality strengths: speed, low memory footprint, coherent short-form responses, low battery drain.
  • Quality weaknesses: weak multi-step reasoning, frequent factual errors on niche topics, repetitive on long generations (>500 tokens), shallow conversational depth.
  • Best for: users with phones below the 6 GB RAM threshold who still want on-device AI, or anyone optimising for battery life on long flights or in low-power scenarios.

💡Tip: Use Gemma 3 1B for short, focused tasks — single-sentence summarisation, one-paragraph drafts, quick definitions, simple translation between major language pairs. Avoid asking it for multi-paragraph explanations, multi-step reasoning, or anything where accuracy on niche facts matters. The model knows it is small; prompting it to "be concise" plays to its strengths.

SmolLM 2 1.7B: Fastest Tokens per Second

SmolLM 2 1.7B (Hugging Face, 2024) is the fastest tokens-per-second mobile model in this lineup on every reference phone. ~26–32 tok/sec on iPhone 17 Pro and ~20–28 on Galaxy S25 Ultra. Best when responsiveness matters more than answer depth.

  • Parameters and training: 1.7B parameters; trained on a Hugging Face-curated mix optimised for small-model efficiency. Architecture tuned for low-latency inference on consumer hardware.
  • Memory footprint: ~1.1 GB at Q4_K_M. Fits on any phone with 6 GB+ RAM with substantial OS headroom.
  • Speed (tokens/sec): iPhone 17 Pro ~26–32, iPhone 16 Pro ~22–28, Galaxy S25 Ultra ~20–28, iPhone 14 Pro ~15–22. Roughly 2× faster than Phi-4 Mini on the same chip.
  • Quality strengths: snappy conversational responses, simple Q&A, autocomplete-style continuation, English-language drafting.
  • Quality weaknesses: weaker reasoning than Phi-4 Mini, narrower world knowledge than Llama 3.2 3B, weaker multilingual than Qwen3 1.7B, occasional hallucination on factual queries.
  • Best for: mid-range phones where latency matters (text-input autocomplete, voice assistant turn-taking, real-time chat), or older flagships where larger models feel sluggish.
Indicative tokens-per-second comparison for 6 mobile LLM models at Q4_K_M (reference devices, not measured by PromptQuorum): Gemma 3 1B (~35–45 on iPhone 17 Pro) and SmolLM 2 1.7B (~26–32) lead on speed; Phi-4 Mini (~13–18) and Gemma 3 4B (~10–13) are slower but smarter.
Indicative tokens-per-second comparison for 6 mobile LLM models at Q4_K_M (reference devices, not measured by PromptQuorum): Gemma 3 1B (~35–45 on iPhone 17 Pro) and SmolLM 2 1.7B (~26–32) lead on speed; Phi-4 Mini (~13–18) and Gemma 3 4B (~10–13) are slower but smarter.

💡Tip: SmolLM 2 1.7B is the strongest pairing for an offline voice assistant stack on mobile — see Build a Local Voice Assistant on Your Phone for the Whisper + LLM + TTS pipeline. The high tokens/sec keeps voice turn-taking under the ~1.5-second perceptual threshold even on mid-range hardware.

Qwen3 1.7B: Strongest Multilingual Mobile Model

Qwen3 1.7B (Alibaba, 2024) is the strongest multilingual mobile model in 2026 — trained on 35+ languages including Chinese, Japanese, Korean, Arabic, German, French, Spanish, and Russian. Best choice for translation, non-English chat, and travel use where the user switches languages mid-conversation.

  • Parameters and training: 1.7B parameters; trained on Alibaba's multilingual corpus with strong representation of CJK languages, Arabic, and major European languages. Architecture optimised for multilingual reasoning.
  • Memory footprint: ~1.1 GB at Q4_K_M. Fits on any phone with 6 GB+ RAM.
  • Speed (tokens/sec): iPhone 17 Pro ~24–32, iPhone 16 Pro ~20–28, Galaxy S25 Ultra ~18–26, iPhone 14 Pro ~14–20. Comparable speed to SmolLM 2.
  • Quality strengths: native-quality output in 35+ languages (most small models are English-first with weak multilingual fallback), strong translation between major language pairs, coherent CJK output where Phi-4 Mini and Llama 3.2 produce broken characters.
  • Quality weaknesses: English-only reasoning slightly weaker than Phi-4 Mini, shorter natural creative writing than Gemma 3 4B, weaker tool-calling than Llama 3.2 3B.
  • Best for: non-English users (especially Chinese, Japanese, German, Spanish, French speakers), travellers needing offline translation, or developers building multilingual mobile features.

