o4-mini vs o3
Compare pricing, context windows, and strengths for o4-mini by OpenAI and o3 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
A powerful reasoning model excelling at complex, multi-step tasks across math, science, coding, and visual reasoning; succeeded by GPT-5.
View o3o4-mini vs o3 at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | o4-mini | o3 |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Model type | Text | Text |
| Context window | 200K tokens | 200K tokens |
| Input price | $1.1 / 1M tokens | $2 / 1M tokens |
| Output price | $4.4 / 1M tokens | $8 / 1M tokens |
| Status | Current | Current |
How o4-mini and o3 differ
What the numbers mean in practice when choosing between o4-mini and o3.
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o4-mini is 45% cheaper on input tokens ($1.1 vs $2 per million), which adds up quickly in document-heavy workloads.
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o4-mini is 45% cheaper on output tokens ($4.4 vs $8 per million) - the bigger factor for tools that generate long documents.
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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
1. Advanced reasoning capability
- Designed for multi-step thinking across text, code, and visual inputs.
- Excels at math, science, logic puzzles, and complex analytical workflows.
2. Strong performance across domains
- Highly capable in technical writing, data analysis, and structured problem-solving.
- Useful for research, engineering tasks, and intricate instruction-following.
3. Visual reasoning support
- Accepts image inputs, enabling tasks such as diagram analysis, chart interpretation, and visual logic assessments.
4. High output capacity
- Up to 100,000 output tokens, supporting long-form content, technical breakdowns, and multi-part solutions.
5. Excellent instruction following
- Produces detailed, step-by-step responses for tasks requiring precision and clarity.
- Ideal for educational explanations, system design reasoning, and code walkthroughs.
6. Large 200K context window
- Handles long documents, multi-file reasoning, or extended conversations with minimal loss of context.
7. Broad API support
- Works with Chat Completions, Responses, Realtime, Assistants, Batch, Embeddings, Image Generation, and more.
- Supports streaming and function calling for advanced workflows.
8. Positioned as a legacy reasoning model
- Remains extremely capable but formally succeeded by GPT-5, which offers stronger reasoning and performance.
Use o4-mini or o3 - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by o4-mini or o3 - 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. No code, no API keys, no deployment.
Switch models without rebuilding
Start on o4-mini, test the same tool on o3, 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 - connected to the tools you already use.
Related comparisons
See how o4-mini and o3 stack up against other models in the directory.
FAQs
o4-mini is generally cheaper: $1.1 input / $4.4 output per million tokens, versus $2 / $8 for o3. Actual cost depends on how many tokens your workload reads and writes.
They are equal: both o4-mini and o3 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, 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, 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
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.