GPT-5.2 Codex vs GPT-4.1 Nano
Compare pricing, context windows, and strengths for GPT-5.2 Codex by OpenAI and GPT-4.1 Nano by OpenAI - and see how to put either to work in Appaca.
GPT-5.2 Codex
Highly capable coding model optimized for long-horizon, agentic coding tasks with configurable reasoning and strong codebase awareness.
View GPT-5.2 CodexGPT-4.1 Nano
Fastest and most cost-efficient GPT-4.1 model with strong instruction following, tool calling, and a 1M-token context window for lightweight, real-time tasks.
View GPT-4.1 NanoGPT-5.2 Codex vs GPT-4.1 Nano at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | GPT-5.2 Codex | GPT-4.1 Nano |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Model type | Text | Text |
| Context window | 400K tokens | 1.05M tokens |
| Input price | $1.75 / 1M tokens | $0.1 / 1M tokens |
| Output price | $14 / 1M tokens | $0.4 / 1M tokens |
| Status | Superseded by GPT-5.3 Codex | Superseded by GPT-5 Mini |
How GPT-5.2 Codex and GPT-4.1 Nano differ
What the numbers mean in practice when choosing between GPT-5.2 Codex and GPT-4.1 Nano.
-
GPT-4.1 Nano is 94% cheaper on input tokens ($0.1 vs $1.75 per million), which adds up quickly in document-heavy workloads.
-
GPT-4.1 Nano is 97% cheaper on output tokens ($0.4 vs $14 per million) - the bigger factor for tools that generate long documents.
-
GPT-4.1 Nano's 1.05M tokens context window is roughly 2.6x larger than GPT-5.2 Codex's 400K tokens, so it can work across bigger codebases, contracts, or archives in one pass.
-
GPT-5.2 Codex has been superseded by GPT-5.3 Codex - for new builds, consider the newer model first.
-
GPT-4.1 Nano has been superseded by GPT-5 Mini - for new builds, consider the newer model first.
Strengths side by side
Where each model shines, according to benchmarks and provider positioning.
GPT-5.2 Codex
1. Optimized for Long-Horizon Coding Tasks
- OpenAI describes GPT-5.2 Codex as a highly intelligent coding model built for long-horizon, agentic coding work.
- Well suited to planning, refactoring, debugging, and multi-step implementation flows inside real codebases.
2. Adjustable Reasoning for Coding Work
- Supports configurable reasoning effort from low to xhigh depending on speed and quality needs.
- Accepts both text and image inputs while producing text output.
3. Large Context + Long Output
- 400 k token context window supports broad repository understanding and larger working sets.
- Allows up to 128 k output tokens for longer patches, code generation, and technical explanations.
4. Up-to-Date Model Snapshot
- Knowledge cut-off of Aug 31 2025 keeps it current with newer tools and frameworks.
- Supports streaming, function calling, and structured outputs for tool-driven coding workflows.
GPT-4.1 Nano
1. Ultra-Fast, Low-Latency Performance
- The fastest model in the GPT-4.1 family, ideal for real-time interactions and high-throughput applications.
- Designed for scenarios where speed matters more than complex reasoning.
2. Most Cost-Efficient GPT-4.1 Variant
- Lowest price point among GPT-4.1 models.
- Enables large-scale deployments such as support bots, routing systems, and lightweight assistants without high compute costs.
3. Solid Instruction Following
- Consistent and reliable at following clear instructions.
- Well-suited for:
- Classification
- Simple reasoning
- Data extraction
- Content rewriting
- Chat-style responses
4. Strong Tool Calling Capabilities
- Built with robust support for:
- Function calling
- Structured outputs (e.g., JSON)
- Lightweight automation tasks
- Works well within multi-step agent workflows that rely on simple tools.
5. Basic Multimodal Input
- Supports text and image input.
- Useful for:
- Simple visual recognition
- Alt-text generation
- Reading graphics or screenshots
6. Text-Only Output
- Produces text only, ensuring:
- Clean structured outputs
- High reliability for downstream processing
- Ease of integration into backend systems
7. 1M-Token Context Window
- Supports up to 1,047,576 tokens, allowing:
- Long documents
- Multiple files
- Large prompt memory
- Reduces or eliminates the need for chunking and retrieval in many simple workflows.
8. Ideal Use Cases
- Customer support bots
- Routing and intent detection
- Simple agents and workflow automation
- Content cleanup and rewriting
- Basic Q&A, summaries, and extraction
9. Broad API Integration
- Available across major API endpoints:
- Chat Completions
- Responses
- Realtime
- Assistants
- Fine-tuning
- Supports predicted outputs for reliability and determinism.
Use GPT-5.2 Codex or GPT-4.1 Nano - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by GPT-5.2 Codex or GPT-4.1 Nano - connected to your real data and ready for your whole team. No code, no deployment.
Describe it, and it's built
Tell the Appaca agent the internal tool you need and it builds a working app powered by GPT-5.2 Codex or GPT-4.1 Nano. No code, no API keys, no deployment.
Switch models without rebuilding
Start on GPT-5.2 Codex, test the same tool on GPT-4.1 Nano, and keep whichever performs better - the rest of your app stays exactly as it is.
Automated for the whole team
Schedule tools to run on autopilot - daily digests, weekly reports, real-time triggers - and share them with your whole team from one workspace.
Describe it, and it's built
Tell the Appaca agent what your team needs and it builds a working app powered by GPT-5.2 Codex or GPT-4.1 Nano - connected to the tools you already use.
Related comparisons
See how GPT-5.2 Codex and GPT-4.1 Nano stack up against other models in the directory.
FAQs
GPT-4.1 Nano is generally cheaper: $0.1 input / $0.4 output per million tokens, versus $1.75 / $14 for GPT-5.2 Codex. Actual cost depends on how many tokens your workload reads and writes.
GPT-4.1 Nano has the larger context window at 1.05M tokens, compared to 400K tokens for GPT-5.2 Codex. A larger window means the model can consider more text at once - useful for long contracts, codebases, or months of records.
It depends on the job. Compare the pricing, context window, and strengths above against your workload - and remember the choice isn't permanent. In Appaca you can build a tool on GPT-5.2 Codex, test the same tool on GPT-4.1 Nano, and switch at any time without rebuilding anything.
Yes. Appaca is a no-code AI workspace: describe the internal tool your team needs and the Appaca agent builds it as a working app powered by GPT-5.2 Codex, GPT-4.1 Nano, or any other model in the directory - with a built-in database, team access, and integrations. No API keys to wire up and nothing to deploy.
Build AI tools with GPT-5.2 Codex or GPT-4.1 Nano
Describe the tool your team needs and get a working app powered by the model you choose - with a built-in database, team access, and integrations. No code, no deployment.