Claude Opus 4.8 vs Gemini 3.5 Flash
Compare Claude Opus 4.8 and Gemini 3.5 Flash side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.
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Models in this comparison
Claude Opus 4.8 vs Gemini 3.5 Flash on Vision Evals
Gemini 3.5 Flash scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Gemini 3.5 Flash leads 70.6% to 38.6%.
Overall, Claude Opus 4.8 averages 68.7% (#27 of 52) against 86.0% (#1 of 52) for Gemini 3.5 Flash.
Gemini 3.5 Flash is cheaper ($0.011 vs $0.016 per sample), while Claude Opus 4.8 is faster (5.2s vs 14.8s per sample).
Claude Opus 4.8 vs Gemini 3.5 Flash Comparison Table
Evals updated September 3, 2026Pricing updated September 4, 2026
| Property | Claude Opus 4.8 | Gemini 3.5 Flash |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | May 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $1.50 |
| Output $/1M | $25.00 | $9.00 |
| 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 |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 68.7% | 86.0% |
| Avg cost / sample | $0.016 | $0.011 |
| Avg speed / sample | 5.20s | 14.77s |
| By task | ||
| Object Detection (low) | 38.6% | 70.6% ±2.0, Mean of 3 runs, range 68.7 to 72.6 |
| Object Detection (high) | – | 69.8% ±1.8, Mean of 3 runs, range 67.5 to 71.1 |
| Counting (low) | 54.0% | 80.6% ±0.7, Mean of 3 runs, range 79.7 to 81.1 |
| Counting (high) | – | 82.4% ±0.0, Mean of 3 runs, range 82.4 to 82.4 |
| Identification (low) | 84.4% | 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 |
| OCR (low) | 93.8% | 89.3% ±1.6, Mean of 3 runs, range 88.0 to 91.1 |
| OCR (high) | – | 88.9% ±0.2, Mean of 3 runs, range 88.7 to 89.1 |
| Data Extraction (low) | 88.7% | 94.5% ±0.5, Mean of 3 runs, range 93.8 to 94.8 |
| Data Extraction (high) | – | 95.5% ±1.5, Mean of 3 runs, range 93.8 to 96.9 |
| Reasoning (low) | 53.0% | 82.1% ±2.0, Mean of 3 runs, range 80.1 to 84.1 |
| Reasoning (high) | 52.3% | 81.0% ±1.7, Mean of 3 runs, range 79.5 to 82.8 |
Claude Opus 4.8 vs Gemini 3.5 Flash: Overview
Claude Opus 4.8 is Anthropic's most capable generally available large language model, released on May 28, 2026 as an incremental upgrade to Claude Opus 4.7. The model accepts text and image inputs and produces text outputs, with a 1 million token context window on the Claude API, Amazon Bedrock, and Google Cloud Vertex AI (200k tokens on Microsoft Foundry) and up to 128k max output tokens. It uses adaptive thinking and supports adjustable effort tiers — high by default, with extra and max tiers available for more demanding tasks. A fast mode operates at approximately 2.5x standard speed. The model is described by Anthropic as a hybrid reasoning model designed for advanced coding, agentic workflows, long-context reasoning, and professional knowledge work.
Key behavioral improvements over Opus 4.7 include substantially reduced rates of unreported code flaws, improved honesty in self-assessment, and better tool-calling reliability. On Anthropic's Super-Agent benchmark, Opus 4.8 completes every case end-to-end, and it scores 84% on Online-Mind2Web for computer-use and browser-agent tasks. It achieves 88.6% on SWE-bench Verified and 69.2% on SWE-bench Pro. Alongside the model, Anthropic launched Dynamic Workflows in Claude Code (research preview), which enables Claude to orchestrate hundreds of parallel subagents for codebase-scale tasks such as large migrations. The Messages API was also updated to accept mid-task system messages without breaking prompt caching, improving support for long-running agentic pipelines.
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.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.5 Flash performed better. It scores higher on 5 of the six vision tasks and averages 86.0% (#1 of 52) against 68.7% (#27 of 52) for Claude Opus 4.8. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Gemini 3.5 Flash leads with 70.6% against 38.6%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.5 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.011 per sample against $0.016. Claude Opus 4.8 is priced at $5.00 per 1M input tokens and $25.00 per 1M output; Gemini 3.5 Flash is priced at $1.50 per 1M input tokens and $9.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Claude Opus 4.8 is faster. Across Roboflow's Vision Evals it averaged 5.2s 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 image captioning and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.