Hello! I'm your financial journalist and friend, an economist. Today, we're talking about the newly released GPT-Image 2.5 model by OpenAI.
This review article from “Zhi Wei” reveals a very interesting business and technological phenomenon: there's often a huge gap between the “amazing” capabilities of technology and its “stability.”
To help you easily understand this in-depth review, I'll first summarize the key findings and then break down the logic behind them from five different perspectives.
📌 Key Points in a nutshell:
OpenAI's new GPT-Image 2.5 has made a significant breakthrough in “image consistency” and can theoretically be used to create GIF animations. Netizens have even devised a clever method to use it to generate multiple images into a video. While the results are impressive in some simple scenarios, the model is extremely unstable when dealing with more complex actions, multiple characters, or special camera angles. For now, it's more like a “highly promising half-finished product” – still far from replacing professional animation software – but its emergence marks a new stage in image generation, one focused on achieving stability.
---
🔍 In-depth breakdown: A layman's explanation of five key aspects
1. Technical highlight: From randomness to stability – consistency is the key selling point
In simple terms:
Previously, AI-generated images were like a lottery: you might get a good result, but if you asked it to change the color of a dress, it might distort the face or the background. This is what’s known as “image drift” or “jitter.”
GPT-Image 2.5’s greatest strength is that it’s “ obedient and doesn’t make random changes.” It only modifies the part you ask it to, leaving the rest unchanged. This level of stability is its most valuable feature.
- Why’s this important? Creating animations or continuous images traditionally requires professional software like After Effects, which is very expensive. If AI could generate these images stably, it would significantly lower the barrier to creating GIFs.
2. The “cheat” method: How netizens use it to create videos for free
In simple terms:
OpenAI hasn’t officially released a video generation tool, but users found a loophole:
1. They ask the AI to generate a large image with 16 small squares (4x4 arrangement), each representing a moment of an action.
2. They then use another AI tool (ChatGPT Work) to cut the image into these 16 squares.
3. These squares are combined into a GIF animation.
This is like asking a painter to create a long comic strip, which is then cut up and played quickly to create an animation.
- The Higgsfield example: A team demonstrated the ultimate result of this method, showing a Transformers transformation that looked as smooth as a real video, which raises high hopes for GPT-Image 2.5’s potential.
3. The reality check: Issues found in basic tests
In simple terms:
The review team didn’t just praise the model; they conducted rigorous tests. They found that it works well with simple actions but fails with more complex ones:
- Simple kicking: The animation flows smoothly and repeats without issues.
- Complex actions (drawing a sword and swinging): The animation starts to jitter, and the model makes basic mistakes, like swapping legs between frames.
- Multiple characters interacting: When two characters perform different actions, the image becomes uncontrolled.
- Special camera angles: The model struggles with angles like a vertical rotation, causing abrupt changes.
Conclusion: It’s currently best for transparent backgrounds, a single character, and simple actions. Any more complexity greatly reduces its stability.
4. The challenge of creating complex scenes
In simple terms:
The team tried the most difficult scenario: generating 30-72 images for a complete Transformers transformation video.
- Time-consuming: What took 5 minutes for simple tasks took 30 minutes.
- Poor quality: Even with the advanced Sunburst Max model, the result wasn’t as smooth as the Higgsfield example.
- Physics violation: The AI uses fluid dynamics instead of mechanical movements, which doesn’t match real physics.
- Dependent on other tools: The final image was only possible because ChatGPT Work continuously checked, corrected, and fixed errors. This shows that a single image model isn’t enough; a combination of a language model and an image model is needed.
5. Commercial value and future trends
In simple terms:
Although the results were a bit disappointing, from an economic perspective, GPT-Image 2.5 is still significant:
- Improved efficiency: Creating a perfect marketing image used to require multiple attempts, but now, with higher consistency, fewer attempts are needed. This reduces both computational costs and the need for manual editing.
- Reducing reliance on professional software: AI can now handle much of the image editing.
- Future competition: The focus will no longer be on who can create the most beautiful images, but on which models are the most stable, cost-effective, and capable of automatic error correction.
Tips for everyone:
- Don’t expect it to create movies yet: It’s still unstable for complex animations.
- Great for simple effects: It’s highly cost-effective for Logo animations, simple memes, and dynamic marketing posters.
- Focus on workflows: The future winners won’t be individual models, but intelligent workflows that can automatically check, correct, and combine multiple models like ChatGPT Work.
---
💡 Journalist’s note:
GPT-Image 2.5 is like a “genius but temperamental intern.” It’s very capable, but it can become unstable when dealing with complex tasks. For businesses and individuals, it’s not yet ready for professional animation, but using it to reduce the cost of simple image production is already a real benefit. The future competition will not be about who can create the most beautiful images, but who can do so more stably, cost-effectively, and with fewer errors.
In one sentence: The technology is impressive, but it needs to be used carefully. Costs have decreased, so we should adjust our expectations accordingly.