Big LLM architecture comparison: what matters for agent teams
MoE, MLA, GQA, sliding-window attention — how 2025–2026 model architectures affect cost, latency, and which model to assign to each Neuro OS role.
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Why architecture matters for agents
When you run a Neuro OS org chart, you are not picking one model for one chat. You assign models to roles: reasoning-heavy planning, fast drafting, code, research, cheap heartbeats. Architecture choices — not benchmark hype — drive inference cost, KV cache memory, and latency per role.
This guide summarizes the major 2025–2026 open-weight trends (DeepSeek V3/R1, Gemma 3, Llama 4, Qwen3, OLMo 2, Mistral, Kimi K2) in plain language for operators choosing models per role.
Core building blocks (still GPT-shaped)
Most production LLMs still stack: token embedding → transformer blocks (attention + feedforward) → output head. Innovation is in how attention scales and how many parameters activate per token.
Multi-Head Attention (MHA) → GQA → MLA
- MHA: full key/value per head — best quality, heaviest KV cache.
- GQA (Grouped-Query Attention): shared KV heads — less memory at inference; used in Llama 3 class models.
- MLA (Multi-Head Latent Attention): compresses KV projections (DeepSeek V3) — strong modeling with smaller cache; more complex to implement.
Agent implication: roles that hold long context (research, code review) benefit disproportionately from GQA/MLA efficiency.
Mixture of Experts (MoE)
Total parameters can be huge (e.g. 671B) while active parameters per token stay smaller (e.g. ~37B). Routing picks a few experts per token.
Agent implication: great for high-capacity roles when you pay per token; watch routing stability and provider support on your stack.
Normalization and stability
Models experiment with pre-norm vs post-norm, QK-norm, and placement tweaks — affects training stability more than day-to-day agent config, but explains why two 70B-class models feel different at the same temperature.
Long context tricks
Sliding-window attention (Gemma 3) and hybrid local/global patterns reduce memory for long threads — relevant when entire support history or repo context sits in one role window.
Side-by-side: what to optimize per role
| Role type | Optimize for | Architecture bias |
|---|---|---|
| Coordinator / planning | reasoning, long context | frontier dense or strong MoE; MLA/GQA if context is huge |
| Copy / support drafts | speed, cost | mid-size dense, low latency |
| Code | tool use, accuracy | models strong on SWE-bench class tasks; often separate from marketing model |
| Heartbeats / triage | cost at volume | smallest model that meets quality bar |
| Research synthesis | context + citations | long-context models; watch cache cost |
Practical picks on Neuro OS
- Do not use one model everywhere. Match model to role job description and heartbeat volume.
- Separate “think” from “ship.” Planning role can be slower and smarter; publisher role should be fast.
- Re-evaluate quarterly. Architecture releases (DeepSeek, Qwen, Llama) shift cost/quality faster than agent frameworks.
- Measure per role: cost per completed workflow, not cost per message.
Trends worth watching
- MoE at scale — capacity without linear inference cost.
- MLA-style cache compression — longer shared threads without proportional RAM.
- Reasoning models (R1-class) — higher quality for planning; overkill for microcopy.
- Open weights — self-hosted Neuro OS deployments can pin models in git-backed config per role.
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