Gemini 3.5 Flash vs Gemini 3.6 Flash
Compare Gemini 3.5 Flash and Gemini 3.6 Flash side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, OCR, Classification, and Object Detection.
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Models in this comparison
Gemini 3.5 Flash vs Gemini 3.6 Flash on Vision Evals
Gemini 3.5 Flash scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where Gemini 3.5 Flash leads 70.6% to 57.1%.
Overall, Gemini 3.5 Flash averages 86.0% (#1 of 52) against 83.0% (#6 of 52) for Gemini 3.6 Flash.
Gemini 3.6 Flash is both cheaper ($0.0032 vs $0.011 per sample) and faster (14.7s vs 14.8s per sample).
Gemini 3.5 Flash vs Gemini 3.6 Flash Comparison Table
Evals updated September 3, 2026Pricing updated September 4, 2026
| Property | Gemini 3.5 Flash | Gemini 3.6 Flash |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Jul 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.50 | $0.750 |
| Output $/1M | $9.00 | $3.75 |
| 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 | 86.0% | 83.0% |
| Avg cost / sample | $0.011 | $0.0032 |
| Avg speed / sample | 14.77s | 14.66s |
| By task | ||
| Object Detection (low) | 70.6% ±2.0, Mean of 3 runs, range 68.7 to 72.6 | 57.1% ±1.7, Mean of 3 runs, range 55.9 to 59.4 |
| Object Detection (high) | 69.8% ±1.8, Mean of 3 runs, range 67.5 to 71.1 | 70.7% ±0.4, Mean of 3 runs, range 70.3 to 71.2 |
| Counting (low) | 80.6% ±0.7, Mean of 3 runs, range 79.7 to 81.1 | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | 82.4% ±0.0, Mean of 3 runs, range 82.4 to 82.4 | 79.3% ±2.7, Mean of 3 runs, range 77.0 to 82.4 |
| Identification (low) | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 |
| Identification (high) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 100.0% ±0.0, Mean of 3 runs, range 100.0 to 100.0 |
| OCR (low) | 89.3% ±1.6, Mean of 3 runs, range 88.0 to 91.1 | 88.2% ±0.3, Mean of 3 runs, range 87.9 to 88.4 |
| OCR (high) | 88.9% ±0.2, Mean of 3 runs, range 88.7 to 89.1 | 89.5% ±0.0, Mean of 3 runs, range 89.5 to 89.6 |
| Data Extraction (low) | 94.5% ±0.5, Mean of 3 runs, range 93.8 to 94.8 | 95.9% ±1.0, Mean of 3 runs, range 94.8 to 96.9 |
| Data Extraction (high) | 95.5% ±1.5, Mean of 3 runs, range 93.8 to 96.9 | 94.8% ±1.0, Mean of 3 runs, range 93.8 to 95.9 |
| Reasoning (low) | 82.1% ±2.0, Mean of 3 runs, range 80.1 to 84.1 | 77.7% ±2.0, Mean of 3 runs, range 76.2 to 80.1 |
| Reasoning (high) | 81.0% ±1.7, Mean of 3 runs, range 79.5 to 82.8 | 81.0% ±2.0, Mean of 3 runs, range 79.5 to 83.4 |
Gemini 3.5 Flash vs Gemini 3.6 Flash: Overview
Gemini 3.5 Flash is a multimodal language model developed by Google DeepMind and released at Google I/O 2026. It is built on the Gemini 3 Flash reasoning foundation and introduces configurable thinking levels (minimal, low, medium, and high) that allow developers to tune the depth of internal reasoning before a response is generated. The model accepts text, image, video, audio, and PDF inputs and produces text output, with a 1 million token context window and up to 65,000 output tokens per request. It is natively multimodal, processing visual inputs alongside text to support tasks such as image captioning, classification, optical character recognition, object detection, and visual grounding, where the model references specific regions within an image or video frame.
Its vision capabilities extend to interpreting UI screenshots, diagrams, charts, and real-world scenes, as well as understanding video and live frame sequences for activity and scene recognition. The model supports combined tool use, including Google Search, URL context, code execution, and custom functions, within a single request, and it uses reasoning context from previous turns when thought signatures are present in the conversation history, enabling persistent multi-turn reasoning chains. Gemini 3.5 Flash carries a knowledge cutoff of January 2026 and is available via the Gemini API, Google AI Studio, Google Antigravity, and the Gemini Enterprise Agent Platform.
Gemini 3.6 Flash is a multimodal language model from Google DeepMind, positioned as the workhorse tier in the Gemini 3.x family. It accepts text, image, video, audio, and PDF inputs with a 1 million token context window and produces up to 64,000 output tokens. The model builds directly on Gemini 3.5 Flash, incorporating developer and customer feedback to improve token efficiency, coding quality, and knowledge work performance. According to the Artificial Analysis Index, it consumes 17% fewer output tokens than its predecessor, and on some benchmarks such as DeepSWE, token reduction reaches up to 65%. It supports function calling, structured output, search as a tool, and code execution, and includes computer use as a built-in capability in the Gemini API and Gemini Enterprise.
On coding benchmarks, Gemini 3.6 Flash scores 49% on DeepSWE versus 37% for 3.5 Flash, and 63.9% on MLE Bench versus 49.7%. Computer use performance on OSWorld-Verified improves from 78.4% to 83%, and knowledge work scores on GDPval-AA v2 rise from 1349 to 1421. The model carries a knowledge cutoff of March 2026 and ships with enhanced Frontier Safety safeguards covering chemical, biological, radiological, nuclear, and cyber offense domains, with training to minimize refusals for beneficial uses. It is a proprietary, closed-weights model available in preview through the Gemini API via Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise, and the Gemini app.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.5 Flash performed better. It scores higher on 4 of the six vision tasks and averages 86.0% (#1 of 52) against 83.0% (#6 of 52) for Gemini 3.6 Flash. 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 at low effort, Gemini 3.5 Flash leads with 70.6% against 57.1%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0032 per sample against $0.011. Gemini 3.5 Flash is priced at $1.50 per 1M input tokens and $9.00 per 1M output; Gemini 3.6 Flash is priced at $0.75 per 1M input tokens and $3.75 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3.6 Flash is faster. Across Roboflow's Vision Evals it averaged 14.7s per inference against 14.8s. 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 open prompts and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.