💡Tip: For one-shot translation between two specific language pairs, Qwen3 1.7B usually beats a larger English-first model running translation as a secondary task. For a German user chatting in German, Qwen3 produces noticeably more natural output than Phi-4 Mini despite being 60% smaller. The right rule: pick the model trained for your primary language, not the model with the most parameters.

Llama 3.2 3B: Reliable 3B Workhorse

Llama 3.2 3B (Meta, 2024) is the most widely supported 3B model in 2026 — broadest app compatibility, strongest tool-calling support among the six, and the largest community fine-tune ecosystem. Slightly behind Phi-4 Mini on raw quality but more reliable in edge cases and better-supported by mobile apps.

  • Parameters and training: 3B parameters; trained on Meta's large pretraining corpus with instruction-tuning for chat and tool use. Same Llama 3 architecture as the 8B and 70B siblings.
  • Memory footprint: ~2.2 GB at Q4_K_M, ~2.8 GB at Q5_K_M. Fits on 8 GB+ phones with comfortable OS headroom; works on tight 6 GB phones if other apps are closed.
  • Speed (tokens/sec): iPhone 17 Pro ~16–22, iPhone 16 Pro ~12–18, Galaxy S25 Ultra ~12–18, iPhone 14 Pro ~7–11.
  • Quality strengths: broad world knowledge, robust tool-calling and function-calling support (best-in-class among sub-4B models), reliable chat behaviour, mature ecosystem of fine-tunes for specific tasks (medical, legal, coding).
  • Quality weaknesses: weaker chain-of-thought reasoning than Phi-4 Mini, slightly lower MMLU scores at similar size, less natural conversational tone than Gemma 3 4B.
  • Best for: mobile apps that need tool calling or function calling (RAG over local documents, on-device agent workflows), or users who want the model with the largest community fine-tune library.

💡Tip: Llama 3.2 3B is the only model in this lineup with broad tool-calling support reliable enough for on-device agent workflows — see Local AI Agents with MCP 2026 for the agent layer. Phi-4 Mini and SmolLM 2 can technically tool-call but Llama 3.2 3B is the only one production-ready in 2026.

SmolLM 3 and Gemma 3n: Newer Small Models

SmolLM 3 (Hugging Face, July 2025) and Gemma 3n (Google) are the two newer small models readers ask about most. Neither is in the six-model speed comparison above, because no verified on-device tokens/sec numbers exist for them at publication time. There is no SmolLM 4 as of this page's last update: Hugging Face had not published one, and SmolLM 3 was the newest generation in its SmolLM family.

  • SmolLM 3 (3B): a 3B-parameter model trained on about 11 trillion tokens, with a thinking and a non-thinking mode. It was trained with a 64k context and can handle up to 128k at inference using YaRN extrapolation. Official language support: English, French, Spanish, German, Italian and Portuguese. Hugging Face reports it as competitive with the larger Qwen3 4B and Gemma 3 4B on its base-model benchmarks, while noting that Qwen3 4B generally scores highest.
  • SmolLM 3 on a phone: at 3B parameters it sits in the Llama 3.2 3B size class, so expect a Q4_K_M file of roughly 2 GB and the 8 GB-RAM tier from the per-tier table. That is an estimate from parameter count, not a measurement.
  • Gemma 3n (E2B and E4B): the "E" stands for effective parameters. E2B loads over 5 billion raw parameters but, with parameter skipping and per-layer-embedding caching, can run with a memory load of just under 2 billion (1.91B). Gemma 3n accepts text, image and audio input, was trained on over 140 languages, and has a 32K-token context window.
  • Which one to choose: SmolLM 3 if you want a thinking-mode 3B model for English or a major European language. Gemma 3n if your app supports image or audio input. Otherwise the six models above remain the default of this comparison.

💡Tip: Judge any new small model by RAM first, speed second. Find its Q4_K_M file size, match it to the per-tier table, and only then read the benchmark claims. App support also lags model releases — if a model is missing from your app's curated list, sideload the GGUF from Hugging Face.

