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Gemini 3 Flash vs Kimi K3

Compare Gemini 3 Flash and Kimi K3 side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.

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GoogleGemini 3 Flash
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MoonshotAIKimi K3
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Models in this comparison

MoonshotAI

Gemini 3 Flash vs Kimi K3 on Vision Evals

Gemini 3 Flash scores higher on 4 of the six Vision Evals tasks.

The widest gap is Reasoning, where Gemini 3 Flash leads 64.9% to 42.4%.

Overall, Gemini 3 Flash averages 74.9% (#10 of 25) against 66.5% (#17 of 25) for Kimi K3.

Gemini 3 Flash is both cheaper ($0.0021 vs $0.011 per sample) and faster (4.1s vs 12.7s per sample).

Gemini 3 FlashKimi K3

Gemini 3 Flash vs Kimi K3 Comparison Table

Evals updated August 6, 2026Pricing updated August 11, 2026

PropertyGemini 3 FlashKimi K3
OrganizationGoogleMoonshot AI
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateDec 2025Jul 2026
Context Window1.0M1.0M
Parameters2.8T
LicenseProprietaryModified MIT
Pricing per 1M tokens
Input $/1M$0.500$3.00
Output $/1M$3.00$15.00
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
74.9%
66.5%
Avg cost / sample$0.0021$0.011
Avg speed / sample4.10s12.71s
By task
Object Detection
38.6%
$0.0031
51.9%
$0.020
Counting
67.6%
$0.0012
46.0%
$0.0046
Identification
93.8%
$0.0009
81.3%
$0.0041
OCR
87.6%
$0.0024
93.0%
$0.0094
Data Extraction
96.9%
$0.0008
84.5%
$0.0046
Reasoning (low)
64.9%
$0.0020
42.4%
$0.0044
Reasoning (high)
74.2%
$0.0040
74.2%
$0.037

Gemini 3 Flash vs Kimi K3: Overview

Gemini 3 Flash

Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.

The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.

Kimi K3

Kimi K3 is a sparse Mixture-of-Experts large language model developed by Moonshot AI, with 2.8 trillion total parameters and a 1-million-token context window. The model activates 16 out of 896 experts per token using the Stable LatentMoE framework, and is built on two architectural innovations: Kimi Delta Attention (KDA), a hybrid linear attention mechanism that enables up to 6.3x faster decoding in long-context settings, and Attention Residuals (AttnRes), which selectively retrieves representations across model depth and delivers roughly 25% higher training efficiency. Together with refined training and data recipes, these structural advances yield approximately 2.5x better overall scaling efficiency compared to its predecessor Kimi K2. The model applies quantization-aware training from the supervised fine-tuning stage onward, using MXFP4 weights with MXFP8 activations for hardware compatibility. Thinking mode is always enabled at launch, with reasoning effort configurable via the reasoning_effort field.

Kimi K3 supports native visual understanding alongside text, accepting image inputs for tasks that combine software engineering and visual reasoning. It targets long-horizon coding, knowledge work, and agentic workflows, and ships in two variants: K3 Max for general chat and agent tasks, and K3 Swarm Max for large-scale parallel processing across many coordinated sub-agents. The model is compatible with the OpenAI SDK via an OpenAI-compatible API. Full model weights are scheduled for release by July 27, 2026 under a Modified MIT license, following the open-weight pattern established by the Kimi K2 model family. A technical report with full architecture, training, and evaluation details is expected to accompany the weights release.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3 Flash performed better. It scores higher on 4 of the six vision tasks and averages 74.9% (#10 of 25) against 66.5% (#17 of 25) for Kimi K3. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals Reasoning benchmark at low effort, Gemini 3 Flash leads with 64.9% against 42.4%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0021 per sample against $0.011. Gemini 3 Flash is priced at $0.50 per 1M input tokens and $3.00 per 1M output; Kimi K3 is priced at $3.00 per 1M input tokens and $15.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Gemini 3 Flash is faster. Across Roboflow's Vision Evals it averaged 4.1s per inference against 12.7s. 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 object detection and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.