Mistral Large 4 vs Qwen3.6 Plus
Compare Mistral Large 4 and Qwen3.6 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Mistral Large 4 vs Qwen3.6 Plus Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | Mistral Large 4 | Qwen3.6 Plus |
|---|---|---|
| Organization | Mistral | Qwen |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Apr 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | 1.05T total, 49B active | Unknown |
| License | Custom | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.680 | $0.325 |
| Output $/1M | $2.09 | $1.95 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Supported |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Supported |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Phrase Grounding | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 68.5% | Not evaluated |
| Avg cost / sample | $0.0018 | – |
| Avg speed / sample | 8.78s | – |
| By task | ||
| Object Detection (low) | 59.3% ±0.7, Mean of 3 runs, range 58.5 to 60.0 | – |
| Object Detection (high) | 50.2% ±2.5, Mean of 3 runs, range 48.0 to 53.0 | – |
| Counting (low) | 54.5% ±0.7, Mean of 3 runs, range 54.0 to 55.4 | – |
| Counting (high) | 63.1% ±2.0, Mean of 3 runs, range 60.8 to 64.9 | – |
| Identification (low) | 85.4% ±3.1, Mean of 3 runs, range 81.3 to 87.5 | – |
| Identification (high) | 85.4% ±1.6, Mean of 3 runs, range 84.4 to 87.5 | – |
| OCR (low) | 92.7% ±0.8, Mean of 3 runs, range 91.8 to 93.3 | – |
| OCR (high) | 87.1% ±4.4, Mean of 3 runs, range 81.6 to 90.4 | – |
| Data Extraction (low) | 80.1% ±1.0, Mean of 3 runs, range 79.4 to 81.4 | – |
| Data Extraction (high) | 82.5% ±1.0, Mean of 3 runs, range 81.4 to 83.5 | – |
| Reasoning (low) | 38.9% ±0.3, Mean of 3 runs, range 38.4 to 39.1 | – |
| Reasoning (high) | 57.6% ±2.0, Mean of 3 runs, range 55.6 to 59.6 | – |
Mistral Large 4 vs Qwen3.6 Plus: Overview
Mistral Large 4, nicknamed Le Chonk, is a natively multimodal mixture-of-experts model from Mistral that accepts interleaved text and image input and produces text output. It uses a granular MoE design with roughly 1.05 trillion total parameters and 49 billion active per token, reported as 52 billion when embeddings and output layers are counted, paired with a 1.6 billion parameter vision encoder and a context window of one million tokens. The model is trained from scratch on about 3,800 NVIDIA Grace Blackwell GPUs in Mistral's European data centers and supports more than 160 languages. It behaves as a hybrid instruct and reasoning system, with a reasoning effort setting that selects between direct answers and longer deliberation, alongside function calling and structured output for agentic workflows.
Image understanding is a focus of this generation, covering documents, charts, technical drawings and natural scenes, and the model emits bounding box coordinates for visual grounding queries. Reported grounding results include 42 percent on Dense200 and 73 percent on the DIOR-RSVG remote sensing benchmark. Mistral describes agentic vision workflows in which the model zooms into gigapixel satellite imagery or engineering drawings to verify details, and reports coding results such as 62 percent on DeepSWE. Figures published at preview time are preliminary because the reinforcement learning phase is still in progress.
Qwen3.6 Plus is a flagship model in Alibaba’s Qwen Plus series, designed for agentic workflows, coding, and multi-step reasoning. It supports a 1 million token context window and up to 65,536 output tokens, with built-in reasoning capabilities. The model is available as a hosted, proprietary API through Alibaba Cloud.
Compared to Qwen3.5, it improves reliability in multi-step execution and frontend code generation, with stronger performance on agentic coding tasks. It also supports document and image understanding, though its vision capabilities are more limited than dedicated Qwen-VL models. Qwen3.6 Plus is part of a broader Qwen ecosystem that includes both closed-source APIs and open-weight models.
Frequently Asked Questions
Qwen3.6 Plus has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Mistral Large 4 is released under Custom, while Qwen3.6 Plus uses Proprietary. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.