Grok 4.6 vs Qwen3.5 9b
Compare Grok 4.6 and Qwen3.5 9b side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.
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Models in this comparison
Grok 4.6 vs Qwen3.5 9b on Vision Evals
Grok 4.6 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Grok 4.6 leads 61.1% to 45.9%.
Overall, Grok 4.6 averages 68.7% (#33 of 61) against 64.4% (#45 of 61) for Qwen3.5 9b.
Qwen3.5 9b is cheaper ($0.0021 vs $0.0097 per sample), while Grok 4.6 is faster (17.5s vs 41.4s per sample).
Grok 4.6 vs Qwen3.5 9b Comparison Table
Evals updated September 29, 2026Pricing updated September 29, 2026
| Property | Grok 4.6 | Qwen3.5 9b |
|---|---|---|
| Organization | SpaceXAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2026 | Mar 2026 |
| Context Window | 500K | 262K |
| Parameters | 9B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.100 |
| Output $/1M | $6.00 | $0.150 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 68.7% | 64.4% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0097 | $0.0021 |
| Avg speed / sample | 17.55s | 41.36s |
| By task | ||
| Object Detection (low) | 23.8% ±2.8, Mean of 3 runs, range 20.2 to 25.9 | 38.1% ±5.7, Mean of 3 runs, range 33.5 to 44.9 |
| 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 | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 |
| 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 | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 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 | 84.2% ±0.9, Mean of 3 runs, range 83.0 to 84.9 |
| 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 | 78.3% ±2.1, Mean of 3 runs, range 76.3 to 80.4 |
| 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 | 45.9% ±1.7, Mean of 3 runs, range 44.4 to 47.7 |
| Reasoning (high) | 63.8% ±2.0, Mean of 3 runs, range 62.3 to 66.2 | – |
Grok 4.6 vs Qwen3.5 9b: 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.
Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family. Designed for efficient multimodal reasoning and long-context language tasks, it notably outperforms the older Qwen3-30B, a model more than three times its size, on key benchmarks including GPQA Diamond, IFEval, and LongBench.
The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.
Frequently Asked Questions
On Roboflow's Vision Evals, Grok 4.6 performed better. It scores higher on 5 of the six vision tasks and averages 68.7% (#33 of 61) against 64.4% (#45 of 61) for Qwen3.5 9b. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Grok 4.6 leads with 61.1% against 45.9%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5 9b is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0021 per sample against $0.0097. Actual costs depend on your image sizes, prompts, and output length.
Grok 4.6 is faster. Across Roboflow's Vision Evals it averaged 17.5s per inference against 41.4s. 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.