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Architecture Comparison

Trade-offsSelection

Trade-offs

Comparing encoder-only, decoder-only, and encoder-decoder architectures for different use cases.

Overview

Transformer architectures differ in how they process and generate text. The choice between encoder-only, decoder-only, and encoder-decoder models involves trade-offs in capability, efficiency, and task suitability.

Architectural comparison

  • Encoder-only: Bidirectional attention, no generation - best for understanding tasks
  • Decoder-only: Causal attention, autoregressive generation - best for text generation
  • Encoder-decoder: Full encoder + causal decoder - best for seq2seq tasks

When to use each

  • Classification: Encoder-only models
  • Text generation: Decoder-only models
  • Translation: Encoder-decoder models
  • Summarization: Encoder-decoder models
  • Embeddings: Encoder-only models
  • Chat/completion: Decoder-only models

Efficiency considerations

  • Encoder-only: Fast encoding, encoding only at inference
  • Decoder-only: Variable speed, cache growth during generation
  • Encoder-decoder: Encoder + decode time, full encoder benefit

Parameter considerations

  • Encoder-only: Moderate parameters
  • Decoder-only: Scales well with size
  • Encoder-decoder: Higher total parameters
Code View

Architecture Comparison Implementation

// Architecture Comparison recipe using OpenAI
// Install: bun add openai

import OpenAI from "openai";

async function main() {
  const input = "Add your prompt here.";
  const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
  const system = "You are a senior AI engineer and technical writer. Explain how the architecture applies to the request and outline practical implementation guidance. Recipe: Architecture Comparison. Description: Comparing encoder-only, decoder-only, and encoder-decoder architectures for different use cases. Focus: Trade-offs Provide actionable, implementation-ready guidance.";
  const user = `Request: ${input}`;

  const openaiResponse = await openai.chat.completions.create({
    model: "gpt-4o-mini",
    messages: [
      { role: "system", content: system },
      { role: "user", content: user },
    ],
  });

  const openaiText = openaiResponse.choices[0]?.message?.content?.trim() ?? "";

  console.log(openaiText);
}

main().catch((error) => {
  console.error(error);
  process.exitCode = 1;
});