Gemini 3 Flash vs Qwen3.8 27B
Compare Gemini 3 Flash and Qwen3.8 27B side-by-side.
Compare Gemini 3 Flash vs Qwen3.8 27B live
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Models in this comparison
Gemini 3 Flash vs Qwen3.8 27B on Vision Evals
Gemini 3 Flash scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Gemini 3 Flash leads 64.9% to 31.8%.
Overall, Gemini 3 Flash averages 74.9% (#11 of 30) against 61.2% (#29 of 30) for Qwen3.8 27B.
Qwen3.8 27B is cheaper ($0.0018 vs $0.0021 per sample), while Gemini 3 Flash is faster (4.1s vs 7.3s per sample).
Gemini 3 Flash vs Qwen3.8 27B Comparison Table
Evals updated August 14, 2026Pricing updated August 15, 2026
| Property | Gemini 3 Flash | Qwen3.8 27B |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | Aug 2026 |
| Context Window | 1.0M | 262K |
| Parameters | 27.78B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.500 | $0.450 |
| Output $/1M | $3.00 | $3.20 |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Object Detection | Demo | |
| 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% | 61.2% |
| Avg cost / sample | $0.0021 | $0.0018 |
| Avg speed / sample | 4.10s | 7.33s |
| By task | ||
| Object Detection | 38.6% $0.0031 | 54.5% $0.0036 |
| Counting | 67.6% $0.0012 | 41.9% $0.0005 |
| Identification | 93.8% $0.0009 | 78.1% $0.0005 |
| OCR | 87.6% $0.0024 | 81.4% $0.0019 |
| Data Extraction | 96.9% $0.0008 | 79.4% $0.0005 |
| Reasoning (low) | 64.9% $0.0020 | 31.8% $0.0005 |
| Reasoning (high) | 74.2% $0.0040 | 62.3% $0.0087 |
Gemini 3 Flash vs Qwen3.8 27B: Overview
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.
Qwen3.8-27B is a dense vision-language model of roughly 27.78 billion parameters from Alibaba's Qwen team, built on the architectural foundation established by the Qwen3.5 generation. It takes interleaved text, image, and video input through an integrated vision encoder and produces text, covering document and chart understanding, visual question answering, front-end code generation from visual references, and agentic computer-use workflows. The decoder is a hybrid stack of 64 layers that repeats a pattern of three Gated DeltaNet linear-attention blocks, each followed by a feed-forward block, then one grouped-query full-attention block, so only a quarter of the layers maintain a conventional key-value cache. Native context length is 262,144 tokens.
Post-training targets long-horizon agentic work, and the model exposes per-request thinking control that lets callers switch reasoning traces on or off and choose a reasoning effort level. Multi-token prediction weights are trained alongside the main network for speculative decoding. Qwen reports improvements over Qwen3.6-27B on agentic and multimodal evaluations including Terminal-Bench 2.1, OSWorld-Verified, and SWE-bench Multimodal, with additional results on MathVision, CharXiv, and an internal Vision2Web suite.
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% (#11 of 30) against 61.2% (#29 of 30) for Qwen3.8 27B. 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 31.8%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.8 27B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0018 per sample against $0.0021. Gemini 3 Flash is priced at $0.50 per 1M input tokens and $3.00 per 1M output; Qwen3.8 27B is priced at $0.45 per 1M input tokens and $3.20 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 7.3s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.