Quantisation for Mobile: Q4_K_M Is the Default

Q4_K_M is the standard quantisation for mobile LLM inference in 2026 — preserves ~95% of the original model's quality at one-quarter the file size. Use Q5_K_M or Q6_K only on 12 GB+ phones (iPhone 17 Pro Max, Galaxy S25 Ultra) where the extra memory headroom is genuinely free.

📍 In One Sentence

Q4_K_M is the mobile default — ~95% quality at one-quarter size. Q5_K_M / Q6_K are only worth it on 12 GB+ phones.

💬 In Plain Terms

Models on Hugging Face are published at full precision (each parameter stored as a 16-bit number). On phones, you download a quantised version where each parameter is squeezed into 4 bits — making the file four times smaller and inference roughly four times faster, with a small quality cost. Q4_K_M is the variant that everyone in 2026 settled on as the right balance for phones. Higher Q numbers (Q5, Q6, Q8) mean less squeezing and better quality but bigger files; Q4 is the sweet spot for phone constraints.

  • Q4_K_M (recommended default): 4-bit quantisation with K-quants and "M" mixed precision. ~95% of original quality. Standard for mobile in 2026. All six models available in this format on Hugging Face.
  • Q5_K_M (for 12 GB+ phones): 5-bit quantisation. ~98% of original quality. ~25% larger files. Worth it on iPhone 17 Pro Max (12 GB) or Galaxy S25 Ultra (12 GB) for Phi-4 Mini and Llama 3.2 3B; not worth the RAM cost on 8 GB phones.
  • Q6_K (rarely needed): 6-bit quantisation. ~99% of original quality. ~50% larger files. Only worth it for memory-rich phones running models you genuinely care about quality on (e.g., long-form drafting where every percentage point of quality matters).
  • Q8_0 (avoid on mobile): 8-bit quantisation. ~99.5% of original quality. Roughly 2× the size of Q4_K_M. Not worth the RAM cost on phones; reserve for desktop/laptop use.
  • Q3_K_M / Q2_K (only for very constrained phones): 3-bit and 2-bit quantisation. Quality drops to ~85–90%. Use only if Gemma 3 1B at Q4_K_M still does not fit (rare in 2026).
GGUF quantisation levels for mobile: Q4_K_M is the default for 6 GB+ phones (~95% quality, ×0.5 file size); Q5_K_M and Q6_K only for 12 GB+ phones; Q8_0 is desktop-only; Q3_K_M is the last resort for 4 GB phones.
GGUF quantisation levels for mobile: Q4_K_M is the default for 6 GB+ phones (~95% quality, ×0.5 file size); Q5_K_M and Q6_K only for 12 GB+ phones; Q8_0 is desktop-only; Q3_K_M is the last resort for 4 GB phones.

⚠️Warning: Do not download the same model in multiple quantisations expecting to "test which is best" on a phone. The quality differences between Q4_K_M and Q5_K_M are real but small, and you will burn 5+ GB of phone storage hosting redundant variants. Pick Q4_K_M, run it for a week of real use, and only upgrade to Q5_K_M if you have specific evidence the quality is insufficient.

Model Download Sizes in MB

At Q4_K_M, the six models range from ~720 MB (Gemma 3 1B) to ~2,900 MB (Gemma 3 4B). Budget roughly 500 MB of extra RAM for inference overhead on top of the file size, and keep at least 3 GB of free storage per 3B–4B model you plan to keep.

📍 In One Sentence

Q4_K_M file sizes: Gemma 3 1B ~720 MB, Qwen3 1.7B and SmolLM 2 ~1,100 MB, Llama 3.2 3B ~2,200 MB, Phi-4 Mini ~2,700 MB, Gemma 3 4B ~2,900 MB.

💬 In Plain Terms

The file you download is also roughly the amount of RAM the model occupies while it runs, plus a little overhead. That is why file size, not parameter count, is the number to check against your phone. If a model plus the operating system does not fit in your RAM, the phone will close it mid-answer.

