Grok 4.5 vs Qwen3.7 Plus
Compare Grok 4.5 and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.
Compare Grok 4.5 vs Qwen3.7 Plus live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Grok 4.5 vs Qwen3.7 Plus on Vision Evals
Qwen3.7 Plus scores higher on 3 of the five Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.7 Plus leads 60.1% to 19.0%.
Overall, Grok 4.5 averages 55.3% (#44 of 61) against 58.9% (#35 of 61) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0078 per sample) and faster (7.8s vs 16.4s per sample).
Grok 4.5 vs Qwen3.7 Plus Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | Grok 4.5 | Qwen3.7 Plus |
|---|---|---|
| Organization | SpaceXAI | Qwen |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Jul 2026 | Jun 2026 |
| Context Window | 500K | — |
| Parameters | Unknown | Unknown |
| License | Proprietary | Unknown |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.320 |
| Output $/1M | $6.00 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| Vision Language | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 55.3% | 58.9% |
| Avg cost / sample | $0.0078 | $0.0008 |
| Avg speed / sample | 16.41s | 7.77s |
| By task | ||
| Object Detection (low) | 19.0% ±0.8, Mean of 3 runs, range 18.0 to 19.6 | 60.1% |
| Object Detection (high) | 17.8% ±0.3, Mean of 3 runs, range 17.5 to 18.0 | – |
| Counting (low) | 59.5% ±3.4, Mean of 3 runs, range 55.4 to 62.2 | 50.0% |
| Counting (high) | 57.7% ±3.4, Mean of 3 runs, range 54.0 to 60.8 | – |
| Identification (low) | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 84.4% |
| Identification (high) | 85.4% ±3.1, Mean of 3 runs, range 81.3 to 87.5 | – |
| OCR (low) | 57.1% | 60.3% |
| by category |
|
|
| OCR (high) | 57.5% | 65.5% |
| by category |
|
|
| Reasoning (low) | 57.6% ±1.7, Mean of 3 runs, range 55.6 to 58.9 | 39.7% |
| Reasoning (high) | 59.8% ±2.6, Mean of 3 runs, range 57.0 to 62.3 | 68.2% |
Grok 4.5 vs Qwen3.7 Plus: Overview
Grok 4.5 is a proprietary reasoning model from SpaceXAI (xAI) that accepts interleaved text and image input and returns text, with a 500,000 token context window. xAI positions it as a model for coding, agentic software work, and knowledge tasks, and states it was trained in the company's Memphis data centers on datasets spanning science, engineering, and mathematics. Its reinforcement learning stage covers hundreds of thousands of multi step software engineering tasks scored by automated checks and model based grading, and training is reported to have run on tens of thousands of NVIDIA GB300 GPUs using an asynchronous scheme in which multi hour agentic rollouts continue while learning proceeds in parallel, targeting long horizon autonomous operation rather than single turn inference.
For vision, the model consumes JPEG and PNG images in any order relative to text prompts, covering visual question answering, description of chart and document imagery, and reading text rendered inside a scene. Reasoning effort is configurable, and the model supports function calling and structured outputs, so image inputs can be interleaved with tool calls inside agent loops. xAI has not published a technical report, architecture details, or parameter count, and reported mixture of experts sizing figures come from secondary coverage rather than official documentation.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.7 Plus performed better. It scores higher on 3 of the five vision tasks and averages 58.9% (#35 of 61) against 55.3% (#44 of 61) for Grok 4.5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Qwen3.7 Plus leads with 60.1% against 19.0%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.7 Plus is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.0078. Grok 4.5 is priced at $2.00 per 1M input tokens and $6.00 per 1M output; Qwen3.7 Plus is priced at $0.32 per 1M input tokens and $1.28 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.8s per inference against 16.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.