Grok 4 vs Qwen3.5 397B A17B
Compare Grok 4 and Qwen3.5 397B A17B side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, and OCR.
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Grok 4 vs Qwen3.5 397B A17B Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Grok 4 | Qwen3.5 397B A17B |
|---|---|---|
| Organization | xAI | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2025 | Feb 2026 |
| Context Window | 256K | 262K |
| Parameters | 397B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.390 | |
| Output $/1M | $2.34 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Grok 4 vs Qwen3.5 397B A17B: Overview
Grok 4, released by xAI on July 9, 2025, is the fourth-generation model in the Grok family and the most advanced to date. It is multimodal, supporting text, vision, tool use, and real-time web search, with a reported 256,000-token context window for long-form reasoning and document analysis. Its training data extends through November 2024, making it the most up-to-date Grok model at launch.
The lineup includes Grok 4 Generalist for broad tasks, Grok 4 Heavy for higher-capacity reasoning, and Grok 4 Code optimized for programming and debugging. A notable feature is its always-on “Think” mode, designed for deeper multi-step reasoning. While xAI has not disclosed parameter counts, Grok 4 is positioned to compete with frontier models like GPT-5 and Claude 4, balancing real-time knowledge via web integration with structured tool use. It is best suited for coding, complex reasoning, and multimodal AI assistants.
Qwen3.5-397B-A17B is a 397B-parameter (17B active) open-weight multimodal model developed by Alibaba’s Qwen team, released on 2026-02-16 under Apache-2.0. It supports text and image inputs with text outputs, combining a sparse Mixture-of-Experts architecture with Gated Delta Networks for efficient scaling. The model provides native vision-language reasoning and a large ~262K token context window, extendable to ~1M tokens.
As the first open-weight release in the Qwen3.5 family, it positions itself as a high-capacity, long-context alternative in the large vision-language space, balancing scale and efficiency via sparse activation. It is designed for advanced reasoning, coding, agent workflows, and multimodal understanding tasks.