Model
Q4_K_M size (MB)
Minimum phone RAM
Size-to-quality note
Gemma 3 1B~7204 GBSmallest viable model; short answers only
Qwen3 1.7B~1,1006 GBBest multilingual output per MB
SmolLM 2 1.7B~1,1006 GBFastest per MB; light reasoning
Llama 3.2 3B~2,2008 GB (6 GB is tight)Smaller than the 4B models; best tool calling
Phi-4 Mini~2,7008 GBStrongest reasoning per parameter
Gemma 3 4B~2,9008 GBLargest file; natural summarisation; accepts images

On accuracy per byte: this article reports no accuracy benchmark of its own, so treat the last column as a qualitative guide. For offline summarisation, the 1.1 GB models (Qwen3 1.7B, SmolLM 2) are the efficient middle; Phi-4 Mini is the quality ceiling if you can spare 2.7 GB.

Per-Tier Verdict: Flagship vs Mid-Range vs Budget

Phone tier dictates the model ceiling — chip generation and RAM matter more than brand. A flagship phone (8 GB+ RAM, A18 Pro / A19 Pro / Snapdragon 8 Elite) runs 3.8B–4B models comfortably; a mid-range phone (6–8 GB RAM, older flagship chip) runs 1.7B–3B; a budget or older phone (4–6 GB RAM) runs 1B–1.7B.

📍 In One Sentence

Flagship phones (8 GB+) → Phi-4 Mini 3.8B; mid-range (6–8 GB) → SmolLM 2 1.7B or Llama 3.2 3B; budget or older (4–6 GB) → Gemma 3 1B or Qwen3 1.7B.

💬 In Plain Terms

Match the model to your phone, not your aspirations. A 3.8B model on a 6 GB phone produces frustrating 3-second pauses and crashes when other apps need memory. A 1B model on a flagship phone leaves capability on the table. Pick the largest model your phone can run comfortably with the OS and at least one other app open.

Phone Tier
Examples
Recommended Model
Why
Flagship (8–12 GB RAM)iPhone 17 Pro / Pro Max, iPhone 16 Pro, Galaxy S25 Ultra, OnePlus 13Phi-4 Mini (3.8B Q4_K_M)Smartest model the chip sustains at usable speed
Older flagship (8 GB RAM)iPhone 15 Pro, Galaxy S24 Ultra, Pixel 9 ProLlama 3.2 3B or Phi-4 MiniLlama 3.2 3B for tool calling; Phi-4 Mini for raw quality
Mid-range (6–8 GB RAM)iPhone 14 Pro, Pixel 9, Snapdragon 8 Gen 2 phonesSmolLM 2 1.7B or Qwen3 1.7BSnappy speed; fits with OS headroom
Budget / older (4–6 GB RAM)iPhone 14, mid Snapdragon 7-series, older AndroidGemma 3 1B or Qwen3 1.7BSmallest viable models that still produce coherent output
Very old (4 GB RAM)iPhone SE 3rd gen, older 4 GB AndroidGemma 3 1BOnly model that fits; limited reasoning, fast tokens/sec
Unsupported (<4 GB)iPhone SE 2nd gen, ancient AndroidRemote-connect to home machine insteadOn-device LLM not practical; use a tablet/phone as a UI for a home Ollama server
Mobile LLM recommendations by phone tier: flagship phones (8–12 GB RAM) → Phi-4 Mini 3.8B; older flagship (8 GB) → Llama 3.2 3B; mid-range (6–8 GB) → SmolLM 2 1.7B; budget (4–6 GB) → Qwen3 1.7B; very old (4 GB) → Gemma 3 1B.
Mobile LLM recommendations by phone tier: flagship phones (8–12 GB RAM) → Phi-4 Mini 3.8B; older flagship (8 GB) → Llama 3.2 3B; mid-range (6–8 GB) → SmolLM 2 1.7B; budget (4–6 GB) → Qwen3 1.7B; very old (4 GB) → Gemma 3 1B.

💡Tip: For the app side of the equation, see the iPhone and Android sister guides — they cover which apps actually expose each of these models on each platform. App availability sometimes lags model availability: Gemma 3 4B was on Hugging Face six months before PocketPal AI added a one-tap downloader for it. If a model is missing from your app's curated list, it can usually be sideloaded as a GGUF from Hugging Face.

