o4-mini vs o3-mini
Compare pricing, context windows, and strengths for o4-mini by OpenAI and o3-mini by OpenAI - and see how to put either to work in Appaca.
o4-mini
A fast, cost-efficient small reasoning model optimized for coding and visual tasks; succeeded by GPT-5 mini.
View o4-minio3-mini
A small, cost-efficient reasoning model offering high intelligence at the same pricing and latency targets as o1-mini, with strong support for structured outputs and developer tooling.
View o3-minio4-mini vs o3-mini at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | o4-mini | o3-mini |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Model type | Text | Text |
| Context window | 200K tokens | 200K tokens |
| Input price | $1.1 / 1M tokens | $1.1 / 1M tokens |
| Output price | $4.4 / 1M tokens | $4.4 / 1M tokens |
| Status | Current | Current |
How o4-mini and o3-mini differ
What the numbers mean in practice when choosing between o4-mini and o3-mini.
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Both models cost the same on input: $1.1 per million tokens.
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Both models offer the same 200K tokens context window.
Strengths side by side
Where each model shines, according to benchmarks and provider positioning.
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.
o3-mini
1. High-intelligence small reasoning model
- Delivers strong reasoning performance in a compact footprint.
- Ideal for tasks that need intelligence but must stay cost-efficient.
2. Excellent for developer workflows
- Supports Structured Outputs, function calling, and Batch API.
- Reliable for backend automation, agents, and data-processing pipelines.
3. Strong text reasoning capabilities
- Handles multi-step logic, natural language analysis, SQL translation, entity extraction, and content generation.
- Works well for landing pages, policy summaries, and knowledge extraction (as shown in built-in examples).
4. 200K context window
- Allows large documents, multi-step analysis, and long-running conversations.
- Reduces the need for aggressive chunking or external retrieval systems.
5. High 100K-token output limit
- Enables long explanations, multi-section documents, or detailed reasoning sequences.
6. Pure text-focused model
- Input/output is text-only (no image or audio support).
- Optimized for language-heavy reasoning and logic tasks.
7. Broad API compatibility
- Works across Chat Completions, Responses, Realtime, Assistants, Embeddings, Image APIs (as tools), and more.
- Supports streaming, function calling, and structured outputs.
8. Cost-efficient for production at scale
- Same cost/performance profile as o1-mini but with higher intelligence.
Use o4-mini or o3-mini - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by o4-mini or o3-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 o4-mini or o3-mini. No code, no API keys, no deployment.
Switch models without rebuilding
Start on o4-mini, test the same tool on o3-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 o4-mini or o3-mini - connected to the tools you already use.
Related comparisons
See how o4-mini and o3-mini stack up against other models in the directory.
FAQs
They cost the same overall: both charge $1.1 per million input tokens and $4.4 per million output tokens.
They are equal: both o4-mini and o3-mini support a 200K tokens context window.
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 o4-mini, test the same tool on o3-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 o4-mini, o3-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 o4-mini or o3-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.