Gemini 3.5 Flash vs Mistral Medium 3.1

Compare Gemini 3.5 Flash and Mistral Medium 3.1 side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, and OCR.

Compare Gemini 3.5 Flash vs Mistral Medium 3.1 live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Extract and compare text from images across multiple models.

Open OCR in the full playground
GoogleGemini 3.5 Flash
Run to compare this model.
MistralMistral Medium 3.1
Run to compare this model.

Models in this comparison

Gemini 3.5 Flash vs Mistral Medium 3.1: Overview

Gemini 3.5 Flash

Gemini 3.5 Flash is a multimodal language model developed by Google DeepMind and released at Google I/O 2026. It is built on the Gemini 3 Flash reasoning foundation and introduces configurable thinking levels (minimal, low, medium, and high) that allow developers to tune the depth of internal reasoning before a response is generated. The model accepts text, image, video, audio, and PDF inputs and produces text output, with a 1 million token context window and up to 65,000 output tokens per request. It is natively multimodal, processing visual inputs alongside text to support tasks such as image captioning, classification, optical character recognition, object detection, and visual grounding, where the model references specific regions within an image or video frame.

Its vision capabilities extend to interpreting UI screenshots, diagrams, charts, and real-world scenes, as well as understanding video and live frame sequences for activity and scene recognition. The model supports combined tool use, including Google Search, URL context, code execution, and custom functions, within a single request, and it uses reasoning context from previous turns when thought signatures are present in the conversation history, enabling persistent multi-turn reasoning chains. Gemini 3.5 Flash carries a knowledge cutoff of January 2026 and is available via the Gemini API, Google AI Studio, Google Antigravity, and the Gemini Enterprise Agent Platform.

Mistral Medium 3.1

Mistral Medium 3.1, released in August 2025 as the mistral-medium-2508 update, is a proprietary frontier model from Mistral AI positioned between smaller open models and high-end closed LLMs. It is multimodal, handling both text and image inputs, with a context window of ~128K tokens.

Compared to Mistral Medium 3.0, the 3.1 release introduces improvements in reasoning, coding, STEM, and enterprise workflows, along with better tone control for conversational and business applications. It is designed for scalable enterprise deployments, including hybrid cloud and on-premises VPC setups. As part of Mistral’s Premier line, Medium 3.1 is a commercial-only offering: while it delivers strong accuracy and performance, trade-offs include higher costs than open-weight models, restricted fine-tuning access, and increased latency/cost for very large contexts.

Gemini 3.5 Flash vs Mistral Medium 3.1 Comparison Table

PropertyGemini 3.5 FlashMistral Medium 3.1
OrganizationGoogleMistral
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2026Aug 2025
Context Window1.0M128K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.50$0.400
Output $/1M$9.00$2.00
Vision Tasks
CaptioningDemoDemo
OCRDemoDemo
Visual Question AnsweringDemoDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Multi-Label Classification
Object DetectionDemo
Vision Language
Model Features
Multimodal Vision
LLMs with Vision Capabilities
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
79.1%
Avg Response Time6.71s
Median input tokensincl. image tokens1.1K
Median output tokens294
Est. cost / taskon this benchmark$0.0043
Defect Detection
80%(12/15)
Document Understanding
77.8%(7/9)
Object Counting
60%(6/10)
Object Understanding
92.9%(13/14)
Spatial Understanding
78.9%(15/19)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology