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roboflow playground test compare vision ai models free roboflow products solutions resources pricing docs blog toggle theme sign in home playground rankings vision evals models explore compare 2026 roboflow terms the vision ai playground test and compare models on your own images run 150 vision ai models side by side free pick from google openai anthropic meta qwen and more try detection ocr captioning classification segmentation and 20 other vision tasks live arena rankings show which models win upload an image browse models 150 models 25 vision tasks live arena rankings free to use top models on vision evals by overall score rank model score overall score est cost sample speed 1 google gemini 4 argon 87 5 0 0058 13 40 s 2 openai gpt 6 1 terra new 85 8 0 0033 10 05 s 3 openai gpt 6 astra 83 4 0 034 9 94 s 4 anthropic claude opus 5 5 82 0 0 015 7 63 s 5 google gemini 3 5 flash 81 8 0 012 11 52 s see full evals what do you want to test object detection boxes around your objects ocr read text from images image captioning describe any image classification label whole images segmentation pixel perfect masks visual q a ask questions of images pose estimation track body keypoints all 25 tasks full task index everything in the vision ai playground model directory browse 150 vision ai models from every major lab newest first explore models compare models see specs and tasks for 2 to 4 models side by side compare vision models vision evals how models score on set image tasks we add each new model browse evals test vision ai models online for free you don t need an api key a notebook or a credit card to find your model upload one of your own images pick your task and run up to four models on it at once free in seconds you see how each one handles your lighting your objects and your edge cases with the outputs side by side openai gpt 6 1 terra new gpt 6 1 terra is an openai model released on october 26 2026 for visual reasoning computer use and writing it accepts text and image inputs and produces text with a 1 050 000 token context window and up to 128 000 output tokens openai lists applications including matching product photographs to catalog entries interpreting technical drawings and operating application screens the api exposes six reasoning effort settings none low medium high xhigh and max medium is the default the model supports structured outputs and tool calling through the responses api vision evals reports its low and high results for text understanding counting identification reasoning and object detection using native openai api inference terra is a proprietary model its weights are not released vlm meta muse spark open muse spark open is an open weight multimodal model released by meta on october 15 2026 its sparse mixture of experts architecture has 816 billion total parameters and activates 42 billion per token it accepts text images audio and video produces text and supports a context window of 1 048 576 tokens meta provides bf16 and nvfp4 checkpoints for self hosting alongside access through hosted inference partners the model is designed for coding tool use and multi step reasoning over long tasks its visual inputs support workflows involving screenshots documents and other image content vision evals measures its text understanding counting identification reasoning and object detection at low and high effort the published weights use the custom muse model license with commercial use subject to its terms and the accompanying usage policy vlm google gemini 4 argon gemini 4 argon is google deepmind s frontier gemini 4 model built to sustain extended reasoning across long horizon workflows in software engineering enterprise knowledge work such as legal and finance scientific analysis and defensive cybersecurity it accepts text image video and document input and returns text operating over a one million token context window the model raises the maximum output length to one million tokens compared with a 64k ceiling on earlier gemini releases so a single reasoning trajectory can run for hundreds of thousands of tokens before terminating on the visual side argon targets multimodal document comprehension professional chart analysis and retrieval of specific details from long recordings reporting 91 7 on lvbench for long video understanding and 71 6 on chartography for chart reasoning google also reports 77 9 on deepswe v1 1 51 3 on automationbench 68 on cwe bench v1 and first place on the vals index which aggregates finance coding legal and tax tasks following gemini api conventions it localizes objects by emitting bounding boxes as ymin xmin ymax xmax integers normalized to a 0 to 1000 range which supports prompt driven detection and grounding alongside captioning document parsing and visual question answering google describes adversarial training and automated red teaming for indirect prompt injection resistance plus chain of thought and action monitors that halt execution when behavior drifts outside user intent vlm meta muse wonder muse wonder is a proprietary multimodal reasoning model from meta released on october 12 2026 as the first step past the muse spark line on meta s scaling ladder it launched together with muse wonder full a larger configuration that is available only through muse code subscription plans while muse wonder itself is served through the meta model api under the identifier muse wonder 1 0 the model accepts text images video audio and pdf documents as input and returns text with a context window of 1 048 576 tokens and graded reasoning effort settings meta trained both models at its