Best Phones for Running Local LLMs

The best phone for local AI is the one with the most RAM and the newest flagship chip. In this article's indicative figures iPhone 17 Pro (A19 Pro) is the fastest, with Galaxy S25 Ultra (Snapdragon 8 Elite) roughly 10–20% slower on the same Q4_K_M files. RAM decides which models fit; the chip decides how fast they run.

📍 In One Sentence

Pick a phone with 8 GB+ RAM for 3B–4B models and 12 GB for Q5_K_M — iPhone 17 Pro / 17 Pro Max, iPhone 16 Pro, Galaxy S25 Ultra and OnePlus 13 are the phones used in this article.

💬 In Plain Terms

Check your phone's RAM before anything else; it sets the largest model you can run. Chip generation is the second factor and decides speed. You do not need to buy a new phone for local AI: a phone you already own runs the 1B–1.7B models at full speed, and only the 3B–4B models need a recent flagship.

  • RAM first: 4 GB runs Gemma 3 1B only; 6 GB runs the 1.7B models; 8 GB runs the 3B–4B models; 12 GB (iPhone 17 Pro Max, Galaxy S25 Ultra) adds Q5_K_M headroom. Find your RAM in the phone's specifications, because some Android models ship in several RAM variants.
  • Chip second: A19 Pro and A18 Pro (iPhone) and Snapdragon 8 Elite (Android flagships) are the chips behind the speeds in this article. Older flagship chips run the same models, only slower — iPhone 14 Pro runs Phi-4 Mini at ~6–10 tok/sec.
  • iPhone vs Android: on identical Q4_K_M files iPhone is 10–20% faster in this article's figures, mostly because Termux+Ollama on Android runs CPU-only and does not use the Snapdragon NPU the way MLC Chat uses Apple Metal. A better Android runtime closes that gap; the hardware is not the limit.
  • Heat and battery: sustained generation keeps the chip under load, and phones lower their clock speed when they get hot, so tokens/sec drops in long sessions. This article has no measured throttling figures. The practical levers are a smaller model (Gemma 3 1B draws far less power than the 3B–4B models, which use 2–3× more per response), shorter outputs, and not charging while the model runs.
  • Storage: each 3B–4B model takes 2.2–2.9 GB; leave room for one or two models, not a library of variants.

💡Tip: Mid-range Android phone (Samsung Galaxy A-series and similar)? Look up the RAM variant of your exact model, then use the per-tier table: 8 GB runs SmolLM 2 1.7B or Llama 3.2 3B, 6 GB runs the 1.7B models, 4 GB runs Gemma 3 1B. The chip matters less than the RAM figure.

Multimodal on Phones: Image Captions and Alt Text

Of the six core models, only Gemma 3 4B accepts images; Gemma 3 1B, Phi-4 Mini, Llama 3.2 3B, Qwen3 1.7B and SmolLM 2 are text-only. Google lists the 4B, 12B and 27B Gemma 3 models as vision-capable. Gemma 3n (E2B / E4B) adds image and audio input, and Hugging Face's SmolVLM2 family (256M, 500M and 2.2B parameters) is built for vision and video from the start.

  • Best pick for image captions on a flagship phone: Gemma 3 4B (~2.9 GB at Q4_K_M, 8 GB+ RAM). It is the only one of the six that can read a photo and write a caption or alt text.
  • Budget or CPU-only Android: SmolVLM2 256M or 500M are the smallest vision models in the Hugging Face family and the realistic starting point for weak hardware. This article has no verified speed figures for them.
  • App support is the real limit: in llama.cpp-based apps, image input needs a separate vision projector file next to the model. If your app does not expose image input for the model, a vision-capable model will still only chat in text. Check the app before downloading.
  • Alt-text workflow: ask for one or two plain sentences describing what is visible, then read the result before publishing it. Small vision models can misread text inside images and mislabel people or objects, so keep a human check on anything meant for accessibility or social posts.
  • Latency expectations: token generation alone takes ~55–77 ms per token for Phi-4 Mini (13–18 tok/sec) and ~22–29 ms per token for Gemma 3 1B (35–45 tok/sec), before any image encoding. Sub-100 ms end-to-end responses are therefore not a realistic target for captioning on a phone.

💡Tip: For dense documents, receipts or screenshots full of small text, use a cloud model or a desktop vision model instead. On-device captioning is best for short descriptions of photos, where an occasional miss is cheap to correct.

