Gemini 3.5 Flash-Lite vs SmolVLM2
Compare Gemini 3.5 Flash-Lite and SmolVLM2 side-by-side.
Compare Gemini 3.5 Flash-Lite vs SmolVLM2 live
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Gemini 3.5 Flash-Lite vs SmolVLM2 Comparison Table
Evals updated September 5, 2026Pricing updated September 21, 2026
| Property | Gemini 3.5 Flash-Lite | SmolVLM2 |
|---|---|---|
| Organization | Hugging Face | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Feb 2025 |
| Context Window | 1.0M | — |
| Parameters | 256M – 2.2B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | |
| Output $/1M | $2.50 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Video Classification | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 70.3% | Not evaluated |
| Avg cost / sample | $0.0014 | – |
| Avg speed / sample | 2.70s | – |
| By task | ||
| Object Detection | 57.5% | – |
| Counting | 52.7% | – |
| Identification | 84.4% | – |
| OCR | 87.4% | – |
| Data Extraction | 91.8% | – |
| Reasoning (low) | 48.3% | – |
| Reasoning (high) | 68.9% | – |
Gemini 3.5 Flash-Lite vs SmolVLM2: 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.
SmolVLM2 is a compact multimodal vision-language model developed by the Hugging Face TB Research team, released in February 2025 under the Apache 2.0 license. It is designed for efficient image and video understanding on resource-constrained hardware, with model variants ranging from 256M to 2.2B parameters. SmolVLM2 processes images, multi-image inputs, and video alongside text queries to generate text outputs for tasks including visual question answering, image captioning, and OCR.
SmolVLM2 is designed for on-device and edge deployment, requiring substantially less GPU memory than comparable multimodal models. It supports standard fine-tuning pipelines via the Hugging Face transformers library and quantization through bitsandbytes. SmolVLM2 is suited for applications where a capable vision-language model is needed without full server-scale infrastructure.