prometheus data center on larger base models than muse spark 1 3 and attributes their gains on unfamiliar tasks to post training that transfers more effectively from a stronger base meta positions muse wonder for long running personal and professional agents it plans multi step work delegates to sub agents calls tools in parallel recovers from errors and operates browsers and desktop or mobile applications two training objectives shape this behaviour active memory carries goals constraints and earlier decisions across conversations and retrieves the details relevant to the current step and active focus keeps the model trying alternative approaches when a task gets difficult while asking for clarification before important actions visual inputs such as screenshots charts and video feed the same reasoning loop and the model emits bounding box coordinates for grounding queries meta s model card lists visual and spatial reasoning especially 3d tasks as a known limitation and recommends validating performance for applications that depend on precise spatial understanding vlm anthropic claude haiku 5 5 claude haiku 5 5 is a proprietary multimodal language model from anthropic and the smallest member of the claude 5 5 family released on october 7 2026 after claude opus 5 5 and claude sonnet 5 5 it accepts text and image input and returns text with a 1m token context window and up to 128k output tokens per request it is the first haiku class model with an adjustable effort parameter adaptive thinking is on by default and the model decides how much to reason steered by effort levels from low to max with medium as the default its training data cutoff is june 2026 pricing starts at 0 10 per million input tokens and 0 50 per million output tokens for prompts up to 100k tokens which anthropic reports is about 90 lower than claude haiku 4 5 for requests in that range anthropic positions haiku 5 5 for high volume latency sensitive work such as classification extraction routing summarization and subagent tasks and describes it as its fastest model to date at standard speed on visual and agentic evaluations reported at launch it scores 46 4 on chartography a chart reading benchmark compared with 6 4 for haiku 4 5 and 61 6 for sonnet 5 5 and 72 4 on the offline subset of osworld 2 1 a screenshot driven computer use benchmark compared with 15 7 for haiku 4 5 it uses the same tokenizer as claude opus 4 7 and later models so the same text counts as roughly 30 more tokens than on haiku 4 5 vlm mistral mistral large 4 mistral large 4 nicknamed le chonk is a natively multimodal mixture of experts model from mistral that accepts interleaved text and image input and produces text output it uses a granular moe design with roughly 1 05 trillion total parameters and 49 billion active per token reported as 52 billion when embeddings and output layers are counted paired with a 1 6 billion parameter vision encoder and a context window of one million tokens the model is trained from scratch on about 3 800 nvidia grace blackwell gpus in mistral s european data centers and supports more than 160 languages it behaves as a hybrid instruct and reasoning system with a reasoning effort setting that selects between direct answers and longer deliberation alongside function calling and structured output for agentic workflows image understanding is a focus of this generation covering documents charts technical drawings and natural scenes and the model emits bounding box coordinates for visual grounding queries reported grounding results include 42 percent on dense200 and 73 percent on the dior rsvg remote sensing benchmark mistral describes agentic vision workflows in which the model zooms into gigapixel satellite imagery or engineering drawings to verify details and reports coding results such as 62 percent on deepswe figures published at preview time are preliminary because the reinforcement learning phase is still in progress vlm browse all 150 vision ai models from playground to production every model here runs in roboflow workflows chain detection ocr and vlms with logic then deploy to cloud or edge when general models fall short train a model on your own data build a workflow workflow chain 5 blocks inputs object detection model dynamic crop vlm reads the crop outputs get help from roboflow agent found a model you like tell the roboflow agent what you need in plain words it builds the workflow runs it and fixes what breaks on its own it can also read your images and videos then train a custom model when you need one try roboflow agent read the agent docs roboflow agent i need to detect missing labels on bottles coming down a line at 30 fps i need to detect missing labels on bottles coming down a line at 30 fps roboflow playground is a free online tool test and compare 150 vision ai models on your own images it covers 25 tasks such as object detection ocr captioning and segmentation you can browse every model compare models side by side and vote in the arena live elo rankings and vision evals show how each model scores all vision tasks 3d reconstruction captioning chart question answering classification depth estimation document question answering image embedding image similarity image tagging instance segmentation keypoint detection multi label classification object detection ocr open vocabulary object detection phrase grounding pose estimation promptable concept segmentation region proposal semantic segmentation video classification video object tracking vision language visual question answering zero shot segmentation vision ai models compare vision models vision evals rf detr api 2026 roboflow terms