o3 vs GPT-4o
Compare pricing, context windows, and strengths for o3 by OpenAI and GPT-4o by OpenAI - and see how to put either to work in Appaca.
o3
A powerful reasoning model excelling at complex, multi-step tasks across math, science, coding, and visual reasoning; succeeded by GPT-5.
View o3GPT-4o
A versatile, high-intelligence flagship GPT model that handles text and image inputs and produces fast, high-quality text outputs for a wide range of tasks.
View GPT-4oo3 vs GPT-4o at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | o3 | GPT-4o |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Model type | Text | Text |
| Context window | 200K tokens | 128K tokens |
| Input price | $2 / 1M tokens | $2.5 / 1M tokens |
| Output price | $8 / 1M tokens | $10 / 1M tokens |
| Status | Current | Current |
How o3 and GPT-4o differ
What the numbers mean in practice when choosing between o3 and GPT-4o.
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o3 is 20% cheaper on input tokens ($2 vs $2.5 per million), which adds up quickly in document-heavy workloads.
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o3 is 20% cheaper on output tokens ($8 vs $10 per million) - the bigger factor for tools that generate long documents.
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o3's 200K tokens context window is roughly 1.6x larger than GPT-4o's 128K tokens, so it can work across bigger codebases, contracts, or archives in one pass.
Strengths side by side
Where each model shines, according to benchmarks and provider positioning.
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.
GPT-4o
1. High-intelligence, general-purpose model
- Strong reasoning, creativity, summarization, and problem-solving.
- Great balance of speed, accuracy, and cost.
2. Multimodal input support
- Accepts text + image inputs for visual reasoning, extraction, or description.
- Output is text only, making it predictable for production.
3. Excellent for structured and unstructured tasks
- Performs well on Q&A, writing, analysis, classification, chat, and planning.
- Supports Structured Outputs, making it suitable for deterministic workflows.
4. Strong tool-use capabilities
- Supports function calling, API orchestration, and tool-augmented workflows.
- Integrates well with assistants, batch operations, and automation pipelines.
5. Large context for complex tasks
- 128K context allows multi-document reasoning, multi-step conversations, and large input payloads.
6. Production-ready reliability
- Stable outputs, predictable behaviors, and broad modality coverage.
- Supported across all major API endpoints.
7. Lower latency than o-series reasoning models
- Faster responses due to no dedicated reasoning step.
- Ideal for interactive or near-real-time applications.
8. Fine-tuning and distillation supported
- Enables specialization for domain-specific tasks.
- Distillation helps create smaller, efficient custom models.
Use o3 or GPT-4o - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by o3 or GPT-4o - 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 o3 or GPT-4o. No code, no API keys, no deployment.
Switch models without rebuilding
Start on o3, test the same tool on GPT-4o, 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 o3 or GPT-4o - connected to the tools you already use.
Related comparisons
See how o3 and GPT-4o stack up against other models in the directory.
FAQs
o3 is generally cheaper: $2 input / $8 output per million tokens, versus $2.5 / $10 for GPT-4o. Actual cost depends on how many tokens your workload reads and writes.
o3 has the larger context window at 200K tokens, compared to 128K tokens for GPT-4o. 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 o3, test the same tool on GPT-4o, 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 o3, GPT-4o, 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 o3 or GPT-4o
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.