Roboflow Playground: compare vision AI models free

What Roboflow Playground lets you compare for free

Roboflow Playground is a free browser tool that sends one image and one prompt through several vision AI models and shows the results side by side. The home page says you do not need an API key, a notebook or a credit card. The post describes these models as zero-shot: they handle categories they were not trained on, so you describe the target in a prompt instead of preparing training examples.

Roboflow's post names three chores the tool removes: researching which new models to try, writing code to call cloud APIs, and setting up servers for open-weight models. The post adds that a new model is often out before that work ends.

Roboflow's blog post of Aug 27, 2026 and the Playground home page as of Oct 11, 2026 give different figures in places. Wherever a figure differs, it appears below with the date of its source.

The vision AI models in the Model Directory

The Model Directory lists 134 models in the Aug 27, 2026 post and 140+ on the Oct 11, 2026 page, newest first. The models come from Google, OpenAI, Anthropic, Meta, Qwen and other labs, and you can filter the list by modality, organization, license and task. The post counts three kinds of model:

• Open-source models: 87, with downloadable Apache 2.0 or MIT weights
• Proprietary API models: 47, priced per million tokens for input, output and cached input
• Specialized single-task models: 49, such as YOLO26 and RF-DETR

The kinds overlap, so the three counts add up to more than 134 and should not be summed. Among the open-source models, 37 run live in Playground through hosted APIs, which makes self-hosting optional for that group. Each model page offers a live demo, a licensing breakdown, 30-day usage numbers and code snippets in Python, JavaScript and cURL.

Which task you pick decides which models appear

Playground offers 25 vision tasks, and the model list filters by the one you choose. "The 25 tasks on the Playground page, grouped by output" groups the tasks on the page by what each one returns.

The 25 tasks on the Playground page, grouped by output

Group	Tasks on the page
Boxes and masks	Object Detection, Open Vocabulary Object Detection, Phrase Grounding, Region Proposal, Instance Segmentation, Semantic Segmentation, Promptable Concept Segmentation, Zero Shot Segmentation
Text and documents	OCR, Document Question Answering, Chart Question Answering
Descriptions and questions	Captioning, Image Tagging, Classification, Multi-Label Classification, Visual Question Answering, Vision Language
Geometry and video	3D Reconstruction, Depth Estimation, Keypoint Detection, Pose Estimation, Video Classification, Video Object Tracking
Embeddings	Image Embedding, Image Similarity

Vision Language is the task for vision-language models, which read an image together with a text prompt and answer in text. The post gives an example: Gemini 3.6 Flash and SAM 3 both support object detection, but SAM 3 cannot answer open questions about an image because it does not support visual question answering. If a model you expected is missing, change the task first.

Comparing object detection models on your own image

The post walks through one object detection run. It goes like this:

1. Choose Object detection from the task dropdown.
2. Upload an image and set the prompt. The post uses a photo of a book and a coffee cup with the prompt "glass mug".
3. Run it.

Playground draws each model's bounding boxes and labels over the image. A raw JSON option shows the exact coordinates. Speed and cost update with every run, which lets you weigh a paid model such as Qwen3.8 Max against a free open model such as SAM 3.

A shareable URL saves the setup, the classes and the results. For vision-language models, a settings popover sets the thinking level and the maximum output tokens. The post's example moves the slider so GPT-5.6 Sol uses its full 128,000-token output window.

Preparing the photo is optional. Compressing the image or resizing it before upload keeps a large file manageable.

Why the blog post says five models and the page says four

The two Roboflow sources give different limits, and no source explains why. The post of Aug 27, 2026 says up to five models per run, while the home page as of Oct 11, 2026 says up to four. The next table puts both side by side.

Roboflow's own figures, by source and date

Figure	Blog post, Aug 27, 2026	Home page, Oct 11, 2026
Models per run	Up to five	Up to four
Catalog size	134 (the title says 130+)	140+
Tasks	25	25
Compare view	Two to four models	Two to four models

"Roboflow's own figures, by source and date" lists the figures only as each source states them. Read the limit off the live page before you plan a test.

The Compare view shows specs, context windows, token pricing, latency and Vision Evals scores for two to four models in one view. The post says pricing syncs continuously.

Arena rankings come from blind human votes

Arena mode hides which model produced an output, and the user votes for the one that performed best. The post says every fifth query in Playground runs such a blind comparison to collect human preference data. Those votes feed the live arena rankings.

The home page says live ELO rankings and Vision Evals show how each model scores. ELO is a rating number that moves with head-to-head results, and the page ties its live rankings to the arena you can vote in.

The post also names their limit: human votes are a useful signal, but production requires objective truth and certainty. Vision Evals, covered next, supplies that ground truth.

Vision Evals scores models against ground truth

Vision Evals tests 34 vision-language models on six tasks with known correct answers: Object Detection, Counting, Identification, OCR, Data Extraction and Visual Reasoning. Object detection is scored with mAP@50, OCR with text similarity, and the other four tasks by exact match. Every model runs at low reasoning effort so scores stay comparable, and reasoning is also tested at high effort side by side.

Token counts come from API responses, and costs multiply the measured usage by current per-million-token pricing. Scores update as providers release new model weights. The Vision Evals page has four parts:

• A leaderboard table that ranks all 34 models by overall score, with average accuracy, tokens per sample, estimated cost per sample and average inference speed
• A scatter plot of score against cost, with a toggle for speed; the upper-left corner means high accuracy at low cost
• Cards for each of the six tasks, listing the top three models
• A per-task heatmap, with dark green at 75 percent or higher, light green from 40 to 74, and yellow below 40

The two ranking systems answer different questions. "Arena ELO and Vision Evals compared" sets them side by side.

Arena ELO and Vision Evals compared

	Arena ELO	Vision Evals
Evidence	Blind votes from people	Scores against ground truth
Scope	Any image users upload	Six fixed tasks, 34 models
Updates	As votes come in	As new model weights are released

On the home page as of Oct 11, 2026, GPT-6 Astra led the overall Vision Evals ranking at 83.4%. The list changes as models are added, so the current ranking is on the page.

From Playground to production: off-the-shelf or custom

Vision-language models suit exploration while the requirements are still changing. Once the classes are fixed and the volume of images grows, the post advises validating the requirements with a vision-language model and then training a specialized model such as RF-DETR on your own annotated data. It says that model runs faster and cheaper.

Roboflow Workflows can chain both kinds. In the post's example, a specialized detector finds a defect on a conveyor belt and crops that region, and the vision-language model reads only the crop. The pipeline deploys to cloud APIs or edge hardware, and Roboflow Agent can build one from a plain-language prompt.

If you are still choosing among vision AI models, start with a free comparison on your own image and read the arena and Vision Evals numbers as two different kinds of evidence. Training your own model comes later, once the classes are fixed and the volume of images grows.