---
type: "post"
id: "from-full-to-hybrid-attention-in-llms-795700"
slug: "from-full-to-hybrid-attention-in-llms-795700"
title: "From Full to Hybrid Attention in LLMs"
description: "Sebastian Raschka’s November 10, 2026 analysis says the LLMs he tracks have shifted toward hybrid attention: in the second half of 2026, models split evenly between those retaining some full attention and those using none."
retrieval_nugget: "Sebastian Raschka’s November 10, 2026 analysis says the LLMs he tracks have shifted toward hybrid attention: in the second half of 2026, models split evenly between those retaining some full attention and those using none. For builders, the trade-off is potentially cheaper long-context use against harder exact retrieval in architectures that compress history."
published_at: "2026-11-10"
updated_at: "2026-11-10"
record_date: "2026-11-10"
date_kind: "published_at"
topics: ["models"]
entities: ["Sebastian Raschka","Gated DeltaNet","Mamba-2","Lightning Attention"]
related_patterns: ["harness-architecture-outlives-model-choice"]
novelty_label: "new_fact"
what_changed: "A new architecture survey finds that second-half 2026 LLMs split evenly between hybrids retaining some full attention and models with none, extending the shift toward cheaper long-context use."
prior_refs: []
source_url: "https://sebastianraschka.com/blog/2026/full-to-hybrid-attention.html"
source_urls: ["https://sebastianraschka.com/blog/2026/full-to-hybrid-attention.html"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:from-full-to-hybrid-attention-in-llms-795700"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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markdown_url: "https://newruntime.com/posts/from-full-to-hybrid-attention-in-llms-795700.md"
---

# From Full to Hybrid Attention in LLMs

## Retrieval answer

Sebastian Raschka’s November 10, 2026 analysis says the LLMs he tracks have shifted toward hybrid attention: in the second half of 2026, models split evenly between those retaining some full attention and those using none. For builders, the trade-off is potentially cheaper long-context use against harder exact retrieval in architectures that compress history.

## Summary
Sebastian Raschka’s November 10, 2026 analysis says the LLMs he tracks have shifted toward hybrid attention: in the second half of 2026, models split evenly between those retaining some full attention and those using none. For builders, the trade-off is potentially cheaper long-context use against harder exact retrieval in architectures that compress history.

## What happened
Sebastian Raschka writes that the transition from full to hybrid attention has continued across the models he tracks. In 2025, many models still used full attention throughout, while some introduced hybrids with selected full-attention layers. That movement continued in the first half of 2026. In the second half, he reports an even split between models with “some full attention” and models with none. He says there are no longer models with full attention in every layer in the set represented by his chart.

Raschka defines full attention throughout as global multi-head attention (MHA), grouped-query attention (GQA), dense multi-head latent attention (MLA), or CCA in every layer. “Some full attention” means mixing full global attention with alternatives such as sliding-window or chunked attention, Gated DeltaNet, KDA, Mamba-2, Lightning Attention, convolutions, or sparse attention. “No full attention” means zero dense global-attention layers. These categories describe layer composition, not a single replacement architecture.

He identifies two efficiency gains. Sparse attention and related methods can select tokens or blocks and perform the expensive attention operation only on that subset. Raschka notes that such methods can still retrieve information from distant positions. Separately, some architectures use less memory to retain context: recurrent layers such as Gated DeltaNet maintain a fixed-size state. That reduces memory use, but compressing the history creates challenges for exact retrieval. Raschka says these recurrent layers are therefore usually interleaved with traditional attention methods, whether sparse or full.

The practical implication he draws is that these architectures are becoming more suitable for long documents and conversations, large codebases, and agent use cases involving many tool calls. The page frames the chart as a trend observed in the models Raschka tracks; it does not name a complete model set or provide the individual counts behind the proportions in the excerpted chart description. The page also identifies itself as a lightly edited website version of a Substack note.

## What the archive adds
New Runtime’s earlier coverage includes an interactive explanation of how LLMs work, published April 27, 2026, and a collection of transformer and LLM concepts published August 24, 2025. Those notes provide background, while Raschka’s analysis adds a current architectural trend. This is a new fact on a familiar topic, not a restatement. (The earlier coverage is not linked here because no New Runtime URL for those items is listed.)

## Trend signal
This supports, with a narrow architectural signal, [harness architecture outlives model choice](https://newruntime.com/patterns/harness-architecture-outlives-model-choice/): long-context economics may affect which models suit agent workflows, but the post does not assess harnesses or show that they are more durable than model choice. Treat the connection as contextual rather than direct evidence for the pattern.

## Why now
Raschka dates the comparison to the second half of 2026 and presents it as a continuation of changes visible in 2025 and the first half of 2026. The timing matters because he links the architectural shift to long documents, large codebases, and agent workflows with many tool calls.

## Why it matters
For teams, a larger advertised context window is not the whole story. Attention design can change the compute and memory cost of using that context, while recurrent fixed-state layers introduce a different risk: compressing prior information can make exact retrieval harder. A model that is efficient at carrying on a long conversation may not be equally reliable at retrieving a specific clause, code detail, or earlier tool result.

The post does not offer comparative benchmarks or name deployments, so builders should treat the trend as a reason to test rather than a claim that hybrid models are universally better. Evaluate the actual task: measure the cost of long-context runs and test whether the model can recover exact information from distant parts of documents or code. Agent workflows with many tool calls are a plausible beneficiary, but their success still depends on what information survives the model’s attention and memory mechanisms.

## Limits and uncertainty
Raschka describes the proportions in the models he tracks, but the page excerpt does not enumerate the full model set or counts behind the chart. The reported split should not be read as a census of all LLM releases. The post does not give controlled comparisons of latency, memory use, cost, or retrieval accuracy, and it does not establish that the architectural categories predict performance on particular workloads. The benefits and retrieval trade-offs remain dependent on implementation and task; they are not independently measured in this analysis.

## Skill to build now
Take one long document or codebase task your team already runs. Compare a current model against a candidate with hybrid or non-full attention, using the same prompt and context. Measure both cost and whether it can retrieve exact facts placed far apart in the input. This tests the trade-off Raschka identifies without assuming that a context-length claim guarantees useful recall.

## Reader takeaway
Hybrid attention is becoming more common in the models Raschka tracks, with potential savings in computation or memory for long contexts. The counterweight is exact retrieval, especially when architectures compress history. For builders, the useful next step is workload-specific testing: measure cost and retrieval together, rather than choosing on context-window size alone.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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