Common Mistakes

  • Picking a model larger than the phone's RAM allows. Phi-4 Mini on a 6 GB phone runs at 3–5 tok/sec and crashes when iOS / Android reclaims memory for another app. Match the model to your tier (see the per-tier table above).
  • Downloading multiple quantisation variants of the same model. Pick Q4_K_M and stop. Five GB of redundant Q5/Q6 variants on a 256 GB phone is wasted space, and the quality differences are not perceptible in everyday chat.
  • Using SmolLM 2 1.7B for multi-step reasoning. It is the fastest model but not the smartest. For chain-of-thought tasks (math, planning, complex reasoning), use Phi-4 Mini even if the slower tokens/sec feels frustrating. Speed without quality is just a faster wrong answer.
  • Asking Phi-4 Mini for non-English output without a multilingual prompt prefix. Phi-4 Mini handles common European languages adequately but produces uneven output in CJK or Arabic. For multilingual use, install Qwen3 1.7B alongside Phi-4 Mini and switch per language.
  • Expecting cloud-AI quality from any of these models. All six are 1B–4B, which means roughly 60–80% of the capability of GPT-5.5 on chat tasks and far less on complex reasoning. Use them for what they are good at (private chat, summarisation, drafting, translation) and use cloud or remote-connect for what requires a 70B+ model.
  • Confusing Phi-4 Mini (3.8B) with the older Phi-3 Mini (3.8B). They share a parameter count but Phi-4 Mini's training data and chat template are different. Always confirm the model identifier in the GGUF filename — phi-4-mini-instruct not phi-3-mini-4k-instruct.

Sources

Frequently Asked Questions

Which mobile model is fastest on iPhone?

Gemma 3 1B is the absolute fastest at ~35–45 tokens/sec on iPhone 17 Pro, but it is the smallest model in this lineup. Among 1.7B models (where speed and quality are balanced), SmolLM 2 1.7B is the fastest at ~26–32 tokens/sec. Among models that produce flagship-quality output, Phi-4 Mini at ~13–18 tokens/sec is the fastest "smart" option. Pick by use case: if responsiveness matters more than depth, SmolLM 2; if depth matters more, Phi-4 Mini.

Is there a Gemma 3 2B or Gemma 3 7B model?

No. Gemma 3 officially ships in five sizes: 270M, 1B, 4B, 12B, and 27B — there is no Gemma 3 2B or Gemma 3 7B. The confusion usually comes from Gemma 2, which did ship a 2B model (Google DeepMind, July 2024) alongside 9B and 27B. If you are looking for the closest match to "Gemma 3 2B," the two real options are Gemma 3 1B (smaller, faster) and Gemma 3 4B (larger, smarter) — both are compared in this article. In April 2026, Google also released Gemma 4, with a different size lineup (Effective 2B and Effective 4B, plus 26B MoE and 31B dense variants). Gemma 4 is a separate model family from Gemma 3 and is not yet covered in this mobile comparison, since no verified on-device tokens/sec numbers exist for it at publication time.

Do these models need a GPU, or do they run CPU-only?

All six models run without a discrete GPU. On iPhone, MLC Chat uses Apple's Metal API to accelerate inference on the Neural Engine/GPU cores, but PocketPal AI and LLM Farm (llama.cpp) run primarily on CPU. On Android, Termux+Ollama is CPU-only today — it does not yet leverage the Snapdragon Hexagon NPU, which is why Android tokens/sec trails iPhone by roughly 10–20% on identical Q4_K_M files (see the comparison table above). In practice: Phi-4 Mini on Galaxy S25 Ultra runs CPU-only at ~10–15 tokens/sec, and no model in this lineup requires a dedicated GPU to produce usable output.

Does Phi-4 Mini really beat 7B models on phone?

It beats older 7B models (Llama 3.3 7B, Mistral Small v0.1) on standard benchmarks like MMLU and reasoning tasks despite being half the size. It does NOT beat current 7B models (Llama 3.3 7B, Mistral Small v0.3) on raw capability — those still lead on broad knowledge and complex reasoning. The reason Phi-4 Mini punches above its weight is Microsoft's training-data mix (heavy on synthetic reasoning chains and high-quality text). On phone, 7B models are usually too slow to be practical anyway, so Phi-4 Mini wins by default.

