GPT-5.3 Codex vs GPT-3.5 Turbo
Compare pricing, context windows, and strengths for GPT-5.3 Codex by OpenAI and GPT-3.5 Turbo by OpenAI - and see how to put either to work in Appaca.
GPT-5.3 Codex
Most capable agentic coding model to date, optimized for long-horizon software engineering tasks with configurable reasoning and multimodal input.
View GPT-5.3 CodexGPT-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-5.3 Codex vs GPT-3.5 Turbo at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | GPT-5.3 Codex | GPT-3.5 Turbo |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Model type | Text | Text |
| Context window | 400K tokens | 16.4K tokens |
| Input price | $1.75 / 1M tokens | $0.5 / 1M tokens |
| Output price | $14 / 1M tokens | $1.5 / 1M tokens |
| Status | Current | Current |
How GPT-5.3 Codex and GPT-3.5 Turbo differ
What the numbers mean in practice when choosing between GPT-5.3 Codex and GPT-3.5 Turbo.
-
GPT-3.5 Turbo is 71% cheaper on input tokens ($0.5 vs $1.75 per million), which adds up quickly in document-heavy workloads.
-
GPT-3.5 Turbo is 89% cheaper on output tokens ($1.5 vs $14 per million) - the bigger factor for tools that generate long documents.
-
GPT-5.3 Codex's 400K tokens context window is roughly 24.4x larger than GPT-3.5 Turbo's 16.4K 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-5.3 Codex
1. Strongest Codex Model for Agentic Engineering
- OpenAI positions GPT-5.3 Codex as its most capable agentic coding model to date.
- Built for long-horizon software engineering tasks that require planning, iteration, and reliable code transformation across files.
2. Configurable Reasoning + Multimodal Input
- Supports configurable reasoning effort from low to xhigh so teams can trade off depth against latency.
- Accepts both text and image inputs while producing text output.
3. Large Context for Real Codebases
- 400 k token context window helps it work across larger repositories, implementation plans, and supporting documentation.
- Allows up to 128 k output tokens for longer code generations, patches, and technical write-ups.
4. Current Knowledge for Modern Dev Workflows
- Knowledge cut-off of Aug 31 2025 keeps it aligned with newer frameworks, libraries, and tooling.
- Supports streaming, function calling, and structured outputs for agent-style coding workflows.
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-5.3 Codex or GPT-3.5 Turbo - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by GPT-5.3 Codex 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-5.3 Codex or GPT-3.5 Turbo. No code, no API keys, no deployment.
Switch models without rebuilding
Start on GPT-5.3 Codex, 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-5.3 Codex or GPT-3.5 Turbo - connected to the tools you already use.
Related comparisons
See how GPT-5.3 Codex 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 $1.75 / $14 for GPT-5.3 Codex. Actual cost depends on how many tokens your workload reads and writes.
GPT-5.3 Codex has the larger context window at 400K 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-5.3 Codex, 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-5.3 Codex, 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-5.3 Codex 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.