Roboflow

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.

Open Object Detection in the full playground
GrokGrok 4.5
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QwenQwen3.7 Plus
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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 4.5Qwen3.7 Plus

Grok 4.5 vs Qwen3.7 Plus Comparison Table

Evals updated October 8, 2026Pricing updated October 8, 2026

PropertyGrok 4.5Qwen3.7 Plus
OrganizationSpaceXAIQwen
Categoryclosedclosed
Modalitymultimodal—
Release DateJul 2026Jun 2026
Context Window500K—
ParametersUnknownUnknown
LicenseProprietaryUnknown
Pricing per 1M tokens
Input $/1M$2.00$0.320
Output $/1M$6.00$1.28
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Visual Question AnsweringDemoDemo
Chart Question AnsweringSupportedNot listed
Document Question AnsweringSupportedNot listed
Image TaggingSupportedNot listed
Multi-Label ClassificationSupportedNot listed
Vision LanguageSupportedNot listed
Model Features
Foundation VisionSupportedNot listed
LLMs with Vision CapabilitiesSupportedNot listed
Multimodal VisionSupportedNot 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 / sample16.41s7.77s
By task
Object Detection (low)
19.0%
±0.8, Mean of 3 runs, range 18.0 to 19.6
$0.011
60.1%
$0.0013
Object Detection (high)
17.8%
±0.3, Mean of 3 runs, range 17.5 to 18.0
$0.020
–
Counting (low)
59.5%
±3.4, Mean of 3 runs, range 55.4 to 62.2
$0.0065
50.0%
$0.0004
Counting (high)
57.7%
±3.4, Mean of 3 runs, range 54.0 to 60.8
$0.012
–
Identification (low)
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0046
84.4%
$0.0003
Identification (high)
85.4%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0067
–
OCR (low)
57.1%
$0.0065
60.3%
$0.0009
by category
Single value
47.8%
Transcription
85.4%
Structured JSON
80.3%
Text localization
6.1%
Single value
53.5%
Transcription
86.7%
Structured JSON
75.8%
Text localization
23.1%
OCR (high)
57.5%
$0.013
65.5%
$0.0042
by category
Single value
47.8%
Transcription
86.1%
Structured JSON
80.9%
Text localization
7.0%
Single value
58.3%
Transcription
89.7%
Structured JSON
81.3%
Text localization
30.4%
Reasoning (low)
57.6%
±1.7, Mean of 3 runs, range 55.6 to 58.9
$0.0082
39.7%
$0.0003
Reasoning (high)
59.8%
±2.6, Mean of 3 runs, range 57.0 to 62.3
$0.019
68.2%
$0.0043

Grok 4.5 vs Qwen3.7 Plus: Overview

Grok 4.5

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.

Qwen3.7 Plus
No description available

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.