OpenAI Models — Complete Guide
🟢 OpenAI Models — Complete Guide
Section titled “🟢 OpenAI Models — Complete Guide”OpenAI offers models across every major AI modality — language, reasoning, code, image, video, audio, and embeddings. The lineup is split into named families, each designed for a different point on the quality-speed-cost curve.
📊 Model Family Overview
Section titled “📊 Model Family Overview”| 🗂️ Family | 🎯 Purpose | ⚡ Speed | 💰 Cost | 🌟 Latest |
|---|---|---|---|---|
| 🧠 o-series (reasoning) | Deep multi-step reasoning | Slower | Higher | o3-pro |
| 💬 GPT-5.5 | Frontier language & agents | Fast | High | GPT-5.5 |
| 💬 GPT-4.1 series | Balanced production language | Fast | Medium | GPT-4.1 / mini / nano |
| 🏠 GPT-oss (open weight) | Self-hosted / custom | Varies | Infra cost | gpt-oss-120b |
| 💻 GPT Codex | Coding agents | Fast | Medium | Codex |
| 🖼️ GPT Image | Image generation + editing | Medium | Medium | gpt-image-1.5 |
| 🎬 Sora | Video generation | Slow | High | Sora 2 |
| 🎙️ Whisper / Transcribe | Speech-to-text | Fast | Low | gpt-4o-transcribe |
| 🔊 TTS | Text-to-speech | Fast | Low | gpt-4o-mini-tts |
| 🔍 Embeddings | Text → vector for search/RAG | Very fast | Very low | text-embedding-3-large |
🧠 o-Series — Reasoning Models
Section titled “🧠 o-Series — Reasoning Models”The o-series models are built for tasks where thinking through a problem step-by-step matters more than raw speed. They use extended internal “thinking” before producing an answer.
🔬 o3-pro — Maximum Reasoning
| 🔧 Property | 📋 Detail |
|---|---|
| 🏷️ Class | Reasoning — highest compute |
| 🎯 Purpose | Hard problems where consistency matters most |
| 📄 Best for | Scientific analysis, complex architecture, production debugging |
✅ Use when:
- 🧩 The task is genuinely hard and ambiguous
- 💸 Failure would be expensive and hard to undo
- 🔬 You need maximum reasoning consistency, not just a fast answer
💡 Example:
“Analyse the security implications of this authentication flow, identify the attack vectors, and propose a fix that doesn’t break existing sessions.”
🧠 o3 — Flagship Reasoning
| 🔧 Property | 📋 Detail |
|---|---|
| 🏷️ Class | Reasoning — flagship |
| 🎯 Purpose | Math, science, coding, and multi-step analysis |
| 📄 Context | Large context window |
✅ Use for:
- 🔢 Advanced mathematics and STEM problems
- 💻 Complex coding tasks with multiple constraints
- 🏗️ Architecture and system design decisions
- 📊 Data analysis requiring multi-step reasoning
💡 Example:
“Design a rate-limiting strategy for an API that handles 50k RPM with burst tolerance, tenant isolation, and graceful degradation under load.”
⚡ o4-mini — Efficient Reasoning
| 🔧 Property | 📋 Detail |
|---|---|
| 🏷️ Class | Reasoning — compact and efficient |
| 🎯 Purpose | Fast reasoning on math, code, and visual tasks |
| 🏆 Benchmarks | Best-performing on AIME 2024 and AIME 2025 |
✅ Use for:
- 🔢 Math and logic tasks at lower cost than o3
- 👁️ Visual reasoning tasks
- 💻 Coding problems that need reasoning but not maximum depth
- ⚡ High-volume reasoning tasks where o3 would be too expensive
💬 GPT-5.5 — Frontier Language Model
Section titled “💬 GPT-5.5 — Frontier Language Model”GPT-5.5 is OpenAI’s top general-purpose language model. It sits above the GPT-4.1 family for depth of language understanding, instruction-following, and agent workflows.
