GPT-4.1 vs GPT-3.5 Turbo
Compare pricing, context windows, and strengths for GPT-4.1 by OpenAI and GPT-3.5 Turbo by OpenAI - and see how to put either to work in Appaca.
GPT-4.1
A highly capable non-reasoning model that excels at instruction following, tool calling, and broad domain knowledge with a 1M-token context window.
View GPT-4.1GPT-3.5 Turbo
Legacy lightweight GPT model for cheap text generation and chat tasks; now replaced by faster, smarter, and cheaper 4o-mini models.
View GPT-3.5 TurboGPT-4.1 vs GPT-3.5 Turbo at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | GPT-4.1 | GPT-3.5 Turbo |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Model type | Text | Text |
| Context window | 1.05M tokens | 16.4K tokens |
| Input price | $2 / 1M tokens | $0.5 / 1M tokens |
| Output price | $8 / 1M tokens | $1.5 / 1M tokens |
| Status | Superseded by GPT-5.1 | Current |
How GPT-4.1 and GPT-3.5 Turbo differ
What the numbers mean in practice when choosing between GPT-4.1 and GPT-3.5 Turbo.
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GPT-3.5 Turbo is 75% cheaper on input tokens ($0.5 vs $2 per million), which adds up quickly in document-heavy workloads.
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GPT-3.5 Turbo is 81% cheaper on output tokens ($1.5 vs $8 per million) - the bigger factor for tools that generate long documents.
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GPT-4.1's 1.05M tokens context window is roughly 63.9x larger than GPT-3.5 Turbo's 16.4K tokens, so it can work across bigger codebases, contracts, or archives in one pass.
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GPT-4.1 has been superseded by GPT-5.1 - for new builds, consider the newer model first.
Strengths side by side
Where each model shines, according to benchmarks and provider positioning.
GPT-4.1
1. Smartest non-reasoning model
- Highest intelligence among models without a reasoning step.
- Great for tasks where speed + accuracy matter without deep chain-of-thought.
2. Excellent instruction following
- Very strong at structured tasks, formatting, and precise execution.
- Ideal for productized workflows and deterministic outputs.
3. Reliable tool calling
- Works smoothly with Web Search, File Search, Image Generation, and Code Interpreter.
- Supports MCP and advanced tool-enabled API flows.
4. Large 1M-token context window
- Allows extremely long conversations, large documents, and multi-file use cases.
- Handles context-heavy tasks without requiring chunking.
5. Low latency (no reasoning step)
- Faster responses than GPT-5 family when reasoning mode isn't required.
- More predictable timing for production use.
6. Multimodal input
- Accepts text + image.
- Output is text only.
7. Supports fine-tuning
- Can be fine-tuned for specialized tasks.
- Also supports distillation for smaller custom models.
GPT-3.5 Turbo
1. Extremely low-cost text model
- One of the cheapest legacy models available.
- Suitable for very high-volume workloads with simple requirements.
2. Good for lightweight NLP tasks
- Classification, summarization, rewriting, paraphrasing, intent detection.
- Works for simple logic tasks and short reasoning sequences.
3. Works well for basic chatbots
- Optimized for Chat Completions API, originally powering early ChatGPT use cases.
- Good for rule-based or templated conversation flows.
4. Stable and predictable outputs
- Legacy behavior makes it suitable for systems built years ago that rely on its quirks.
- Good for backward compatibility or long-term enterprise pipelines.
5. Supports fine-tuning
- Useful for teams maintaining older fine-tuned GPT-3.5 models.
- Allows domain-specific compression of older datasets.
6. Limited capabilities compared to newer models
- No vision, no audio, no streaming, and no function calling.
- Much weaker reasoning and correctness vs GPT-4o mini or GPT-5.1.
7. Small context window (16K)
- Limited for multi-document tasks or long conversations.
- Best used for short, simple prompts or structured tasks.
8. Recommended migration path
- OpenAI explicitly recommends using GPT-4o mini instead.
- 4o mini is cheaper, smarter, faster, multimodal, and far more capable.
Use GPT-4.1 or GPT-3.5 Turbo - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by GPT-4.1 or GPT-3.5 Turbo - 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-4.1 or GPT-3.5 Turbo. No code, no API keys, no deployment.
Switch models without rebuilding
Start on GPT-4.1, test the same tool on GPT-3.5 Turbo, 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-4.1 or GPT-3.5 Turbo - connected to the tools you already use.
Related comparisons
See how GPT-4.1 and GPT-3.5 Turbo stack up against other models in the directory.
FAQs
GPT-3.5 Turbo is generally cheaper: $0.5 input / $1.5 output per million tokens, versus $2 / $8 for GPT-4.1. Actual cost depends on how many tokens your workload reads and writes.
GPT-4.1 has the larger context window at 1.05M tokens, compared to 16.4K tokens for GPT-3.5 Turbo. 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-4.1, test the same tool on GPT-3.5 Turbo, 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-4.1, GPT-3.5 Turbo, 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-4.1 or GPT-3.5 Turbo
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.