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GPT 5.6 Sol is the best "vision" model OpenAI ever released
massive gains in object detection and counting.
still very strong in OCR
↓ deep GPT 5.6 dive
Sources: Cursor is building a general-purpose AI agent codenamed Sand, aimed at non-developers, that handles emails, texts, and documents to rival Claude Cowork (@graceihle / The Information)
(Visit Techmeme dot com for the link and full context!)
object counting is one of the most popular use cases for VLMs
what previously required training a model on a custom dataset now fits into a prompt
quality control, inventory checks, shelf monitoring
check how different VLMs handle object counting: https://t.co/OTrn9gP4Mj
I’m evaluating fable 5 on object detection, but so far it performs much worse than gemini 3.5 flash
any tips?
it struggles with cluttered scenes and overlapping objects
try gemini 3.5 flash vs fable 5 for free: https://t.co/8xnioJ6HkG
↓ some positive fable 5 examples
very good thread with sample data showing Fable 5 performance against other models for vision tasks
important to see the types of tasks frontier models can/cant do
will give you ideas for how to use them successfully in production
need a model for OCR and VQA? Fable 5 is NOT your best option
Gemini 3.5 Flash is 1.3x faster, 2.9x cheaper, and more accurate
Fable 5 struggles especially on tasks that require counting objects.
↓ cost comparison and interesting failure cases
Fable 5 is back and tied as a top vision model, Sonnet 5 is outside the top 10
Fable 5 is ~3x slower and ~10x more expensive than Gemini 3.5 though
If you're set on Anthropic models, it's the best one for vision
The delta between Sonnet 5 and Fable 5 is really large, Sonnet 5 is slightly worse than Qwen 3.5 9B
but very fast if you're looking for speed!
You can now build vision applications with Fable 5 in Roboflow
Fable 5 is the best Anthropic model for visual understanding, beating Opus 4.8 on our evals
very high object understanding capability means it's a great option when using a VLM as a judge in your pipeline
ideal for a final step in a process that requires judgement or interpretation of a situation
of course, nails most document, label extraction, ocr, scene reports, etc that you expect of a large frontier model
every legacy vision product/system/company can probably be built with open source tools and models at this point
and then putting them in production is 100x cheaper than it's ever been
vision is no longer cost prohibitive to build and deploy
I rebuilt FIFA's VAR offside tech run on a single camera for about $2 a match 📈
-Find pitch lines + vanishing point- HSV color matching
-Find players - RF-DETR
-Recreate the moment in 3D - SAM3D Body + 3js
We added video understanding and Gemini 3.5 Flash wins there as well
Seems like the all around best frontier vision model
If you're starting to build vision apps, use Gemini 3.5 Flash to prototype
You can quickly build a work pipeline across different use cases and types of data, then move to a fine-tuned model as you scale up
Expanding the video benchmark soon, just getting started
we saw the same thing with our vision leaderboard
still testing and wrapping our heads around Gemini 3.5 Flash but it feels like a pivotal shift in frontier models
way cheaper and way faster than anything else, and performance is super high
first time it feels like you can really use a VLM for high scale tasks. lots of previous tasks that required making workflows of smaller, cheaper models now looks possible (speed and cost wise) with Gemini 3.5 Flash
a few examples below