Every AI lab assumes more languages make a model worse.We bet the opposite.
Born as the general-purpose LLM underneath Lara 4 Translation AI, Lara Mini was built as the most multilingual and multicultural model from day one. At the end of its 6T‑token training it ranks #1 of 36 models in its class worldwide.
Diversity isn't a cost to intelligence. It's a source of it.
The belief
The big labs train mostly on English and treat other languages and cultures as noise that costs quality.
The bet
Translated, whose job is translation, built Lara on real text written in many languages and cultures. Seeing one idea through many of them, the model understands the idea better.
The proof
#1 of 36 models with up to 2B active parameters worldwide, trained on 6T tokens, 128K context.
The run at a glance
Architecture
0.6Bactive parameters per token (4 experts)
7.2Btotal parameters, MoE with 128 experts, 1 shared
34layers: memory (GDN), window and global attention 128Ktokens of context window
Data
6Ttraining tokens
39%of raw tokens filtered
44 + ENlanguages, all 24 EU official ones
151,643tokens in vocabulary, low resource rebalanced
How we built it
A hybrid, sparse design for the most multilingual model
A hybrid, sparse model: 34 layers, 128 experts
Three kinds of layer, one repeating unit
windowmemorywindowmemoryglobal
14 memory · Gated DeltaNet: a linear-attention state per head, updated token by token
13 window · attention over the last 512 tokens, RoPE positions
7 global · full attention, no positional encoding; the last layer is always global
Mixture of experts in every layer
4 of 128 routed experts chosen per token by a router
1 shared expert always on
0.6B of 7.2B parameters active per token; hidden size 1,024, tied input and output embeddings
Optimizer
Muon + AdamW
Each momentum update is orthogonalised (Newton–Schulz) before it is applied, so every direction of a weight matrix moves at a similar rate instead of a few dominating.
4,096-token context window in training; 151,643-token vocabulary.
The most multilingual and multi-cultural model
6T training tokens from a pool deduplicated across all sources: 39% of raw tokens were filtered out. No source is repeated more than 2 epochs (wiki and high-quality math excepted); plain web is never repeated.
English2.49TWeb HQ 1.34T · Synthetic HQ 0.66T · PDFs 0.32T · Web medium 0.15T · Wiki 0.02T
Multilingual2.18TWeb 0.79T · Web HQ 0.66T · PDFs 0.4T · Synthetic 0.28T · Wiki 0.05T
Code0.77TReal code 0.45T · Synthetic 0.31T
Math0.35TMath HQ 0.21T · Math web 0.15T
SFT-like0.21TQA 0.12T · Math 0.07T · Code 0.02T
Three stages, one learning-rate decay
Main · 4.8T0–80% · to step 572.2k
Transition · 0.6T80–90% · to step 643.7k
Anneal · 0.6T90–100% · to step 715.3k
Learning-rate scheduleEvaluated model: end of training, 6T tokens.
Each stage is a separate data blend loaded at its step boundary. Transition: a bridge between the two at the stable learning rate, where the mix already moves halfway toward the anneal's (more high-quality, QA, code and math data) so the model adapts before the decay. Anneal: the highest-quality mix, while the learning rate decays to zero over the last 10%.
Each stage leans further into code, math and SFT-like data
English
Multilingual
Code
Math
SFT-like (English-only) Main
44%37%12%
Transition
35.5%35%14.5%8%
Anneal
27%33%17%11%12%
Whole run
41%36%13%
Share of each stage's tokens. The data are shuffled, not fed domain by domain: every batch of 2,048 sequences mixes all domains in about these proportions. SFT-like (QA, math and code instruction data): 2% in main, 3.5% over the whole run.
Methodology
26 benchmarks, one harness, every model
Overall = ¼ English + ¼ Multilingual + ¼ Math + ¼ Code. Every model is scored by us with lm-evaluation-harness and the same answer-format corrections. Each multilingual benchmark is averaged over our 44 non-English training languages.
English10
NLI
PAWS
Belebele (en) COPA
PIQA
HellaSwag
WinoGrande
ARC-C
MMLU
MMLU-Pro
Multilingual9
XNLI
PAWS-X
Belebele
XCOPA
Global PIQA NP
Global PIQA P
T-HellaSwag
INCLUDE
Global MMLU
Math3
GSM8K
MATH
MGSM
Code4
MBPP
MBPP+
CRUX-I
CRUX-O
No peeking: the final training stage skips every document that holds a benchmark item.
