GPT 5.6 Sol is the best "vision" model OpenAI ever released
OpenAI's new GPT-5.6 vision models (Sol, Terra, Luna) showcase significant gains in detection and counting, positioning them as the company's most advanced to date. However, Roboflow's benchmarks reveal that Google's Gemini 3.5 Flash often surpasses them in overall performance, cost, and speed. Hacker News readers weigh the general-purpose utility of these VLMs against the efficiency of traditional computer vision, scrutinizing their practical readiness for cost-sensitive, real-world applications.
The Lowdown
The Roboflow blog post provides an in-depth evaluation of OpenAI's recently launched GPT-5.6 vision models (Sol, Terra, and Luna), putting them through a VLM benchmark to assess their capabilities across various visual tasks. Following an OpenAI release stream that highlighted potential applications like UI agents and 3D visualizations, Roboflow's testing included object detection, counting, OCR, and data extraction. The findings position Sol as OpenAI's most capable vision model to date, demonstrating considerable advancements in key areas.
- Detection Dynamics: Sol achieved a notable jump in object detection performance, increasing its mAP@50 score from 13.8 to 46.2. It showed proficiency in tasks like document layout analysis and identifying objects in dense scenes. However, stability issues were observed with very large images (2000x2000 pixels or more), leading to instances of misaligned or randomly placed bounding boxes.
- Counting Capabilities: The counting accuracy for Sol improved to 73.0% (up from GPT-5.5's 64.9%), successfully handling complex scenarios such as overlapping items and conditional counting within specific zones. Challenges arose with highly uniform or visually ambiguous items, like blister packs, where it struggled to differentiate between filled and empty slots.
- OCR and Extraction Nuances: While Sol's OCR performance (90.7%) was slightly behind GPT-5.5, and text extraction also saw a minor dip, it proved effective in transcribing handwritten notes and extracting text from visually complex backgrounds, such as tire sidewalls or live sports broadcasts.
- Efficiency Considerations: The enhanced vision capabilities of GPT-5.6 models come with trade-offs: higher token consumption, increased latency (Sol averaged around 10 seconds per image), and greater cost (Sol at approximately 2.5 cents per image). Within the new lineup, Luna emerged as offering the most balanced performance in terms of speed and accuracy.
- Competitive Landscape: Despite OpenAI's advancements, the benchmark data indicates that Gemini 3.5 Flash generally outperforms GPT-5.6 models in both detection and counting benchmarks, all while offering a significantly lower cost (0.8 cents per image) and better speed, making it a more compelling option for high-volume, cost-sensitive applications.
In summary, GPT-5.6 marks a substantial step forward for OpenAI's vision capabilities, enhancing its suitability for complex tasks involving visual reasoning, UI interaction, and document processing. Nonetheless, the models still contend with issues related to cost, latency, and occasional detection instability, and face stiff competition from alternatives like Gemini 3.5 Flash in terms of overall practical utility.
The Gossip
Performance Punditry: Gemini vs. GPT
Many commenters underscored that despite GPT-5.6's improvements for OpenAI, Google's Gemini 3.5 Flash often outstrips it in benchmark performance, cost, and speed. Some felt the article understated this significant lead, with mentions that newer Gemini iterations (like 3.7) are even further ahead. Conversely, a few users shared anecdotal successes of GPT or Claude models excelling in specific, complex visual design analysis, hinting at diverse strengths across the leading AI vision models.
Practicality Puzzle: Cost, Speed, and Specificity
A core debate centered on the real-world practicality of using advanced Visual Language Models (VLMs) for tasks traditionally handled by simpler computer vision methods like OpenCV. Commenters highlighted the prohibitive latency and cost of GPT-5.6 for routine, high-throughput applications such as pill counting in pharmacy robotics. While some lauded VLMs for their general-purpose adaptability, others argued that specialized, optimized solutions remain superior for specific, high-volume needs, suggesting VLMs might be better employed for generating datasets to train more efficient, bespoke models.
Visionary Vignettes & Vexations: Real-World Experiences
Users shared a mix of compelling successes and frustrating failures from their experiences with multimodal AI. Triumphs included precise transcription of sheet music, identifying subtle reflections in challenging images, and navigating foreign grocery aisles. Frustrations stemmed from model hallucinations (e.g., a black image interpreted as a wooden kitchen wall), struggles with 'outpainting' game art, misdiagnosing plant diseases, and inability to read faded handwriting. A lively discussion also covered the subjective 'taste' and design consistency of AI in UI/UX analysis, with strong opinions on the merits of AI-generated design advice.