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.
Roboflow Vision Evals. Full breakdown below.
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Grok 4.7 vs Qwen3.8 Max on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Grok 4.7 vs Qwen3.8 Max Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | Grok 4.7 | Qwen3.8 Max |
|---|---|---|
| Organization | SpaceXAI | Qwen |
| Category | closed | closed |
| Modality | — | multimodal |
| Release Date | Sep 2026 | Aug 2026 |
| Context Window | 500K | 984K |
| Parameters | Unknown | 2.4T total, ~95B active |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | No published price |
| Output $/1M | $6.00 | No published price |
| Vision Tasks | ||
| Model Features | ||
| Foundation Vision | Not listed | Supported |
| LLMs with Vision Capabilities | Not listed | Supported |
| Multimodal Vision | Not listed | Supported |
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 / sample | 33.31s | 18.95s |
| By task | ||
| Object Detection (low) | 40.4% ±0.6, Mean of 3 runs, range 39.8 to 41.0 | 76.7% ±0.3, Mean of 3 runs, range 76.5 to 77.1 |
| Object Detection (high) | 41.2% ±1.6, Mean of 3 runs, range 39.6 to 42.8 | 78.4% ±0.4, Mean of 3 runs, range 78.1 to 78.9 |
| Counting (low) | 61.7% ±1.3, Mean of 3 runs, range 60.8 to 63.5 | 81.1% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | 60.8% ±1.3, Mean of 3 runs, range 59.5 to 62.2 | 81.1% ±0.0, Mean of 3 runs, range 81.1 to 81.1 |
| Identification (low) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 63.6% | 74.0% |
| by category |
|
|
| OCR (high) | – | 71.8% |
| by category |
| |
| Reasoning (low) | 64.2% ±2.3, Mean of 3 runs, range 62.3 to 66.9 | 75.9% ±2.0, Mean of 3 runs, range 73.5 to 77.5 |
| Reasoning (high) | 66.9% ±1.3, Mean of 3 runs, range 65.6 to 68.2 | 80.3% ±2.0, Mean of 3 runs, range 78.2 to 82.1 |
Grok 4.7 vs Qwen3.8 Max: Overview
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 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.