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Get started freeGPT-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
| Feature | GPT-5.4 | GPT-OSS 20B |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Model Type | text | text |
| Context Window | 1,050,000 tokens | 128,000 tokens |
| Input Cost | $2.50/ 1M tokens | $0.00/ 1M tokens |
| Output Cost | $15.00/ 1M tokens | $0.00/ 1M tokens |
Stop choosing. Use both.
With Appaca you don't have to pick — build apps that are powered by GPT-5.4, GPT-OSS 20B, for your specific use case.
Build your first app freeKelvin Htat
Business
Apps
New appStrengths & Best Use Cases
GPT-5.4
OpenAI1. 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.
Prompts to Get Started
Use these prompts to power AI products you build on Appaca. Each works great with the models above.
Best for GPT-5.4
textMarket Entry Strategy
Outline a strategy for entering a new market or geography.
Newsletter/Article Digest Summary
Summarise a collection of newsletters or articles into key takeaways.
Technical Documentation
Write clear technical documentation for a feature, tool, or system.
Best for GPT-OSS 20B
textDigital Nomad Visa Guide
Write a guide to digital nomad visa options for a destination. Practical, current, and covers eligibility and application process.
Caching Strategy Guide
Define a caching strategy for an application to improve performance.
Customer Retention Strategy (Loyalty + Expansion)
Develop a retention strategy that reinforces your USP, improves customer outcomes, and responds to evolving persona challenges.