| 🔧 Property | 📋 Detail |
|---|---|
| 🏷️ Model variants | GPT-5.5 · GPT-5.5 mini · GPT-5.5 nano |
| 🎯 Purpose | Frontier general reasoning, coding, planning |
| 🌐 Access | OpenAI API · ChatGPT |
| 🤖 Variant | 🎯 Best For |
|---|---|
| 💬 GPT-5.5 | Hard analysis, complex coding, agent workflows requiring depth |
| ⚖️ GPT-5.5 mini | Balanced — lower latency and cost than the full model |
| ⚡ GPT-5.5 nano | Fast, lightweight — classification, routing, extraction |
📝 GPT-4.1 Series — Production Workhorses
Section titled “📝 GPT-4.1 Series — Production Workhorses”The GPT-4.1 family introduced a major expansion: up to 1 million token context alongside improved instruction-following and coding.
💬 GPT-4.1 — Long-Context Flagship
| 🔧 Property | 📋 Detail |
|---|---|
| 📄 Context window | Up to 1,000,000 tokens |
| 🎯 Focus | Instruction-following · coding · long document analysis |
| 🌐 Access | OpenAI API · ChatGPT |
✅ Best for:
- 📄 Entire codebases or large documents in a single context
- 💻 Complex coding with many files as input
- 🔄 Long multi-turn conversations
- 🏗️ Detailed technical specification analysis
⚖️ GPT-4.1-mini — Balanced
| 🔧 Property | 📋 Detail |
|---|---|
| 📄 Context window | Up to 1,000,000 tokens |
| 🎯 Focus | Speed + quality balance |
| 💰 Cost | Lower than GPT-4.1 |
✅ Best for:
- 💬 Production chat assistants
- 📝 Summarisation and drafting
- 🔄 High-volume tasks needing good quality without the top price
⚡ GPT-4.1-nano — Fastest and Cheapest
| 🔧 Property | 📋 Detail |
|---|---|
| 📄 Context window | Up to 1,000,000 tokens |
| 🎯 Focus | Maximum speed and minimum cost |
| 💰 Cost | Lowest in the GPT-4.1 family |
✅ Best for:
- 🏷️ Classification, labelling, routing
- 🔎 Simple extraction from text
- ⚡ High-volume background processing
- 📱 Edge-like latency requirements via API
🏠 GPT-oss-120b — Open-Weight Model
Section titled “🏠 GPT-oss-120b — Open-Weight Model”OpenAI’s first open-weight model, released under the Apache 2.0 licence.
| 🔧 Property | 📋 Detail |
|---|---|
| ⚙️ Parameters | 117 billion (MoE — 5.1B active at inference) |
| 📜 Licence | Apache 2.0 — free to use and modify |
| 🏃 Deployment | Self-hosted · any third-party inference provider |
✅ Best for:
- 🏠 On-premise deployment where data cannot leave the organisation
- 🔧 Fine-tuning on proprietary data
- 💰 Cost savings at very large inference scale
- 🏢 Organisations with compliance requirements preventing cloud API use
🖼️ GPT Image — Image Generation and Editing
Section titled “🖼️ GPT Image — Image Generation and Editing”🖼️ GPT Image 1 / gpt-image-1.5
| 🔧 Property | 📋 Detail |
|---|---|
| 🏷️ Model | GPT Image 1 (gpt-image-1.5 is the latest snapshot) |
| 🎯 Purpose | Text-to-image generation + image editing |
| 📜 Note | DALL-E is the older brand — use GPT Image for modern workflows |
✅ Use for:
- 🖼️ Blog thumbnails and marketing visuals
- 🎨 Product images and concept art
- ✏️ Image editing and inpainting
- 📱 UI mockups and visual prototyping
⚠️ Note: DALL-E model snapshots were deprecated from the API in May 2026.
🎬 Sora — Video Generation
Section titled “🎬 Sora — Video Generation”🎬 Sora / Sora 2 — Text-to-Video
| 🔧 Property | 📋 Detail |
|---|---|
| 🏷️ Models | Sora · Sora 2 |
| 🎯 Purpose | High-quality video generation from text prompts |
| ⚠️ API Status | Sora 2 Video API deprecated September 2026 |
✅ Use for:
- 🎬 Short video clips for marketing or content
- 🎨 Creative concept visualisation
- 📱 Social media video content
⚠️ Note: Check current API availability — Sora 2 Video API is being deprecated from the API. The product (ChatGPT) may retain access.
