Embla 1: A Fast, Private AI Model for Your Phone

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Embla 1: A Reasoning Model That Runs on Your Phone — Private, Offline, and Genuinely Fast

Embla 1: A Reasoning Model That Runs on Your Phone — Private, Offline, and Genuinely Fast AI disclosure: This article was drafted with AI assistance and checked against Sevren's own launch blog and architecture post, read 15 November 2026. I have not run Embla 1 hands-on — nobody outside Sevren has had it long enough. Nothing here is sponsored, and there are no affiliate links on this page. Sevren, a Copenhagen startup, has released Embla 1: a reasoning model designed to run entirely on a phone. Private, offline, and free per request — no API call, no server round-trip, no per-token bill. If that sounds like the pitch every on-device model makes, the difference here is the evidence Sevren published alongside it: a full architecture write-up, a training recipe, and benchmark numbers measured on an actual iPhone, including the places where their model loses. That last part is why this launch is worth your attention. Most small-model launches hand you a cherry-picked leaderboard. Sevren handed us their model's weaknesses too. What Embla 1 actually is Embla 1 is a 10-billion-parameter mixture-of-experts model with only about 1 billion parameters active per token (that's the "A1B" in its size class). It's a reasoning model — it thinks through problems step by step before answering — built from the ground up for phone hardware rather than shrunk down from a server model. The headline numbers, all measured by Sevren at 4-bit precision: 57.4 average across five benchmarks: BFCL (tool use), IFBench (instruction following), AA-Omniscience, GPQA (graduate-level science questions) and MATH-500. 1.77 seconds end to end on an iPhone 18 Pro for a 1,024-token prompt producing 256 output tokens, using Sevren's own Metal implementation. In llama.cpp the same task takes 2.18 seconds. 888 tokens per answer on average — the fewest of any reasoning model Sevren tested. The next-cheapest, Gemma 4 E4B, needed 1,577 tokens for the same answers. Qwen3.5-4B needed 6,619. That last number is the one developers should care about most. On a phone, every token costs battery and time. A reasoning model that reaches a good answer in 888 tokens instead of 6,600 is not a little faster — it's a different class of product. It's the difference between an assistant that feels instant and one that makes you watch a spinner. The honest caveats (read these first) Sevren's numbers are Sevren's numbers. They say so themselves, and the caveats matter: Self-measured, one phone, one harness. All benchmarks were run by Sevren on a single iPhone 18 Pro, with an uncertainty of about ±1 point per average. Other setups may rank the models differently. No independent lab has verified these results yet. Five of the twelve models Sevren tested scored higher. LFM2.5-8B-A1B is 1.4 points ahead (58.8 vs 57.4) — but takes twice as long. LFM2.5-2.6B, Granite 4.2 3B, Gemma 4 E4B and Qwen3.5-4B are 2.6 to 5.3 points ahead, but take 3.0 to 5.5 times as long. Embla 1 trades a few points of accuracy for a large speed advantage. Whether that trade is right depends on what you're building. Math is the weak spot. Embla 1 scores 76.1 on MATH-500, against 89.6 for LFM2.5-8B-A1B and 90.5 for LFM2.5-2.6B. Sevren says it plainly: "There is still a lot to improve." If your app leans on hard math, this is not your model yet. The latency claim depends on Sevren's own runtime. The 1.77-second figure uses their custom Metal implementation. In the standard llama.cpp runtime it's 2.18 seconds — still fast, but the headline number is tied to their code, not just their weights. None of that makes the launch unimpressive. It makes it checkable — and that's rarer than it should be. Why it's fast: the architecture in plain language Embla 1 is 32 layers, 2,048 wide, with 8 of 96 routed experts plus one shared expert active per token. Four design choices do most of the work: Sevren Sparse Attention. The full-attention layers use ReLU² instead of softmax, which makes about 95% of attention weights exactly zer...

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