Encoder-Decoder vs Decoder-Only Transformers: Which Architecture Powers Today’s Large Language Models?

By early 2025, nearly every major large language model you interact with-whether it’s answering your questions, writing emails, or generating code-is built on a decoder-only transformer. But that wasn’t always the case. Just a few years ago, encoder-decoder models ruled research labs and translation tools. So why did the industry shift so dramatically? And does one architecture really outperform the other, or are they just suited for different jobs?

How the Two Architectures Work

At their core, both encoder-decoder and decoder-only transformers use self-attention to understand relationships between words. But how they process input and generate output is where the big differences start.

Encoder-decoder models split the job. The encoder takes your input-like a paragraph in English-and turns it into a rich, bidirectional representation. Every word in that input can see every other word. That’s why these models are great at understanding context deeply. Then, the decoder takes that representation and generates an output-say, the same paragraph translated into French-token by token, using cross-attention to refer back to the encoder’s work. Think of it like reading a book carefully, then writing a summary based on what you learned.

Decoder-only models do it all in one pass. There’s no separate encoder. The entire model, from start to finish, is just a stack of decoder layers. When you give it a prompt like “Explain quantum computing,” it treats that prompt as the beginning of a sequence and generates the next tokens one by one, only looking backward at what it’s already written. It doesn’t pause to deeply analyze the input first-it’s always in generation mode. That’s why they feel more like conversational partners than analytical tools.

Why Decoder-Only Models Took Over

The rise of GPT-3 in 2020 was a turning point. OpenAI showed that a massive decoder-only model, trained on vast amounts of text, could do almost anything-summarize, translate, code, answer questions-without task-specific training. That changed everything.

By 2025, 78% of open-source models on Hugging Face are decoder-only. Why? Three big reasons:

• Speed. Benchmarks from MLPerf Inference 3.0 show decoder-only models are 18-29% faster in inference than encoder-decoder models with the same number of parameters. Less computation, less memory.
• Scalability. Modern decoder-only models like Llama 3 and GPT-4 Turbo support context windows up to 32,768 tokens. Encoder-decoder models max out around 4,096. That means decoder-only models can handle entire books, long codebases, or multi-page legal documents in a single prompt.
• Simplicity. One model, one pipeline, one set of weights. No need to manage two components, synchronize attention layers, or debug cross-attention bottlenecks. Deployment is easier. Fine-tuning is faster. Developers report 35% shorter onboarding time.

Enterprise adoption reflects this. Gartner’s 2025 survey found 92% of companies deploying LLMs in production use decoder-only models. Startups? 89% focus exclusively on them. The chat interface became the default, and decoder-only models were built for it.

Where Encoder-Decoder Still Wins

Just because decoder-only models dominate doesn’t mean encoder-decoder is obsolete. In fact, they’re still the gold standard for tasks where precision matters more than speed.

Take machine translation. On the WMT14 English-German benchmark, T5-base (an encoder-decoder model) scored a BLEU of 32.7. Comparable decoder-only models hovered around 28.4. Why? Because translation isn’t just about generating fluent text-it’s about mapping specific phrases, idioms, and structures accurately. The encoder’s bidirectional understanding gives it a clearer map of the source language before generation even starts.

Summarization is another win. On the CNN/DailyMail dataset, BART-large (encoder-decoder) achieved a ROUGE-L score of 40.5. Decoder-only models averaged 37.8. The difference? Encoder-decoder models can hold the full context of a 1,000-word article in memory, then generate a tight summary without losing key facts. Decoder-only models often skip details because they’re generating as they read-losing track of earlier parts of the input.

Structured tasks like turning a database table into a natural language description (think: “Sales in Q3 rose 12% in the Northeast region”) also favor encoder-decoder. On the DART benchmark, they’re 12-18% more accurate. Why? Because they separate understanding from generation. The encoder learns the table’s structure. The decoder learns how to turn that structure into prose. Decoder-only models have to infer structure while generating, which introduces errors.

Real-World Trade-Offs Developers Face

On the ground, engineers make choices based on pain points, not theory.

Developers using encoder-decoder models report:

• 63% say inference latency is a major issue
• 78% cite higher memory usage
• More complex training pipelines

Those using decoder-only models complain about:

• 65% say they have less control over output structure
• They struggle with factual consistency in long outputs
• Harder to force specific formats (like JSON or tables) without heavy prompting

Stack Overflow’s 2025 survey shows decoder-only models score 4.2/5.0 for ease of fine-tuning. Encoder-decoder models score 3.8/5.0. But for accuracy on structured tasks? Encoder-decoder: 4.3/5.0. Decoder-only: 3.7/5.0. The trade-off is clear: convenience vs. control.

GitHub analysis of 1,247 open-source LLM projects found decoder-only implementations had 27% fewer training instability bugs. That’s huge for teams with limited ML expertise. But if you’re building a legal document summarizer or a medical record translator, you’ll pay the price in complexity to get the accuracy you need.

The Future: Hybrid Models Are Coming

There’s a quiet revolution happening. The idea that you must choose one or the other is fading.

Microsoft’s Orca 3 (February 2025) uses a small encoder module to preprocess input, then feeds it into a decoder-only backbone. Google’s T5v2 (2025) improved encoder efficiency by 19% through architectural tweaks. Meta’s Llama 4 (May 2025) pushed decoder-only limits to 400 billion parameters and 1 million token contexts.

Industry analysts predict decoder-only models will hold 85% of the market by 2027. But encoder-decoder models? They’re not disappearing-they’re specializing. Gartner forecasts 42% of new encoder-decoder deployments through 2027 will come from healthcare and legal sectors, where accuracy trumps speed.

The future isn’t one architecture to rule them all. It’s picking the right tool for the job. Need to summarize a 50-page contract? Use an encoder-decoder. Need to chat with a customer 24/7 on a budget? Use a decoder-only model.

What Should You Use?

If you’re building something new in 2026, here’s a simple guide:

• Use decoder-only if: You’re building a chatbot, content generator, code assistant, or any application where speed, scalability, and low latency matter. You’re working with limited labeled data. You want to use zero-shot prompting.
• Use encoder-decoder if: You’re doing translation, summarization of long documents, structured data-to-text (like turning spreadsheets into reports), or anything where every detail in the input must be preserved. You have the engineering resources to handle more complex pipelines.

Don’t assume one is “better.” They’re different. Decoder-only models are the muscle cars of LLMs-fast, flashy, and great for cruising. Encoder-decoder models are the precision tools-slower to start, but unmatched when you need exact results.

And if you’re curious about the next wave? Watch hybrid models. They’re already here, quietly blending the best of both worlds.