Claude Opus 4 vs Gemini 3.5 Flash
Compare Claude Opus 4 and Gemini 3.5 Flash side-by-side. See how these vision models stack up in Image Captioning, OCR, Object Detection, Open Prompt, and Classification.
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Claude Opus 4 is deprecated and can no longer be run. Details and evals are still available on its model page.
Models in this comparison
Claude Opus 4 vs Gemini 3.5 Flash: Overview
Claude 4 Opus, released by Anthropic in May 2025, is the flagship model of the Claude 4 family, built for complex, long-horizon reasoning and advanced coding workflows. It is multimodal, supporting text (including voice), images, and tool use, and operates as a hybrid reasoning model—able to deliver quick answers in fast mode or switch to extended thinking for deeper, multi-step problem solving. With a ~200,000-token context window and a training cutoff around March 2025, it is optimized for handling large documents, long conversations, and sophisticated agentic tasks.
Positioned at the high end of Anthropic’s offerings, Opus 4 achieves state-of-the-art results on coding benchmarks like SWE-Bench (72.5%) and Terminal-Bench (43.2%). It is best suited for research, enterprise automation, and software development at scale. The model is classified at Anthropic’s ASL-3 safety level, denoting advanced oversight and safety features.
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 Opus 4 vs Gemini 3.5 Flash Comparison Table
| Property | Claude Opus 4 | Gemini 3.5 Flash |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2025 | May 2026 |
| Context Window | 200K | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $15.00 | $1.50 |
| Output $/1M | $75.00 | $9.00 |
| Vision Tasks | ||
| Captioning | Demo | |
| Classification | Demo | |
| Object Detection | Demo | |
| OCR | Demo | |
| Visual Question Answering | Demo | |
| Chart Question Answering | ||
| Document Question Answering | ||
| Multi-Label Classification | ||
| Vision Language | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 56.72% | 79.1% |
| Avg Response Time | 19.74s | 6.71s |
| Median input tokensincl. image tokens | 1.1K | |
| Median output tokens | 294 | |
| Est. cost / taskon this benchmark | $0.0043 | |
| Defect Detection | 66.7%(10/15) | 80%(12/15) |
| Document Understanding | 88.9%(8/9) | 77.8%(7/9) |
| Object Counting | 0%(0/10) | 60%(6/10) |
| Object Understanding | 64.3%(9/14) | 92.9%(13/14) |
| Spatial Understanding | 57.9%(11/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 90.39% | |
| Avg Response Time | 4.86s | |
| Median input tokensincl. image tokens | 1.1K | |
| Median output tokens | 196 | |
| Est. cost / taskon this benchmark | $0.0034 | |
| Focused Scene OCR | 90.9%(90/99) | |
| Handwritten Math | 90%(9/10) | |
| License Plate Recognition | 100%(30/30) | |
| Text Recognition | 86.7%(26/30) | |
| VQA & Extraction | 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