Can SmolLM 2 run on a 4-year-old phone?

Yes, on most 4-year-old flagships. SmolLM 2 1.7B at Q4_K_M needs ~1.1 GB RAM for the model plus ~500 MB for inference overhead — fits on iPhone 13 (6 GB), iPhone 12 Pro Max (6 GB), and equivalent Android (6 GB+). On 4 GB phones from 2021 (iPhone 12, base Android), it technically loads but is unstable under any other memory pressure; use Gemma 3 1B instead.

Which model handles translation best on mobile?

Qwen3 1.7B for any pair involving Chinese, Japanese, Korean, Arabic, German, French, Spanish, or Russian. It was trained with strong multilingual representation and produces native-quality output where English-first models (Phi-4 Mini, Llama 3.2 3B) produce stilted or broken results. For European-language pairs only, Gemma 3 4B is a viable second choice. For one-off translations between English and one specific language, an installed translation app (Google Translate, DeepL) is often better than any local LLM — local models shine when you need translation chained with chat or summarisation in the same conversation.

Do I need a flagship phone to run these well?

No, only for the largest models (Phi-4 Mini 3.8B, Gemma 3 4B, Llama 3.2 3B). Mid-range phones with 6–8 GB RAM run SmolLM 2 1.7B and Qwen3 1.7B at full speed (~20–28 tokens/sec). Budget phones with 4–6 GB RAM run Gemma 3 1B at ~15–25 tokens/sec. The honest answer: if you do not already own a flagship phone, do not buy one for local AI — the smaller models on your existing phone are good enough for most use cases.

Which model has the smallest battery drain?

Gemma 3 1B by a wide margin — smallest model means fewest computations per token, which means lower CPU/GPU load and lower power draw. SmolLM 2 1.7B and Qwen3 1.7B are next. The 3B–4B models (Phi-4 Mini, Llama 3.2 3B, Gemma 3 4B) draw 2–3× more power per response. For long flights or extended off-grid use where battery matters most, Gemma 3 1B is the right pick despite the quality cost.

Can mobile models handle multi-turn conversations?

Yes for short conversations (5–10 turns), with quality degrading after that. All six models have 4k–8k token context windows; longer conversations exceed the window and the model loses track of earlier turns. For ongoing chat that needs memory beyond a session, the practical pattern is: summarise the conversation periodically, store the summary, and feed it back as context. Most mobile apps (PocketPal AI, Private LLM) do this automatically; LLM Farm requires manual configuration.

Do these models work with voice input?

Yes, when paired with a Whisper speech-to-text layer. The standard offline mobile voice stack in 2026 is: Whisper (small or tiny model) for speech-to-text → Phi-4 Mini or SmolLM 2 for response generation → Apple TTS or Android TTS for speech synthesis. SmolLM 2 1.7B is the best LLM choice for voice because the high tokens/sec keeps voice turn-taking responsive — see Build a Local Voice Assistant on Your Phone for the full pipeline.

Which is best for offline travel use?

For travel where you switch languages and need translation: Qwen3 1.7B. For travel where you mostly need an English-language reference (questions, summarising travel docs, drafting emails): Phi-4 Mini on a flagship phone, SmolLM 2 1.7B on a mid-range phone. Travel use is the strongest case for local AI overall — no roaming data needed, no cloud-API costs, and no risk of cloud dependencies failing in low-connectivity areas. Download the model before the trip; it works for the whole journey on a single charge if used moderately.

What is the best phone for running a local LLM?

The phone with the most RAM and the newest flagship chip. In this article's indicative figures iPhone 17 Pro (A19 Pro) is the fastest, and Galaxy S25 Ultra (Snapdragon 8 Elite) is roughly 10–20% slower on the same Q4_K_M files. For 3B–4B models like Phi-4 Mini you need 8 GB RAM or more; 12 GB (iPhone 17 Pro Max, Galaxy S25 Ultra) adds headroom for Q5_K_M. If you already own a phone with 6 GB RAM, you can run the 1.7B models at full speed, so do not buy a new phone just for local AI.

Will running a local LLM overheat my phone?

