Gemini 3.5 Flash-Lite vs Gemini 3 Flash
Compare Gemini 3.5 Flash-Lite and Gemini 3 Flash side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, Object Detection, and OCR.
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Models in this comparison
Gemini 3.5 Flash-Lite vs Gemini 3 Flash on Vision Evals
Gemini 3 Flash scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Gemini 3.5 Flash-Lite leads 57.5% to 38.6%.
Overall, Gemini 3.5 Flash-Lite averages 69.6% (#14 of 25) against 74.9% (#10 of 25) for Gemini 3 Flash.
Gemini 3.5 Flash-Lite is both cheaper ($0.0014 vs $0.0021 per sample) and faster (2.7s vs 4.1s per sample).
Gemini 3.5 Flash-Lite vs Gemini 3 Flash Comparison Table
Evals updated August 6, 2026Pricing updated August 11, 2026
| Property | Gemini 3.5 Flash-Lite | Gemini 3 Flash |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Dec 2025 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | $0.500 |
| Output $/1M | $2.50 | $3.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Video Classification | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 69.6% | 74.9% |
| Avg cost / sample | $0.0014 | $0.0021 |
| Avg speed / sample | 2.70s | 4.10s |
| By task | ||
| Object Detection | 57.5% $0.0023 | 38.6% $0.0031 |
| Counting | 52.7% $0.0007 | 67.6% $0.0012 |
| Identification | 81.3% $0.0004 | 93.8% $0.0009 |
| OCR | 87.4% $0.0011 | 87.6% $0.0024 |
| Data Extraction | 90.7% $0.0004 | 96.9% $0.0008 |
| Reasoning (low) | 48.3% $0.0012 | 64.9% $0.0020 |
| Reasoning (high) | 68.9% $0.0042 | 74.2% $0.0040 |
Gemini 3.5 Flash-Lite vs Gemini 3 Flash: Overview
Gemini 3.5 Flash-Lite is a natively multimodal reasoning model developed by Google DeepMind, released on July 21, 2026 as part of the Gemini 3.5 model family. It is the fastest model in the 3.5 series, designed for both low-latency tasks and high-throughput production workloads such as agentic search, document processing, receipt translation, and large-scale data extraction. The model accepts text, images, audio, and video as inputs, with a context window of up to 1 million tokens, and produces text output. It supports configurable thinking levels, allowing developers to tune the balance between response quality, cost, and latency depending on workload requirements.
On agentic and coding benchmarks, Gemini 3.5 Flash-Lite significantly outperforms its predecessor, Gemini 3.1 Flash-Lite, including on Terminal-Bench 2.1 (54% vs. 31%), GDM-MRCR v2 long-context (72.2% vs. 60.1%), and real-world task execution as measured by GDPval-AA v2 (1140 vs. 642). It also surpasses Gemini 3 Flash on SWE-Bench Pro (54.2% vs. 49.6%) and OSWorld-Verified (74.0% vs. 65.1%). According to the Artificial Analysis Index, the model generates output at approximately 350 tokens per second. It is built on the Gemini 3.5 Flash foundation and is evaluated across reasoning, coding, multimodal understanding, multilingual performance, and long-context tasks. The model is developed under Google's Frontier Safety Framework.
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.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3 Flash performed better. It scores higher on 5 of the six vision tasks and averages 74.9% (#10 of 25) against 69.6% (#14 of 25) for Gemini 3.5 Flash-Lite. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Object Detection benchmark, Gemini 3.5 Flash-Lite leads with 57.5% against 38.6%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.5 Flash-Lite is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0014 per sample against $0.0021. Gemini 3.5 Flash-Lite is priced at $0.30 per 1M input tokens and $2.50 per 1M output; Gemini 3 Flash is priced at $0.50 per 1M input tokens and $3.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3.5 Flash-Lite is faster. Across Roboflow's Vision Evals it averaged 2.7s per inference against 4.1s. 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 open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.