Grok 4.6 vs Qwen3.7 Plus
Compare Grok 4.6 and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.
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Models in this comparison
Grok 4.6 vs Qwen3.7 Plus on Vision Evals
Grok 4.6 scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.7 Plus leads 60.1% to 23.8%.
Overall, Grok 4.6 averages 68.7% (#33 of 59) against 67.4% (#35 of 59) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0097 per sample) and faster (7.0s vs 17.5s per sample).
Grok 4.6 vs Qwen3.7 Plus Comparison Table
Evals updated September 29, 2026Pricing updated October 6, 2026
| Property | Grok 4.6 | Qwen3.7 Plus |
|---|---|---|
| Organization | SpaceXAI | Qwen |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Aug 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 6 vision tasks, pooled at low effort | ||
| Overall | 68.7% | 67.4% |
| Avg cost / sample | $0.0097 | $0.0008 |
| Avg speed / sample | 17.55s | 7.01s |
| By task | ||
| Object Detection (low) | 23.8% ±2.8, Mean of 3 runs, range 20.2 to 25.9 | 60.1% |
| Object Detection (high) | 24.0% ±1.0, Mean of 3 runs, range 23.1 to 25.1 | – |
| Counting (low) | 65.8% ±4.1, Mean of 3 runs, range 62.2 to 70.3 | 50.0% |
| Counting (high) | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 | – |
| Identification (low) | 84.4% ±3.1, Mean of 3 runs, range 81.3 to 87.5 | 84.4% |
| Identification (high) | 85.4% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | – |
| OCR (low) | 91.8% ±0.3, Mean of 3 runs, range 91.5 to 92.1 | 86.5% |
| OCR (high) | 91.6% ±0.2, Mean of 3 runs, range 91.4 to 91.7 | – |
| Data Extraction (low) | 85.6% ±1.0, Mean of 3 runs, range 84.5 to 86.6 | 83.5% |
| Data Extraction (high) | 85.6% ±1.0, Mean of 3 runs, range 84.5 to 86.6 | – |
| Reasoning (low) | 61.1% ±1.3, Mean of 3 runs, range 59.6 to 62.3 | 39.7% |
| Reasoning (high) | 63.8% ±2.0, Mean of 3 runs, range 62.3 to 66.2 | 68.2% |
Grok 4.6 vs Qwen3.7 Plus: Overview
Grok 4.6 is a proprietary reasoning model from xAI aimed at long-running agentic workflows, coding, and knowledge work. It accepts text and image input and returns text, with a 500,000 token context window and a knowledge cutoff of February 1, 2026. The model exposes an adjustable reasoning budget with low, medium, high, and xhigh settings, where high is the default, and it supports function calling, structured outputs, web and X search, and code execution as documented tool behaviors. Its visual capability covers interpreting images supplied alongside text prompts, which places it in the visual question answering and document understanding family, and it can also return object detection boxes as text coordinates when prompted.
xAI characterizes Grok 4.6 as the result of an extended post-training run over the Grok 4.5 lineage rather than a new pretrained base. The described recipe combines curated model-generated reasoning and technical data, engineering data, a revised optimizer, regenerated supervised fine-tuning trajectories, and reinforcement learning across agent environments spanning knowledge work, coding, kernel optimization, web development, and computer-aided design. Parameter count and architecture specifics are not disclosed. Independent measurement from Artificial Analysis places the model at 61 on its Intelligence Index, five points above Grok 4.5.
Frequently Asked Questions
On Roboflow's Vision Evals, Grok 4.6 performed better. It scores higher on 4 of the six vision tasks and averages 68.7% (#33 of 59) against 67.4% (#35 of 59) for Qwen3.7 Plus. 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 23.8%. 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 $0.0097. Grok 4.6 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 7.0s per inference against 17.5s. 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.