Cohere has released North Small Translate, an open-weight machine translation model from Cohere and Cohere Labs. It is a sparse Mixture-of-Experts (MoE) model with 218B total and 25B active parameters. It covers 50 languages, from Albanian to Vietnamese. On Cohere’s WMT26 evaluation, it scores 83.6 averaged across all languages. Cohere says that beats DeepL and Google Translate, plus open options like GLM 5.2 and Mistral Large 3.
Is it deployable? Yes. Call it free on Cohere’s API until rate limits, self-host it non-commercially, or license it commercially.
Back to Where the Transformer Started
Google researchers introduced the Transformer in 2017 with Attention Is All You Need. Its main results came from WMT 2014 English-to-German and English-to-French translation. 9 years later, Cohere is returning to that original problem with a dedicated model. Cohere’s launch post on X frames translation as a sovereignty issue. Organizations that cannot communicate globally cannot stay sovereign.
North Small Translate is the first translation model in Cohere’s North family. It follows Tiny Aya and Command A Translate in Cohere’s multilingual lineage. Cohere built it with RWS, whose Language Weaver scientists and language experts shaped its real-world quality.
Architecture
The model structure describes a decoder-only sparse MoE Transformer. Here are the key details:
- Experts: 128 experts, 8 activated per token, plus shared experts applied to every token.
- Router: A sigmoid over expert logits, normalized over the selected top-k.
- Attention: Sliding-window layers (window 4096, RoPE) and global layers without positional embeddings, interleaved 3:1.
- Lineage: That attention layout was first introduced in Command A.
- Context: 16K input and 16K output tokens, text only.
- Training: Post-trained specifically for translation quality.
About 11.5% of the weights are active per token. Per-token compute tracks the 25B active parameters. Memory still has to hold all 218B.
‘;}); $(‘nst-weights’).innerHTML=h; requestAnimationFrame(function(){requestAnimationFrame(function(){ $(‘nst-weights’).querySelectorAll(‘i’).forEach(function(x){x.style.width=x.dataset.w+’%’;}); });}); } route(3); /* 2 attention */ var stack=$(‘nst-stack’),layerBtns=[],sel=0; for(var L=0;LGlobal layer ‘+(sel+1)+’ attends to all ‘+fmt(n)+’ earlier tokens. It has no positional embeddings.’ :’Sliding-window layer ‘+(sel+1)+’ attends to ‘+fmt(n)+’ tokens, from ‘+fmt(start)+’ to ‘+fmt(pos)+’. It uses RoPE.’; } $(‘nst-pos’).addEventListener(‘input’,drawAttn); sel=3;drawAttn(); /* 3 wmt */ var wmt=[[‘North Small Translate (Agentic)’,84.36,’agentic’,1],[‘North Small Translate’,83.60,’north’],[‘Qwen 3.5 397B A17B’,81.56],[‘DeepL NextGen’,81.37],[‘Gemma 4 31B (on)’,79.46],[‘GLM 5.2 FP8’,76.50],[‘Google Translate’,68.20]]; function barsHTML(rows){ return rows.map(function(r){return ‘
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‘;}).join(”); } function animate(box){ var f=box.querySelectorAll(‘.fill’);f.forEach(function(x){x.style.width=”0″;}); requestAnimationFrame(function(){requestAnimationFrame(function(){f.forEach(function(x){x.style.width=x.dataset.v+’%’;});});}); } $(‘nst-wmt’).innerHTML=barsHTML(wmt); function drawWMT(anim){ var on=$(‘nst-ag’).getAttribute(‘aria-checked’)===’true’; $(‘nst-wmt’).querySelector(‘.ag’).classList.toggle(‘hide’,!on); if(anim)animate($(‘nst-wmt’));setTimeout(send,50); } $(‘nst-ag’).onclick=function(){this.setAttribute(‘aria-checked’,this.getAttribute(‘aria-checked’)===’true’?’false’:’true’);drawWMT(true);}; drawWMT(false); /* 4 regions */ var regs={ ‘EU languages’:[82.74,82.17,72.73], ‘Non-EU Europe’:[81.52,81.24,75.90], ‘South Asia’:[87.13,86.16,88.04] }; var regNote={ ‘EU languages’:’North leads Gemma by about 9.4 points here with the standard model.’, ‘Non-EU Europe’:’North leads Gemma by about 5.3 points here with the standard model.’, ‘South Asia’:’Gemma is slightly ahead. The blog summary also cites 86.7 for Gemma here, which conflicts with 88.04.’ }; var regWrap=$(‘nst-regions’),regBtns=[],curReg=’EU languages’; Object.keys(regs).forEach(function(k){ var b=document.createElement(‘button’);b.className=”chip”;b.textContent=k; b.onclick=function(){curReg=k;drawReg(true);};regWrap.appendChild(b);regBtns.push(b); }); function drawReg(anim){ regBtns.forEach(function(b){b.setAttribute(‘aria-pressed’,b.textContent===curReg?’true’:’false’);}); var v=regs[curReg]; $(‘nst-reg’).innerHTML=barsHTML([[‘North (Agentic)’,v[0],’agentic’],[‘North Small Translate’,v[1],’north’],[‘Gemma 4 31B (on)’,v[2]]]); $(‘nst-regnote’).innerHTML=regNote[curReg]+’ Against DeepL NextGen, Cohere reports North ahead in MENA, South Asia, Southeast Asia and East Asia, by roughly 8 to 10 points in South Asia and MENA.’; animate($(‘nst-reg’));setTimeout(send,50); } drawReg(false); /* 5 speed */ var tps={low:[112,81],high:[39,30]},mode=”low”,raf=null; R.querySelectorAll(‘[data-c]’).forEach(function(b){b.onclick=function(){ mode=b.dataset.c;R.querySelectorAll(‘[data-c]’).forEach(function(x){x.setAttribute(‘aria-pressed’,x===b?’true’:’false’);});resetRace();};}); function resetRace(){ if(raf)cancelAnimationFrame(raf); $(‘nst-r1’).style.width=”0″;$(‘nst-r2’).style.width=”0″; $(‘nst-t1′).textContent=tps[mode][0]+’ tok/s, ‘+(1000/tps[mode][0]).toFixed(1)+’ s’; $(‘nst-t2′).textContent=tps[mode][1]+’ tok/s, ‘+(1000/tps[mode][1]).toFixed(1)+’ s’; } $(‘nst-race’).onclick=function(){ resetRace(); var a=tps[mode][0],b=tps[mode][1],speed=mode===’low’?1.6:5,t0=null; function step(ts){ if(!t0)t0=ts;var s=(ts-t0)/1000*speed; $(‘nst-r1′).style.width=Math.min(100,a*s/10)+’%’; $(‘nst-r2′).style.width=Math.min(100,b*s/10)+’%’; if(b*s‘+c[0]+’ $’+c[1]+’ per task$’+m.toLocaleString(‘en-US’,{minimumFractionDigits:2,maximumFractionDigits:2})+’
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