GPT-5.2 Codex vs o4-mini
Compare pricing, context windows, and strengths for GPT-5.2 Codex by OpenAI and o4-mini 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 Codexo4-mini
A fast, cost-efficient small reasoning model optimized for coding and visual tasks; succeeded by GPT-5 mini.
View o4-miniGPT-5.2 Codex vs o4-mini at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | GPT-5.2 Codex | o4-mini |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Model type | Text | Text |
| Context window | 400K tokens | 200K tokens |
| Input price | $1.75 / 1M tokens | $1.1 / 1M tokens |
| Output price | $14 / 1M tokens | $4.4 / 1M tokens |
| Status | Superseded by GPT-5.3 Codex | Current |
How GPT-5.2 Codex and o4-mini differ
What the numbers mean in practice when choosing between GPT-5.2 Codex and o4-mini.
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o4-mini is 37% cheaper on input tokens ($1.1 vs $1.75 per million), which adds up quickly in document-heavy workloads.
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o4-mini is 69% cheaper on output tokens ($4.4 vs $14 per million) - the bigger factor for tools that generate long documents.
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GPT-5.2 Codex's 400K tokens context window is roughly 2x larger than o4-mini's 200K tokens, so it can work across bigger codebases, contracts, or archives in one pass.
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GPT-5.2 Codex has been superseded by GPT-5.3 Codex - for new builds, consider the newer model first.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.
o4-mini
1. Fast and efficient reasoning
- Provides strong reasoning capabilities with significantly lower latency and cost compared to larger o-series models.
- Ideal for lightweight reasoning tasks, logic steps, and quick multi-step thinking.
2. Optimized for coding tasks
- Performs exceptionally well in code generation, debugging, and explanation.
- Useful for IDE integrations, coding assistants, and developer tools with tight latency budgets.
3. Strong visual reasoning
- Accepts image inputs for tasks such as diagram interpretation, charts, UI analysis, and visual logic.
- Great for hybrid text-image reasoning flows.
4. Large 200K-token context window
- Capable of processing long documents, multi-file codebases, or extended analysis.
- Reduces need for chunking or external retrieval pipelines.
5. High 100K-token output limit
- Supports lengthy reasoning sequences, full codebase explanations, or multi-section documents.
6. Broad API compatibility
- Available in Chat Completions, Responses, Realtime, Assistants, Batch, Embeddings, and Image workflows.
- Supports streaming, function calling, structured outputs, and fine-tuning.
7. Cost-efficient for production
- Lower input/output pricing makes it suitable for large-scale deployments, SaaS products, and recurring tasks.
8. Succeeded by GPT-5 mini
- GPT-5 mini offers improved speed, reasoning power, and pricing, but o4-mini remains a strong option for cost-sensitive workloads.
Use GPT-5.2 Codex or o4-mini - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by GPT-5.2 Codex or o4-mini - 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 o4-mini. No code, no API keys, no deployment.
Switch models without rebuilding
Start on GPT-5.2 Codex, test the same tool on o4-mini, 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 o4-mini - connected to the tools you already use.
Related comparisons
See how GPT-5.2 Codex and o4-mini stack up against other models in the directory.
FAQs
o4-mini is generally cheaper: $1.1 input / $4.4 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-5.2 Codex has the larger context window at 400K tokens, compared to 200K tokens for o4-mini. 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 o4-mini, 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, o4-mini, 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 o4-mini
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.