🎙️ Audio Models — Speech-to-Text and Text-to-Speech
Section titled “🎙️ Audio Models — Speech-to-Text and Text-to-Speech”🎙️ Whisper and gpt-4o-transcribe
| 🤖 Model | ↔️ Direction | 🎯 Best For |
|---|---|---|
| whisper-1 | 🎙️ Audio → 📄 text | Standard transcription, established pipelines |
| gpt-4o-transcribe | 🎙️ Audio → 📄 text | Higher accuracy, modern transcription |
| gpt-4o-mini-transcribe | 🎙️ Audio → 📄 text | Fast, cost-efficient transcription |
✅ Use for:
- 📋 Meeting and call transcription
- 🎬 Subtitle generation
- 🔍 Audio content indexing for search
- 📞 Call centre analytics
🔊 Text-to-Speech — gpt-4o-mini-tts
| 🔧 Property | 📋 Detail |
|---|---|
| 🏷️ Model | gpt-4o-mini-tts |
| 🎯 Purpose | Generate spoken audio from text |
| 🌐 Access | OpenAI API |
✅ Use for:
- 🤖 Voice agent responses
- 📖 Narration and accessibility
- 📞 IVR systems and customer support bots
🔍 Embedding Models — Semantic Search and RAG
Section titled “🔍 Embedding Models — Semantic Search and RAG”🔍 text-embedding-3-large and text-embedding-3-small
| 🤖 Model | 📐 Dimensions | 🎯 Best For |
|---|---|---|
| text-embedding-3-large | Up to 3,072 | High-precision RAG and semantic search |
| text-embedding-3-small | Up to 1,536 | Cost-efficient high-volume search |
✅ Use for:
- 🔍 Semantic search and document retrieval
- 🤖 RAG pipelines — embed documents, retrieve context, pass to LLM
- 🔄 Duplicate and near-duplicate detection
- 💡 Content recommendation engines
⚠️ Embed documents at index time. Embed queries at search time. Never ask the embedding model to answer a question.
🎯 Which OpenAI Model to Use
Section titled “🎯 Which OpenAI Model to Use”| 🎯 Task | ✅ Best Model | 💡 Why |
|---|---|---|
| 🔬 Hard scientific or logic problem | o3 or o3-pro | Extended reasoning for difficult analysis |
| 💻 Complex multi-file coding task | o3 or GPT-4.1 | Reasoning + large context for whole-repo tasks |
| 🚨 Production incident debugging | o3-pro | Maximum reasoning consistency |
| 📄 Long document or large codebase (1M tokens) | GPT-4.1 | Up to 1M token context window |
| 💬 Everyday chat or writing assistant | GPT-4.1-mini or GPT-5.5-mini | Good quality at reasonable cost |
| 🏷️ Classification, routing, extraction | GPT-4.1-nano or GPT-5.5-nano | Cheapest and fastest for simple tasks |
| 🖼️ Generate an image | gpt-image-1.5 | Current OpenAI image generation model |
| 🎙️ Transcribe audio | gpt-4o-transcribe | Best accuracy for speech-to-text |
| 🔍 Semantic search or RAG retrieval | text-embedding-3-large | High-quality vectors for precision retrieval |
| 🏠 Self-hosted / on-premise | gpt-oss-120b | Open-weight, Apache 2.0, runs anywhere |
🌐 Where to Access OpenAI Models
Section titled “🌐 Where to Access OpenAI Models”| 🏷️ Platform | 🤖 Models Available |
|---|---|
| 🟢 OpenAI API (platform.openai.com) | All models |
| 💬 ChatGPT | GPT-5.5, GPT-4.1, o3, o4-mini, GPT Image, Sora |
| 🔷 Azure OpenAI Service | GPT-4.1, o3, Whisper, embeddings |
| 🏠 Self-hosted | gpt-oss-120b (Apache 2.0) |
| 🔀 Third-party inference providers | gpt-oss-120b |