I have introduced Azure OpenAI models and gone into detail about some models in previous blogs. You can find it here:
- Language Models: https://blog.nashtechglobal.com/azure-openai-service-models-part-1-language-models/
- Reasoning & Pronlem-Solving Models: https://blog.nashtechglobal.com/azure-openai-service-models-part-2-reasoning-problem-solving-models/
- Multimodal Models: https://blog.nashtechglobal.com/azure-openai-service-models-part-3-multimodal-models/
Now, let’s look at another type of model: Image Generation Models and Speech Recognition Models!
Image Generation Models
An Image Generation Model is an AI system that creates images from non-image inputs — most commonly text prompts, but sometimes also other images (for editing or transformation).
Think of it as a digital artist that reads or sees your instructions, then paints a picture accordingly.
Core Capabilities
Text → Image
You describe an idea in words, and the model generates an image that matches the description.
Example:
Prompt: "A futuristic city at sunset, viewed from above, with flying cars."
→ The model outputs a high-resolution, original artwork.Image → Image (Editing)
You provide an existing image and tell the model what to change — like replacing the sky, changing colors, or adding objects.
Inpainting
You mask part of an image and the model fills in the missing area in a realistic or creative way.
Outpainting
The model extends an existing image beyond its borders, adding matching scenery or objects.
How It Works (Simplified)
- Training Data: Millions or billions of image–text pairs.
- Neural Network: Often based on diffusion models or transformer architectures.
- Generation Process:
- Starts with noise (like TV static).
- Gradually “denoises” while following the prompt until the final image emerges.
Examples in Azure OpenAI
- DALL·E 3 – Highly creative, strong at interpreting natural language prompts.
- GPT-Image-1 – Newer, better at detailed instruction following, accurate text rendering in images, and precise editing.
Let’s compare DALL·E 3 and GPT-Image-1
Note: Below are just the writer’s personal experiments.
Test Prompt
“A cozy reading corner with a large window showing a snowy mountain landscape, warm yellow lighting, and a sleeping golden retriever on the rug.”
DALL·E 3
Strengths:
- Creativity & Composition: Often produces more artistic, imaginative interpretations.
- Storybook Quality: Can lean toward painterly or illustration-like styles.
- Prompt Expansion: Adds artistic flair beyond what’s described.
Example Output (typical characteristics):
- Warm, saturated colors.
- Slightly dreamy look.
- Details like extra bookshelves or a steaming mug, even if not requested.
GPT-Image-1
Strengths:
- Realism & Detail: Excels at producing photorealistic textures, accurate lighting, and natural proportions.
- Instruction Fidelity: Follows complex, multi-part prompts more literally without excessive artistic interpretation
- Iterative Refinement: Works seamlessly with conversational updates, allowing multi-step edits in the same chat
Example Output:
- Photorealistic textures: detailed dog fur, realistic sunlight patterns
- Mountains more defined and true-to-life
- Room elements proportionally accurate (rug, chair, window frame)
- Lighting and shadows follow physically realistic behavior
Comparison Results
| Aspect | DALL·E 3 | GPT-Image-1 |
| Purpose | Text-to-image model with strong creative/artistic rendering. | Newer image generation model built into GPT-4o, better at following precise, nuanced instructions. |
| Strengths | – Highly creative, painterly, or illustrated styles. – Strong color harmony and composition. – Great for imaginative concepts. – Robust inpainting (partial edits). | – Strong realism & photorealistic detail. – Better at complex multi-element prompts. – Can refine results interactively in the same conversation. – Handles spatial relationships more accurately. |
| Weaknesses | May take artistic liberties with small details; sometimes less literal for technical scenes. | Sometimes slightly inconsistent in stylistic cohesion for purely artistic illustrations. |
| Best For | Art, marketing illustrations, book covers, stylized storytelling visuals. | Product mockups, realistic photography-style images, multi-step design refinement. |
Speech Recognition Models
Speech Recognition Models are AI models designed to convert spoken language (audio) into written text.
They’re also called ASR (Automatic Speech Recognition) models.
