Roboflow

Grok 4.7 vs Qwen3.8 Max

Qwen3.8 Max and Grok 4.7 score 79.3% vs 63.5% averaged across five Vision Evals tasks (#9 and #33 of 65 models), so Qwen3.8 Max leads overall.

  • Qwen3.8 Max wins all 5 tasks, with its widest lead on object detection (76.7% vs 40.4%).
  • Qwen3.8 Max is about half the cost: $0.0080 vs $0.020 overall average cost per sample.
  • Qwen3.8 Max is faster: 18.9s vs 33.3s per sample.

Compare Grok 4.7 vs Qwen3.8 Max live

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GrokGrok 4.7
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QwenQwen3.8 Max
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Grok 4.7 vs Qwen3.8 Max on Vision Evals

Score by taskGrok 4.7Qwen3.8 Max
Object Detection
40.4
76.7
Counting
61.7
81.1
Reasoning
64.2
75.9
OCR
63.6
74.0
Identification
87.5
88.5

Roboflow Vision Evals. Full breakdown below.

Grok 4.7 vs Qwen3.8 Max Comparison Table

Evals updated October 26, 2026Pricing updated November 7, 2026

PropertyGrok 4.7Qwen3.8 Max
OrganizationSpaceXAIQwen
Categoryclosedclosed
Modality—multimodal
Release DateSep 2026Aug 2026
Context Window500K984K
ParametersUnknown2.4T total, ~95B active
LicenseProprietaryApache 2.0
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1M$2.00No published price
Output $/1M$6.00No published price
Vision Tasks
Both support 10 more tasks
Model Features
Foundation VisionNot listedSupported
LLMs with Vision CapabilitiesNot listedSupported
Multimodal VisionNot listedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
63.5%
79.3%
Avg cost / sample$0.020$0.0080
Avg speed / sample33.31s18.95s
By task
Object Detection (low)
40.4%
±0.6, Mean of 3 runs, range 39.8 to 41.0
$0.021
76.7%
±0.3, Mean of 3 runs, range 76.5 to 77.1
$0.012
Object Detection (high)
41.2%
±1.6, Mean of 3 runs, range 39.6 to 42.8
$0.028
78.4%
±0.4, Mean of 3 runs, range 78.1 to 78.9
$0.030
Counting (low)
61.7%
±1.3, Mean of 3 runs, range 60.8 to 63.5
$0.011
81.1%
±2.0, Mean of 3 runs, range 78.4 to 82.4
$0.0046
Counting (high)
60.8%
±1.3, Mean of 3 runs, range 59.5 to 62.2
$0.017
81.1%
±0.0, Mean of 3 runs, range 81.1 to 81.1
$0.0091
Identification (low)
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0063
88.5%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0027
Identification (high)
80.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0.0093
89.6%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0037
OCR (low)
63.6%
$0.023
74.0%
$0.0077
by category
Single value
54.4%
Transcription
89.5%
Structured JSON
86.4%
Text localization
14.8%
Single value
63.9%
Transcription
95.2%
Structured JSON
85.4%
Text localization
59.0%
OCR (high)–
71.8%
$0.028
by category
Single value
61.7%
Transcription
94.8%
Structured JSON
81.6%
Text localization
59.6%
Reasoning (low)
64.2%
±2.3, Mean of 3 runs, range 62.3 to 66.9
$0.015
75.9%
±2.0, Mean of 3 runs, range 73.5 to 77.5
$0.0048
Reasoning (high)
66.9%
±1.3, Mean of 3 runs, range 65.6 to 68.2
$0.024
80.3%
±2.0, Mean of 3 runs, range 78.2 to 82.1
$0.011

Grok 4.7 vs Qwen3.8 Max: Overview

Grok 4.7

Grok 4.7 is a proprietary multimodal reasoning model from SpaceXAI that accepts images alongside text and returns text-only output. On visual inputs it supports image captioning, visual question answering, OCR, document and chart question answering, and image classification and tagging, with all results expressed as generated text rather than bounding boxes or masks. Its 500,000 token context window leaves room for several images, long documents, or extended conversations about visual content in a single request.

The model exposes a configurable reasoning effort setting with low, medium, high, and xhigh levels (high by default), letting callers trade latency for the amount of deliberation spent on a prompt, including multi-step questions about an image. Built on a larger base model than Grok 4.6 with extended reinforcement learning on harder tasks, it works longer on difficult problems and checks its own work more carefully at the same serving speed. SpaceXAI's launch materials focus on coding and agentic knowledge work and report no image benchmark results.

Qwen3.8 Max

Qwen3.8 Max is the flagship tier of Alibaba's Qwen3.8 family, a sparse mixture-of-experts multimodal model with roughly 2.4 trillion total parameters of which about 95 billion activate per token, which keeps serving cost and latency well below what the total parameter count would imply. It builds on the architectural foundation established by Qwen3.5 and accepts text, images, video, and documents as input while producing text output. Reported context handling reaches close to one million tokens, with a maximum generation length of 131,072 tokens, so the model is aimed at long-horizon agentic work such as repository-scale coding, multi-step research, data analysis, and office document workflows.

For vision work the model performs image and video understanding, document and chart interpretation, text recognition inside images, and grounded visual question answering, and Alibaba reports gains concentrated in multimodal and agentic evaluation categories rather than general reasoning. It is the first model in the Max tier of the Qwen line for which the team states weights will be released publicly, alongside a smaller Qwen3.8 27B checkpoint. No training or safety model card has been published.