GPT-5.5 vs o4-mini
Compare pricing, context windows, and strengths for GPT-5.5 by OpenAI and o4-mini by OpenAI - and see how to put either to work in Appaca.
GPT-5.5
OpenAI's smartest and most capable model yet for agentic coding, knowledge work, and computer use, delivering a new class of intelligence at GPT-5.4 latency.
View GPT-5.5o4-mini
A fast, cost-efficient small reasoning model optimized for coding and visual tasks; succeeded by GPT-5 mini.
View o4-miniGPT-5.5 vs o4-mini at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | GPT-5.5 | o4-mini |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Model type | Text | Text |
| Context window | 1M tokens | 200K tokens |
| Input price | $5 / 1M tokens | $1.1 / 1M tokens |
| Output price | $30 / 1M tokens | $4.4 / 1M tokens |
| Status | Current | Current |
How GPT-5.5 and o4-mini differ
What the numbers mean in practice when choosing between GPT-5.5 and o4-mini.
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o4-mini is 78% cheaper on input tokens ($1.1 vs $5 per million), which adds up quickly in document-heavy workloads.
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o4-mini is 85% cheaper on output tokens ($4.4 vs $30 per million) - the bigger factor for tools that generate long documents.
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GPT-5.5's 1M tokens context window is roughly 5x larger than o4-mini's 200K 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.
GPT-5.5
1. Strongest Agentic Coding Model
- State-of-the-art on Terminal-Bench 2.0 (82.7%), Expert-SWE (73.1%), and SWE-Bench Pro (58.6%), outperforming GPT-5.4 on complex coding tasks.
- Holds context across large systems, reasons through ambiguous failures, and carries changes through surrounding codebases with fewer tokens.
2. Higher Intelligence at GPT-5.4 Latency
- Co-designed, trained, and served on NVIDIA GB200/GB300 NVL72 systems to match GPT-5.4 per-token latency while performing at a significantly higher level.
- Uses fewer tokens to complete the same tasks, making it more efficient as well as more capable.
3. Powerful for Knowledge Work & Computer Use
- Scores 84.9% on GDPval (44 occupations) and 78.7% on OSWorld-Verified for autonomous computer operation.
- Excels at generating documents, spreadsheets, and reports; naturally moves across finding information, using tools, and checking output.
4. Scientific Research Co-Scientist
- Leading performance on GeneBench, BixBench, and FrontierMath; helped discover a new proof about Ramsey numbers verified in Lean.
- Strong enough to meaningfully accelerate progress at the frontiers of biomedical and mathematical research.
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-5.5 or o4-mini - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by GPT-5.5 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-5.5 or o4-mini. No code, no API keys, no deployment.
Switch models without rebuilding
Start on GPT-5.5, 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-5.5 or o4-mini - connected to the tools you already use.
Related comparisons
See how GPT-5.5 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 $5 / $30 for GPT-5.5. Actual cost depends on how many tokens your workload reads and writes.
GPT-5.5 has the larger context window at 1M 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-5.5, 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-5.5, 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-5.5 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.