Claude Opus 4.8 vs Gemini 3.8 Flash
Compare Claude Opus 4.8 and Gemini 3.8 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 3.8 Flash on Vision Evals
Gemini 3.8 Flash scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Gemini 3.8 Flash leads 68.1% to 38.6%.
Overall, Claude Opus 4.8 averages 68.7% (#21 of 36) against 85.1% (#3 of 36) for Gemini 3.8 Flash.
Gemini 3.8 Flash is cheaper ($0.0033 vs $0.016 per sample), while Claude Opus 4.8 is faster (5.2s vs 11.6s per sample).
Claude Opus 4.8 vs Gemini 3.8 Flash Comparison Table
Evals updated September 2, 2026Pricing updated September 2, 2026
| Property | Claude Opus 4.8 | Gemini 3.8 Flash |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | |
| Output $/1M | $25.00 | |
| 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% | 85.1% |
| Avg cost / sample | $0.016 | $0.0033 |
| Avg speed / sample | 5.20s | 11.65s |
| By task | ||
| Object Detection (low) | 38.6% | 68.1% ±0.8, Mean of 3 runs, range 67.3 to 69.0 |
| Object Detection (high) | – | 74.8% ±1.1, Mean of 3 runs, range 73.4 to 75.6 |
| Counting (low) | 54.0% | 78.8% ±2.7, Mean of 3 runs, range 75.7 to 81.1 |
| Counting (high) | – | 79.3% ±1.4, Mean of 3 runs, range 78.4 to 81.1 |
| Identification (low) | 84.4% | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 |
| Identification (high) | – | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 |
| OCR (low) | 93.8% | 87.3% ±0.8, Mean of 3 runs, range 86.5 to 88.2 |
| OCR (high) | – | 88.8% ±0.7, Mean of 3 runs, range 88.0 to 89.4 |
| Data Extraction (low) | 88.7% | 97.3% ±0.5, Mean of 3 runs, range 96.9 to 97.9 |
| Data Extraction (high) | – | 94.8% ±1.0, Mean of 3 runs, range 93.8 to 95.9 |
| Reasoning (low) | 53.0% | 81.2% ±0.3, Mean of 3 runs, range 80.8 to 81.5 |
| Reasoning (high) | 52.3% | 84.5% ±1.0, Mean of 3 runs, range 83.4 to 85.4 |
Claude Opus 4.8 vs Gemini 3.8 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 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.
On vision oriented evaluations the model reports 86.2% on CharXiv Reasoning for chart and figure synthesis and 87.8% on LVBench for long video understanding in agentic mode, alongside 90.8% on Terminal-Bench 2.1 and 61.6% on SWE-Bench Pro for coding. Following Gemini API conventions, it can localize objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.8 Flash performed better. It scores higher on 5 of the six vision tasks and averages 85.1% (#3 of 36) against 68.7% (#21 of 36) 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 3.8 Flash leads with 68.1% against 38.6%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.8 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0033 per sample against $0.016. 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 11.6s. 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.