Claude Opus 4.8 vs Gemini 3.6 Flash
Compare Claude Opus 4.8 and Gemini 3.6 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 3.6 Flash on Vision Evals
Gemini 3.6 Flash scores higher on all five Vision Evals tasks.
The widest gap is Counting, where Gemini 3.6 Flash leads 80.2% to 54.0%.
Overall, Claude Opus 4.8 averages 58.3% (#38 of 63) against 77.1% (#11 of 63) for Gemini 3.6 Flash.
Gemini 3.6 Flash is cheaper ($0.0033 vs $0.021 per sample), while Claude Opus 4.8 is faster (7.7s vs 10.4s per sample).
Claude Opus 4.8 vs Gemini 3.6 Flash Comparison Table
Evals updated October 13, 2026Pricing updated October 19, 2026
| Property | Claude Opus 4.8 | Gemini 3.6 Flash |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Jul 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.750 |
| Output $/1M | $25.00 | $3.75 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Video Classification | Not listed | Supported |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 58.3% | 77.1% |
| Avg cost / sample | $0.021 | $0.0033 |
| Avg speed / sample | 7.66s | 10.43s |
| By task | ||
| Object Detection (low) | 38.6% | 57.1% ±1.7, Mean of 3 runs, range 55.9 to 59.4 |
| Object Detection (high) | – | 70.7% ±0.4, Mean of 3 runs, range 70.3 to 71.2 |
| Counting (low) | 54.0% | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | – | 79.3% ±2.7, Mean of 3 runs, range 77.0 to 82.4 |
| Identification (low) | 84.4% | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 |
| Identification (high) | – | 100.0% ±0.0, Mean of 3 runs, range 100.0 to 100.0 |
| OCR (low) | 61.5% | 71.8% |
| by category |
|
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| OCR (high) | 62.1% | 77.4% |
| by category |
|
|
| Reasoning (low) | 53.0% | 77.7% ±2.0, Mean of 3 runs, range 76.2 to 80.1 |
| Reasoning (high) | 52.3% | 81.0% ±2.0, Mean of 3 runs, range 79.5 to 83.4 |
Claude Opus 4.8 vs Gemini 3.6 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 3.6 Flash is a multimodal language model from Google DeepMind, positioned as the workhorse tier in the Gemini 3.x family. It accepts text, image, video, audio, and PDF inputs with a 1 million token context window and produces up to 64,000 output tokens. The model builds directly on Gemini 3.5 Flash, incorporating developer and customer feedback to improve token efficiency, coding quality, and knowledge work performance. According to the Artificial Analysis Index, it consumes 17% fewer output tokens than its predecessor, and on some benchmarks such as DeepSWE, token reduction reaches up to 65%. It supports function calling, structured output, search as a tool, and code execution, and includes computer use as a built-in capability in the Gemini API and Gemini Enterprise.
On coding benchmarks, Gemini 3.6 Flash scores 49% on DeepSWE versus 37% for 3.5 Flash, and 63.9% on MLE Bench versus 49.7%. Computer use performance on OSWorld-Verified improves from 78.4% to 83%, and knowledge work scores on GDPval-AA v2 rise from 1349 to 1421. The model carries a knowledge cutoff of March 2026 and ships with enhanced Frontier Safety safeguards covering chemical, biological, radiological, nuclear, and cyber offense domains, with training to minimize refusals for beneficial uses. It is a proprietary, closed-weights model available in preview through the Gemini API via Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise, and the Gemini app.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3.6 Flash performed better. It scores higher on all five vision tasks and averages 77.1% (#11 of 63) against 58.3% (#38 of 63) 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 Counting benchmark at low effort, Gemini 3.6 Flash leads with 80.2% against 54.0%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0033 per sample against $0.021. Claude Opus 4.8 is priced at $5.00 per 1M input tokens and $25.00 per 1M output; Gemini 3.6 Flash is priced at $0.75 per 1M input tokens and $3.75 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 7.7s per inference against 10.4s. 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.