Key Capabilities
- Transcription – Convert spoken audio to accurate text.
- Translation – Render speech from one language into text in another.
- Speaker Diarization – Identify and separate speech by different speakers.
- Noise Robustness – Recognize speech even in noisy environments or with varied accents.
Speech Recognition Models in Azure OpenAI
Azure OpenAI includes dedicated and integrated transcription models:
Whisper
- Multilingual transcription and translation.
- Handles a wide variety of accents and background noise.
gpt-4o-transcribe
- Uses GPT-4o’s multimodal capabilities for speech-to-text.
- Can directly accept audio input and return accurate transcripts.
- Works well for real-time, low-latency applications.
gpt-4o-mini-transcribe
- Lighter, faster, and more cost-efficient version of GPT-4o transcription.
- Suitable for quick processing where lower latency and cost are priorities.
Typical Use Cases
- Meeting Notes – Automatically transcribe team discussions.
- Customer Service Analytics – Convert call center audio into searchable text
- Voice Command Interfaces – Control systems or applications by speech.
- Closed Captioning – Generate real-time captions for live events or recorded videos.
Let’s compare Whisper, gpt-4o-transcribe, and gpt-4o-mini-transcribe
Note: Below are just the writer’s personal experiments.
Example 1: Meeting Transcription
- Whisper: Transcribes entire 2-hour meeting in English and Vietnamese accurately; saves as text for later review.
- gpt-4o-transcribe: Transcribes in real time and summarizes action items automatically.
- gpt-4o-mini-transcribe: Transcribes in real time with minimal delay, but no deep summarization—just raw text.
Example 2: Customer Service Call
- Whisper: Produces multilingual transcript, even with background noise.
- gpt-4o-transcribe: Transcribes and classifies sentiment, flags urgent issues.
- gpt-4o-mini-transcribe: Transcribes for quick keyword search, without advanced analytics.
Example 3: Live Event Captions
- Whisper: Works well offline for pre-recorded audio but may lag for live captions.
- gpt-4o-transcribe: Provides real-time captions and adjusts tone/phrasing for clarity.
- gpt-4o-mini-transcribe: Provides ultra-low-latency captions with fewer stylistic adjustments.
Comparison Results
| Feature / Example | Whisper | gpt-4o-transcribe | gpt-4o-mini-transcribe |
| Core Strength | Robust multilingual transcription & translation | Accurate transcription with integrated GPT-4o reasoning | Fast, low-cost transcription |
| Speed | Medium | Fast | Very fast |
| Cost | Low | Higher than Whisper | Lower than gpt-4o-transcribe |
| Best Use Case | Large-batch transcription of audio files in many languages | Real-time transcription with context understanding | Quick transcription for lightweight tasks |
| Noise Handling | Excellent | Good | Good |
| Multilingual Support | Yes (broad range) | Yes (slightly fewer low-resource languages) | Yes (same as gpt-4o-transcribe) |
| Integration with AI Reasoning | No (pure transcription) | Yes (can summarize, analyze, or translate in the same call) | Limited (basic reasoning possible) |
Summary
Azure OpenAI provides a range of models across five main categories:
- Language Models like GPT-4 and GPT-3.5 Turbo handle text understanding, chat, summarization, and content creation.
- Reasoning Models such as the o-series (o3-mini, o1) excel at logic-heavy, math, and code reasoning tasks.
- Multimodal Models like GPT-4o process text, images, and audio together for richer interactions.
- Image Generation Models (DALL·E 3, DALL·E 2, GPT-Image-1) create high-quality visuals from text prompts.
- Speech Recognition Models such as Whisper, gpt-4o-transcribe, and gpt-4o-mini-transcribe convert audio into text, with varying trade-offs in speed, cost, and reasoning ability.
This lineup lets developers choose the best model for pure language, complex reasoning, multimodal interaction, creative imagery, or accurate transcription.
Note: The above results are only based on the author’s personal experience and testing, depending on the case, the results may be different. In addition, AI is constantly developing and new models will be continuously released, so in the future, the recommendations may no longer be correct.