Compare Vision AI Models Side by Side
The fastest way to compare vision models on the tasks that matter, from object detection and counting to OCR, data extraction, and visual reasoning. Comparisons show specs, inference speed, and cost for frontier VLMs and open computer vision models, with ground-truth accuracy from Roboflow Vision Evals for every benchmarked model.
Evals updated July 10, 2026Pricing updated July 22, 2026
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Add 2-4 models to generate a side-by-side technical evaluation.
Featured Comparisons
The most-searched model matchups, each with the current read from Roboflow Vision Evals.
GPT-5.6 Luna vs GPT-5.4 Mini
Current read: GPT-5.6 Luna leads 4 of 6 Vision Evals tasks (74% vs 65% average), while GPT-5.4 Mini runs cheaper and faster.
See full comparisonGPT-5.6 Terra vs Claude Sonnet 5
Current read: GPT-5.6 Terra leads 3 of 6 Vision Evals tasks (74% vs 66% average), while Claude Sonnet 5 runs cheaper and faster.
See full comparisonGPT-5.6 Sol vs Claude Fable 5
Current read: Claude Fable 5 leads 4 of 6 Vision Evals tasks (80% vs 75% average), while GPT-5.6 Sol runs cheaper.
See full comparisonGemini 2.5 Flash vs Gemma 4 31B
Current read: Both run open prompts and OCR in the live demo; Gemma 4 31B is open-weight while Gemini 2.5 Flash is proprietary.
See full comparisonClaude Opus 4.8 vs Claude Sonnet 4.6
Current read: Both run image captioning and image classification in the live demo; Claude Opus 4.8 is the newer release (May 2026).
See full comparisonClaude Sonnet 4.6 vs Gemma 4 31B
Current read: Both run image captioning and image classification in the live demo; Gemma 4 31B is open-weight while Claude Sonnet 4.6 is proprietary.
See full comparisonHow to Compare Vision Models
Benchmarks alone will not tell you which vision model fits your application. The right way to compare vision models is to test them on the task you actually need, with images that look like yours, and weigh accuracy against latency and cost. Here is the method behind every comparison on this page.
Test on real tasks, not aggregate scores
A model that tops a general multimodal leaderboard can still lose badly on OCR or small-object detection. Roboflow Vision Evals scores current vision language models on six real tasks: object detection, object counting, visual identification, OCR, data extraction, and visual reasoning. Comparisons show per-task results so you can pick the winner for your workload, not the average of workloads you do not have.
The metrics that matter per task
- Object detection: localization accuracy (mAP) on your object classes, especially small and overlapping objects.
- Counting and identification: exact answers, not near misses. A model that says 11 when there are 12 fails the sample.
- OCR and data extraction: character accuracy across fonts and handwriting, and whether structure like tables survives.
- Visual reasoning: factually grounded answers, not fluency. A confident wrong answer is worse than a hedge.
- Every task: latency and cost per sample. A model that is 2 percent more accurate but 8x slower rarely wins in production.
How our scores are generated
Scores come from Vision Evals, Roboflow's ground-truth benchmark: every covered model runs the same real-world samples across all six tasks, and answers are scored against ground truth, using mAP for object detection, text similarity for OCR, and exact-match accuracy for the rest. Token usage, cost, and speed are measured per sample. The Roboflow Vision Arena adds a complementary signal: head-to-head votes on identical images. Scores update as new models ship, and every comparison links to the evaluation behind it.
When a fine-tuned model beats a frontier VLM
Frontier VLMs are the right choice for open-ended visual reasoning and zero-shot tasks. But for a defined production task, detecting your specific defects, products, or parts, a small model fine-tuned on your own data is usually more accurate, faster, and far cheaper per inference. Teams typically prototype with a VLM in this playground, then train RF-DETR on their own dataset and deploy it with Roboflow. The comparison tool above helps you find the best starting point for either path.
Frequently Asked Questions
Pick 2 to 4 models above, or upload your own image in the Playground and run it across every model that supports your task. Compare outputs side by side on tasks like object detection, OCR, and open-ended prompts, then check speed and cost before choosing.
It depends on the task. As of July 2026, Gemini 3.5 Flash leads Roboflow's Vision Evals overall and on object detection, Claude Fable 5 leads on OCR, and Gemini 3.1 Pro leads on visual reasoning. Scores update as new models are added, so check the live Vision Evals leaderboard for the current leader on your task. For a defined production task, a model like RF-DETR fine-tuned on your own data usually outperforms general-purpose models.
Accuracy on your specific task (mAP for object detection, text similarity for OCR, exact-match accuracy for tasks like counting and data extraction), plus speed and cost per sample. Aggregate benchmark scores are a starting point, not a decision.
A vision language model (VLM) reasons about images with natural language and works zero-shot on open-ended tasks. A traditional computer vision model like RF-DETR is trained for a specific task and runs faster, cheaper, and more accurately on that task in production. Most teams use both: VLMs to prototype, fine-tuned models to deploy.
Eval results are updated as new models are added, arena rankings refresh every 15 minutes from live head-to-head votes, and prices sync daily from provider listings. The dates under the page title show the most recent updates.
Roboflow Playground's model comparison tool lets you compare vision AI models side by side: pick two to four of the 100+ hosted models from Google, OpenAI, Anthropic, Meta, Qwen, and others, and see their supported tasks, specs, speed, and cost in one view. Models with a demo can also be run on your own image, so the choice is grounded in how each model handles your data, not just benchmark tables.