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LLM ComparisonGPT-5.3 CodexGPT-3.5 Turbo

GPT-5.3 Codex vs GPT-3.5 Turbo

Compare GPT-5.3 Codex and GPT-3.5 Turbo. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.3 CodexGPT-3.5 Turbo
ProviderOpenAIOpenAI
Model Typetexttext
Context Window400,000 tokens16,385 tokens
Input Cost
$1.75/ 1M tokens
$0.50/ 1M tokens
Output Cost
$14.00/ 1M tokens
$1.50/ 1M tokens

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Strengths & Best Use Cases

GPT-5.3 Codex

OpenAI

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

OpenAI

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.