Mistral Large 4 vs Qwen3.7 Plus
Compare Mistral Large 4 and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.
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Models in this comparison
Mistral Large 4 vs Qwen3.7 Plus on Vision Evals
Mistral Large 4 scores higher on 3 of the six Vision Evals tasks.
The widest gap is OCR, where Mistral Large 4 leads 92.7% to 86.5%.
Overall, Mistral Large 4 averages 68.5% (#36 of 61) against 67.4% (#37 of 61) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0018 per sample) and faster (7.0s vs 8.8s per sample).
Mistral Large 4 vs Qwen3.7 Plus Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | Mistral Large 4 | Qwen3.7 Plus |
|---|---|---|
| Organization | Mistral | Qwen |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Oct 2026 | Jun 2026 |
| Context Window | 1.0M | — |
| Parameters | 1.05T total, 49B active | Unknown |
| License | Custom | Unknown |
| Pricing per 1M tokens | ||
| Input $/1M | $0.680 | $0.320 |
| Output $/1M | $2.09 | $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 |
| Phrase Grounding | 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 | 68.5% | 67.4% |
| Avg cost / sample | $0.0018 | $0.0008 |
| Avg speed / sample | 8.78s | 7.01s |
| By task | ||
| Object Detection (low) | 59.3% ±0.7, Mean of 3 runs, range 58.5 to 60.0 | 60.1% |
| 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 | 50.0% |
| 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 | 84.4% |
| 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 | 86.5% |
| 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 | 83.5% |
| 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 | 39.7% |
| Reasoning (high) | 57.6% ±2.0, Mean of 3 runs, range 55.6 to 59.6 | 68.2% |
Mistral Large 4 vs Qwen3.7 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.
Frequently Asked Questions
On Roboflow's Vision Evals, Mistral Large 4 performed slightly better overall. The two split the six vision tasks 3 to 3, but Mistral Large 4 averages 68.5% (#36 of 61) against 67.4% (#37 of 61) for Qwen3.7 Plus. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals OCR benchmark at low effort, Mistral Large 4 leads with 92.7% against 86.5%. 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 $0.0018. Mistral Large 4 is priced at $0.68 per 1M input tokens and $2.09 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 8.8s. 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 OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.