Sustained generation keeps the chip under heavy load, so the phone warms up and may lower its clock speed, which reduces tokens/sec in long sessions. This article has no measured throttling figures. To keep temperatures down, use a smaller model (Gemma 3 1B draws far less power than the 3B–4B models, which use 2–3× more power per response), keep answers short, and avoid charging while the model runs.

Can a mid-range Samsung Galaxy or other Android phone run these models?

Yes, if the RAM is enough. Look up the RAM variant of your exact model, then match it: 8 GB runs SmolLM 2 1.7B or Llama 3.2 3B, 6 GB runs Qwen3 1.7B or SmolLM 2 1.7B, 4 GB runs Gemma 3 1B only. RAM matters more than the chip; the chip mainly changes speed. Android tokens/sec is about 10–20% below iPhone on the same file in this article's figures.

How big are these models in megabytes?

At Q4_K_M: Gemma 3 1B ~720 MB, Qwen3 1.7B ~1,100 MB, SmolLM 2 1.7B ~1,100 MB, Llama 3.2 3B ~2,200 MB, Phi-4 Mini ~2,700 MB and Gemma 3 4B ~2,900 MB. The file is also roughly the RAM the model occupies while running, plus about 500 MB of inference overhead. Q5_K_M files are about 25% larger.

Is there a SmolLM 3 or SmolLM 4?

SmolLM 3 exists: Hugging Face released it in July 2025 as a 3B-parameter model with thinking and non-thinking modes, a 64k training context (up to 128k with YaRN) and support for English, French, Spanish, German, Italian and Portuguese. As of this page's last update there is no SmolLM 4; Hugging Face had not published one. SmolLM 3 is not in this article's speed comparison because no verified on-device numbers exist for it, but at 3B it sits in the Llama 3.2 3B size class.

What is Gemma 3n and is it better for phones than Gemma 3?

Gemma 3n is a Google model family built for on-device use, in E2B and E4B sizes. The "E" means effective parameters: E2B loads over 5 billion raw parameters but can run with a memory load of just under 2 billion (1.91B). It accepts text, image and audio input and covers over 140 languages. It is a different trade-off from Gemma 3 4B, not a drop-in upgrade, and it is not in this article's speed comparison because no verified numbers exist yet.

Can a phone LLM write image captions and alt text?

Only with a vision-capable model. Of the six models here, only Gemma 3 4B accepts images; the other five are text-only. Gemma 3n and SmolVLM2 (256M, 500M, 2.2B) also handle images. Your app must expose image input for the model, because llama.cpp-based apps need a separate vision projector file. Review every caption before publishing — small vision models can misread text and mislabel objects.

Is there a 2.5B-parameter model for phones?

Not among the mainstream phone models covered here. The nearest sizes are the 1.7B tier (SmolLM 2, Qwen3) and the 3B tier (Llama 3.2 3B, SmolLM 3). Gemma 3n E2B is the closest in spirit: it loads over 5 billion raw parameters but runs with an effective memory load of just under 2 billion.

Which model gives the best accuracy per megabyte for offline summarisation?

This article reports no accuracy benchmark of its own, so the answer is qualitative. The 1.1 GB models (Qwen3 1.7B, SmolLM 2 1.7B) are the efficient middle for short summaries, Qwen3 for non-English text. Phi-4 Mini (~2.7 GB) is the quality ceiling among the six if you can spare the memory, and Gemma 3 1B (~720 MB) is the smallest model that still produces coherent short summaries.

Can a phone LLM respond in under 100 milliseconds?

Not for a full answer. Token generation alone takes ~55–77 ms per token for Phi-4 Mini (13–18 tok/sec) and ~22–29 ms per token for Gemma 3 1B (35–45 tok/sec) on iPhone 17 Pro, so even a 20-token reply takes around half a second with the fastest model. Sub-100 ms is realistic only for a handful of tokens, such as a very short classification output, not for chat or captioning.

Are mobile models still useful in 2027?

Yes, but the specific model names will change. The mobile small-LLM frontier moves roughly every 6–9 months — by Q4 2026 there will likely be new ~3B models that outperform Phi-4 Mini, and by mid-2027 the 1B–2B class will likely match what 3B–4B models do today. The category does not become obsolete; specific picks rotate. Re-check this article (refresh due 2027-04-19) for the next-generation lineup.

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