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LLM ComparisonGPT-5.4GPT-OSS 20B

GPT-5.4 vs GPT-OSS 20B

Compare GPT-5.4 and GPT-OSS 20B. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.4GPT-OSS 20B
ProviderOpenAIOpenAI
Model Typetexttext
Context Window1,050,000 tokens128,000 tokens
Input Cost
$2.50/ 1M tokens
$0.00/ 1M tokens
Output Cost
$15.00/ 1M tokens
$0.00/ 1M tokens

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Strengths & Best Use Cases

GPT-5.4

OpenAI

1. Best Intelligence at Scale

  • OpenAI positions GPT-5.4 as its frontier model for agentic, coding, and professional workflows.
  • Built for complex professional work where stronger reasoning and higher answer quality matter.

2. Configurable Reasoning + Multimodal Input

  • Supports configurable reasoning effort from none to xhigh, letting teams balance speed and depth.
  • Accepts both text and image inputs while producing text output.

3. Massive Context for Long-Running Work

  • 1.05M token context window supports very large codebases, documents, and multi-step workflows.
  • Allows up to 128 k output tokens for long-form answers and larger generations.

4. Updated Knowledge & Broad Tool Support

  • Knowledge cut-off of Aug 31 2025 keeps it current for newer frameworks and business context.
  • Supports tools like web search, file search, code interpreter, hosted shell, computer use, and MCP in the Responses API.

GPT-OSS 20B

OpenAI
  • Open-weight / Apache 2.0 licensed: you can use, modify, and deploy freely (commercially & academically) under permissive terms.
  • Large model size (≈ 21B parameters) with Mixture-of-Experts (MoE) architecture: only ~3.6B parameters active per token, yielding efficient inference.
  • Very long context window support: up to ~128 K tokens (or ~131 K tokens per some sources) enabling in-depth reasoning, long documents, or multi-turn context.
  • Adjustable reasoning effort: you can trade latency vs quality by tuning “reasoning effort” levels.
  • Efficient hardware requirements (for its class): designed to run on a single 16 GB-class GPU or optimized local deployments for lower latency applications.
  • Strong for tasks such as reasoning, tool-use, structured output, chain-of-thought debugging: because the model is open and you can inspect its chain of thought.
  • Flexibility: since weights are available, you can self-host, fine-tune, or deploy offline, giving more control than closed API models.