Claude Opus 5.5 vs Gemini 3.5 Flash
Compare Claude Opus 5.5 and Gemini 3.5 Flash side-by-side.
Compare Claude Opus 5.5 vs Gemini 3.5 Flash live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
Claude Opus 5.5 vs Gemini 3.5 Flash on Vision Evals
Gemini 3.5 Flash scores higher on 3 of the six Vision Evals tasks.
The widest gap is Identification, where Gemini 3.5 Flash leads 99.0% to 93.8%.
Overall, Claude Opus 5.5 averages 85.5% (#3 of 57) against 86.0% (#2 of 57) for Gemini 3.5 Flash.
Gemini 3.5 Flash is cheaper ($0.011 vs $0.014 per sample), while Claude Opus 5.5 is faster (12.8s vs 14.8s per sample).
Claude Opus 5.5 vs Gemini 3.5 Flash Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Claude Opus 5.5 | Gemini 3.5 Flash |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | May 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.50 | |
| Output $/1M | $9.00 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 85.5% | 86.0% |
| Avg cost / sample | $0.014 | $0.011 |
| Avg speed / sample | 12.76s | 14.77s |
| By task | ||
| Object Detection (low) | 74.4% ±0.5, Mean of 3 runs, range 73.9 to 74.8 | 70.6% ±2.0, Mean of 3 runs, range 68.7 to 72.6 |
| Object Detection (high) | 76.8% ±1.2, Mean of 3 runs, range 75.4 to 77.8 | 69.8% ±1.8, Mean of 3 runs, range 67.5 to 71.1 |
| Counting (low) | 80.6% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 80.6% ±0.7, Mean of 3 runs, range 79.7 to 81.1 |
| Counting (high) | 82.0% ±2.0, Mean of 3 runs, range 79.7 to 83.8 | 82.4% ±0.0, Mean of 3 runs, range 82.4 to 82.4 |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 |
| Identification (high) | 95.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 |
| OCR (low) | 87.8% ±0.6, Mean of 3 runs, range 87.0 to 88.2 | 89.3% ±1.6, Mean of 3 runs, range 88.0 to 91.1 |
| OCR (high) | 87.2% ±0.6, Mean of 3 runs, range 86.5 to 87.8 | 88.9% ±0.2, Mean of 3 runs, range 88.7 to 89.1 |
| Data Extraction (low) | 93.5% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | 94.5% ±0.5, Mean of 3 runs, range 93.8 to 94.8 |
| Data Extraction (high) | 93.5% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | 95.5% ±1.5, Mean of 3 runs, range 93.8 to 96.9 |
| Reasoning (low) | 83.0% ±1.0, Mean of 3 runs, range 82.1 to 84.1 | 82.1% ±2.0, Mean of 3 runs, range 80.1 to 84.1 |
| Reasoning (high) | 85.9% ±2.6, Mean of 3 runs, range 82.8 to 88.1 | 81.0% ±1.7, Mean of 3 runs, range 79.5 to 82.8 |
Claude Opus 5.5 vs Gemini 3.5 Flash: Overview
Claude Opus 5.5 is a proprietary multimodal reasoning model from Anthropic and the first entry in the Claude 5.5 family. It accepts interleaved text and image input and returns text, with a one million token context window and up to 128,000 output tokens per response. Adaptive thinking is always enabled on this model and cannot be disabled; thinking depth is instead governed by an effort parameter with five levels, where medium is the default, a change from the high default used by Claude Opus 5 and earlier Opus models. Anthropic reports a knowledge cutoff of June 2026.
On the visual side, Anthropic characterizes Opus 5.5 as its strongest Opus release for vision and computer use, describing improved reading of dense documents, charts, screenshots, and diagrams for document extraction and visual analysis tasks. Published results include 89.0% on Chartography with tools and 81.8% on OSWorld 2.0 under partial credit scoring, alongside 48.7% under strict scoring reported in the system card. The accompanying system card states that Opus 5.5 scored higher than Opus 5 on every evaluation in its capability summary, with the largest gains concentrated in agentic coding, visual reasoning, computer use, and long-horizon knowledge work. The model ships with safety classifiers covering biology and cybersecurity that can route blocked requests to earlier Claude models.
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.