Gemini 2.5 Flash-Lite vs Qwen3.5 397B A17B
Compare Gemini 2.5 Flash-Lite and Qwen3.5 397B A17B side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Gemini 2.5 Flash-Lite vs Qwen3.5 397B A17B: Overview
Gemini 2.5 Flash-Lite, released for general availability on July 22, 2025, is the most cost-efficient model in the Gemini 2.5 family, designed for high-volume and latency-sensitive tasks. It is multimodal, supporting text, images, video, audio, and PDFs as inputs, with text as its primary output. The model handles up to 1 million input tokens and generates outputs up to 64K tokens, making it suitable for large-scale document or media processing at low cost. It is built on a Sparse Mixture-of-Experts architecture with native multimodal support, though exact parameter counts are undisclosed.
Flash-Lite offers the lowest usage cost among Gemini 2.5 models. It introduces developer controls for “thinking mode,” allowing fine-tuning of reasoning depth vs. efficiency. It also integrates native tools such as code execution, search grounding, and URL context. While strong on translation, classification, coding, and general multimodal reasoning, it lacks support for image or audio generation in its stable release and is less capable than Gemini 2.5 Flash or Pro on complex reasoning-heavy workflows.
Qwen3.5-397B-A17B is a 397B-parameter (17B active) open-weight multimodal model developed by Alibaba’s Qwen team, released on 2026-02-16 under Apache-2.0. It supports text and image inputs with text outputs, combining a sparse Mixture-of-Experts architecture with Gated Delta Networks for efficient scaling. The model provides native vision-language reasoning and a large ~262K token context window, extendable to ~1M tokens.
As the first open-weight release in the Qwen3.5 family, it positions itself as a high-capacity, long-context alternative in the large vision-language space, balancing scale and efficiency via sparse activation. It is designed for advanced reasoning, coding, agent workflows, and multimodal understanding tasks.
Gemini 2.5 Flash-Lite vs Qwen3.5 397B A17B Comparison Table
| Property | Gemini 2.5 Flash-Lite | Qwen3.5 397B A17B |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2025 | Feb 2026 |
| Context Window | 1.0M | 262K |
| Parameters | 397B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $0.385 |
| Output $/1M | $0.400 | $2.45 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 53.73% | 58.21% |
| Avg Response Time | 7.19s | 56.61s |
| Median input tokensincl. image tokens | 294 | 1.1K |
| Median output tokens | 6 | 54 |
| Est. cost / taskon this benchmark | $0.0000 | $0.0006 |
| Defect Detection | 66.7%(10/15) | 66.7%(10/15) |
| Document Understanding | 66.7%(6/9) | 77.8%(7/9) |
| Object Counting | 10%(1/10) | 20%(2/10) |
| Object Understanding | 71.4%(10/14) | 64.3%(9/14) |
| Spatial Understanding | 47.4%(9/19) | 57.9%(11/19) |
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