Results
#1 of 36 in tiny models worldwide
First on Overall, Text and Multilingual, 3.1 points ahead of granite-4.0-h-tiny on Overall.
Overall
Tiny models ranking
#1
out of 36 models
Category ranking
Math & Code#1
Text#1
Multilingual#1
Benchmark definition. Our model is the final checkpoint of the 6T‑token run, with its context extended to 128K; its anneal skips every document holding a benchmark item. Its class: models with up to 2B active parameters (gemma-4-E2B at its 2B effective size). Multilingual scores are averaged over the 44 non-English languages we train on, for every model.
Rankings
Top 12 tiny models
36 models with up to 2B active parameters (gemma-4-E2B at its 2B effective size, 5.1B total). Multilingual on our training languages. Bars start from 0.
OverallEuropeTextMath & Code
#1Lara Mini Base (A0.6B/7.2B)57.0
#2IBM - granite-4.0-h-tiny (1B active)53.9
#3Alibaba - Qwen3-1.7B53.8
#4Falcon-H1-1.5B-Deep53.2
#5Hunyuan-1.8B52.2
#6LFM2.5-8B-A1B (1.7B active)50.8
#7Trinity-Nano (1B active)50.7
#8LFM3-1.7B49.2
#9Qwen3.5-2B48.9
#10Falcon-H1-1.5B48.8 #11Qwen2.5-1.5B47.6
#12granite-4.0-1b47.2 ⋯
#17Google - gemma-4-E2B41.9 Overall = ¼ English + ¼ Multilingual + ¼ Math + ¼ Code.
#1Lara Mini Base (A0.6B/7.2B)57.0
#2Bielik-1.5B-v3 (Qwen-based)40.9
#3Hugging Face - SmolLM2-1.7B36.8
#4Luciole-1B31.3
#5Apertus-v1.1-1.5B31.2
#6Kyutai - helium-1-2b30.7
#7EuroMoE-2.6B-A0.6B-2512 (0.5B active)29.4
#8EuroLLM-1.7B27.6
#9Apertus-v1.1-0.5B24.2 +16 points (+39%) over the next European model.
European sovereign models of our class (9), from European labs: up to 2B active parameters. Overall score, Multilingual on our training languages.
#1Lara Mini Base (A0.6B/7.2B)61.5
#2IBM - granite-4.0-h-tiny (1B active)59.0
#3Google - gemma-4-E2B58.3
#4Trinity-Nano (1B active)57.9
#5LFM2.5-8B-A1B (1.7B active)57.1
#6Qwen3-1.7B57.0
#7Qwen3.5-2B56.7
#8Falcon-H1-1.5B-Deep55.2 #9Qwen2.5-1.5B54.7
#10Hunyuan-1.8B53.5
#11LFM3-1.7B53.3
#12Falcon-H1-1.5B53.3
Text = mean of English (10 benchmarks) and Multilingual (9 benchmarks, our 44 non-English training languages). Separately: English #2 (66.4), Multilingual #1 (56.7).
#1Lara Mini Base (A0.6B/7.2B)52.5
#2Falcon-H1-1.5B-Deep51.2
#3Hunyuan-1.8B50.9
#4Alibaba - Qwen3-1.7B50.6
#5IBM - granite-4.0-h-tiny (1B active)48.9
#6LFM3-1.7B45.1
#7LFM2.5-8B-A1B (1.7B active)44.4
#8Falcon-H1-1.5B44.4
#9Trinity-Nano (1B active)43.5
#10granite-4.0-1b43.1
#11Qwen3.5-2B41.1
#12Qwen2.5-1.5B40.5
⋯
#26Google - gemma-4-E2B (5.1B total)25.4
Math & Code = mean of Math (GSM8K, MATH, MGSM) and Code (MBPP, MBPP+, CRUX-I, CRUX-O). Math #1 (59.9), Code #5 (45.1).
