Claude Sonnet 5 vs Gemini 3.5 Flash
Compare Claude Sonnet 5 and Gemini 3.5 Flash side-by-side. See how these vision models stack up in Object Detection, Open Prompt, OCR, Classification, and Image Captioning.
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Claude Sonnet 5 vs Gemini 3.5 Flash: Overview
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.
The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
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.
Claude Sonnet 5 vs Gemini 3.5 Flash Comparison Table
| Property | Claude Sonnet 5 | Gemini 3.5 Flash |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | May 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $1.50 |
| Output $/1M | $10.00 | $9.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Document Question Answering | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | ||
| Vision Language | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 70.15% | 79.1% |
| Avg Response Time | 3.90s | 6.71s |
| Median input tokensincl. image tokens | 2.1K | 1.1K |
| Median output tokens | 61 | 294 |
| Est. cost / taskon this benchmark | $0.0048 | $0.0043 |
| Defect Detection | 73.3%(11/15) | 80%(12/15) |
| Document Understanding | 66.7%(6/9) | 77.8%(7/9) |
| Object Counting | 20%(2/10) | 60%(6/10) |
| Object Understanding | 92.9%(13/14) | 92.9%(13/14) |
| Spatial Understanding | 78.9%(15/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 83.84% | 90.39% |
| Avg Response Time | 2.77s | 4.86s |
| Median input tokensincl. image tokens | 642 | 1.1K |
| Median output tokens | 64 | 196 |
| Est. cost / taskon this benchmark | $0.0019 | $0.0034 |
| Focused Scene OCR | 88.9%(88/99) | 90.9%(90/99) |
| Handwritten Math | 50%(5/10) | 90%(9/10) |
| License Plate Recognition | 90%(27/30) | 100%(30/30) |
| Text Recognition | 80%(24/30) | 86.7%(26/30) |
| VQA & Extraction | 80%(48/60) | 86.7%(52/60) |
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