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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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MistralMistral Large 4
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QwenQwen3.7 Plus
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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 4Qwen3.7 Plus

Mistral Large 4 vs Qwen3.7 Plus Comparison Table

Evals updated October 8, 2026Pricing updated October 8, 2026

PropertyMistral Large 4Qwen3.7 Plus
OrganizationMistralQwen
Categoryopenclosed
Modalitymultimodal—
Release DateOct 2026Jun 2026
Context Window1.0M—
Parameters1.05T total, 49B activeUnknown
LicenseCustomUnknown
Pricing per 1M tokens
Input $/1M$0.680$0.320
Output $/1M$2.09$1.28
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Visual Question AnsweringDemoDemo
Chart Question AnsweringSupportedNot listed
Document Question AnsweringSupportedNot listed
Image TaggingSupportedNot listed
Multi-Label ClassificationSupportedNot listed
Phrase GroundingSupportedNot listed
Vision LanguageSupportedNot listed
Model Features
Foundation VisionSupportedNot listed
LLMs with Vision CapabilitiesSupportedNot listed
Multimodal VisionSupportedNot 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 / sample8.78s7.01s
By task
Object Detection (low)
59.3%
±0.7, Mean of 3 runs, range 58.5 to 60.0
$0.0028
60.1%
$0.0013
Object Detection (high)
50.2%
±2.5, Mean of 3 runs, range 48.0 to 53.0
$0.023
–
Counting (low)
54.5%
±0.7, Mean of 3 runs, range 54.0 to 55.4
$0.0010
50.0%
$0.0004
Counting (high)
63.1%
±2.0, Mean of 3 runs, range 60.8 to 64.9
$0.0094
–
Identification (low)
85.4%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0009
84.4%
$0.0003
Identification (high)
85.4%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.0044
–
OCR (low)
92.7%
±0.8, Mean of 3 runs, range 91.8 to 93.3
$0.0016
86.5%
$0.0009
OCR (high)
87.1%
±4.4, Mean of 3 runs, range 81.6 to 90.4
$0.025
–
Data Extraction (low)
80.1%
±1.0, Mean of 3 runs, range 79.4 to 81.4
$0.0010
83.5%
$0.0004
Data Extraction (high)
82.5%
±1.0, Mean of 3 runs, range 81.4 to 83.5
$0.0033
–
Reasoning (low)
38.9%
±0.3, Mean of 3 runs, range 38.4 to 39.1
$0.0013
39.7%
$0.0003
Reasoning (high)
57.6%
±2.0, Mean of 3 runs, range 55.6 to 59.6
$0.013
68.2%
$0.0043

Mistral Large 4 vs Qwen3.7 Plus: Overview

Mistral Large 4

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.

Qwen3.7 Plus
No description available

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.