Efficiency
#1 under 2B, with just 0.6B active
Lara Mini Base outscores all 35 other models of its class, including models that use up to 3× more active parameters, such as Qwen3-1.7B and gemma-4-E2B.
Overall score against active parameters, log scale. The step line is the frontier: no model with fewer active parameters scores higher. gemma-4-E2B is drawn at its 2B effective size (5.1B total).
Limited release · who gets it
What Lara Mini is for
Lara Translate app
Offline Translation
Lara Mini is the base model behind Lara 4 offline translation feature for the next Lara Translate app. Your text never leaves the phone, and each translation still takes under a second.
Translated customers
Tiny Base LLM
Selected Translated enterprise customers will have access. Lara Mini is designed to be fine-tuned for specialized sovereign applications, runs on mobile devices and on our cloud delivers an end-to-end latency under 20 ms delivering millions of tokens per sec.
Research
AI researchers
Access for AI researchers to reproduce our results and build on them, pushing multilingual research further.
What's next
Three competitions, one rule
We train the next stage only if we win a medal in the current one.
Stage 1
Local
Tiny · <2B active
Nov 2026Lara Mini Base
Win the tiny category with Lara Base (A0.6B/7.2B).
Dec 2026Lara Mini
Lara 4 Mini and Lara Mini LLM, for ultra-low latency and mobile devices.
Medal won → we train the National stage. No medal, we stop.
Stage 2
National
Small · <10B active
Mar 2027Lara Piccola Base (A7B/138B) Enter the small category and win a medal there too.
Apr 2027Lara 4 & Lara Piccola LLM
Lara 4 translation model and Lara Piccola LLM, an EU-sovereign A7B/138B model.
Medal won → we train the Olympics stage. No medal, we stop.
Stage 3
Olympics
Frontier · 40B+ active
Dec 2027Lara Grande Base (A60B/1.2T) Enter the frontier category, against the world's best models.
Mar 2028Lara 5 & Lara Grande LLM
Lara 5 surpasses P95 pro translators. Lara Grande: an EU-sovereign frontier LLM, ahead of Chinese models and approaching Anthropic and OpenAI.
Reference models:tiny Gemma 4 E2B · small Gemma 4B/26B · medium Qwen 17B/397B · frontier GPT-6 Astra, Claude 5.6, Kimi K3.
Training budget
Training single-run budgets, next releases
Model
Tokens
FLOPs
Hardware
Days
Training cost
Lara 4 PiccolaMoE 6.7B / 138B
20T
8.0 × 10²³
512 × H100
31
$1.0M
Lara 4 GrandeMoE 60B / 1.2T
24T
8.6 × 10²⁴
512 × Vera Rubin
25
$2.4M
Lara 4 SaggiaMoE 600B / 12T
36T
1.3 × 10²⁶
2,304 × Vera Rubin
83
$36.6M
For reference Typical frontierMoE 150B / 3T
36T
3.2 × 10²⁵
4,096 × B200
51
$18.8M
Source: Lara 4 LLM Training Budget Planning, 3 Nov 2026 · FP8 · compute cost only · Saggia days estimated with the plan's method
Next training
Lara Piccola Candidate
60 layers and 138B parameters: the model to beat Gemma 4 31B.
138Btotal parameters
6.7Bactive per token
Tokenizernew, rebalanced
60layers
2816hidden size
512 × 512experts, top-8 + 2 shared
400kH100 GPU-hours for 20T tokens
30 dayson 512 H100 GPUs
$1MTraining run cost
Goal: win the small model class (<10B active)
Current leaders: Qwen3.5-122B-A10B, GLM-4.5-Air
Good chance
Goal: beat Gemma 4 31B on multilingual
Beat highly multilingual dense models in the medium model class.
Good chance
30.7 s per step at 32 nodes · same hybrid attention as Lara Mini Base (GDN, window, global)
Technical report
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Architecture, training data, benchmarks and methodology behind Lara Mini Base, in one document. Leave your details and we'll email it to you.
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Work with Lara
Two ways to put Lara to work
Interested in the generative model?
Selected Translated enterprise customers will have access. Lara Mini is designed to be fine-tuned for specialized sovereign applications, runs on mobile devices and on our cloud delivers an end-to-end latency under 20 ms delivering millions of tokens per sec.