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Claude Opus 4.8 vs Claude Opus 5

Compare Claude Opus 4.8 and Claude Opus 5 side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.

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AnthropicClaude Opus 4.8
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AnthropicClaude Opus 5
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Models in this comparison

Anthropic

Claude Opus 4.8 vs Claude Opus 5 on Vision Evals

Claude Opus 5 scores higher on 5 of the six Vision Evals tasks.

The widest gap is Reasoning, where Claude Opus 5 leads 71.5% to 53.0%.

Overall, Claude Opus 4.8 averages 66.8% (#16 of 25) against 77.0% (#8 of 25) for Claude Opus 5.

Claude Opus 4.8 is both cheaper ($0.016 vs $0.017 per sample) and faster (5.2s vs 7.4s per sample).

Claude Opus 4.8Claude Opus 5

Claude Opus 4.8 vs Claude Opus 5 Comparison Table

Evals updated August 6, 2026Pricing updated August 11, 2026

PropertyClaude Opus 4.8Claude Opus 5
OrganizationAnthropicAnthropic
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2026Jul 2026
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$5.00$5.00
Output $/1M$25.00$25.00
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Image Tagging
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
66.8%
77.0%
Avg cost / sample$0.016$0.017
Avg speed / sample5.20s7.38s
By task
Object Detection
38.6%
$0.026
54.0%
$0.027
Counting
52.7%
$0.0076
70.3%
$0.0096
Identification
75.0%
$0.0067
84.4%
$0.0072
OCR
93.8%
$0.020
93.2%
$0.020
Data Extraction
87.6%
$0.0076
88.7%
$0.0080
Reasoning (low)
53.0%
$0.0078
71.5%
$0.010
Reasoning (high)
52.3%
$0.0078
74.2%
$0.018

Claude Opus 4.8 vs Claude Opus 5: Overview

Claude Opus 4.8

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.

Claude Opus 5

Claude Opus 5 is a large language model with multimodal vision capabilities developed by Anthropic, released on July 24, 2026 as the fourth model in the Claude 5 family. It sits in the Opus tier of Anthropic's lineup, positioned below the Mythos-class Fable 5 and Mythos 5 models, and is framed by Anthropic as the go-to model for most knowledge work and automation tasks. The model approaches Fable 5's capabilities at roughly half the cost, priced at $5 per million input tokens and $25 per million output tokens. It becomes the default model on Claude Max and the strongest model available on Claude Pro. The model ships with a 1 million token context window and an adjustable "effort" parameter that allows users to trade reasoning depth for speed and token savings. Early enterprise customers reported that Opus 5 achieved comparable performance to Opus 4.8's maximum-reasoning mode while generating significantly fewer tokens on average, and demonstrated higher accuracy on financial modeling tasks with fewer tool calls and less time.

Claude Opus 5 supports multimodal inputs including images and text, and is designed for agentic workflows, coding, scientific research, and complex enterprise tasks. Anthropic reports the model scores 10.2 percentage points higher than Opus 4.8 on an internal chemistry benchmark, making it the most capable generally available model for scientific research in the Claude lineup. Cyber classifiers on Opus 5 are designed to intervene approximately 85 percent less often than those on Fable 5, with fallback to Opus 4.8 when a classifier triggers. The model does not retain user data for 30 days, unlike Fable 5. It is available across Anthropic's platforms including Claude Code and Claude Cowork, as well as cloud partners.

Frequently Asked Questions

On Roboflow's Vision Evals, Claude Opus 5 performed better. It scores higher on 5 of the six vision tasks and averages 77.0% (#8 of 25) against 66.8% (#16 of 25) 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 Reasoning benchmark at low effort, Claude Opus 5 leads with 71.5% against 53.0%. This is the widest gap between the two models across the benchmark's tasks.

Claude Opus 4.8 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.016 per sample against $0.017. Claude Opus 4.8 is priced at $5.00 per 1M input tokens and $25.00 per 1M output; Claude Opus 5 is priced at $5.00 per 1M input tokens and $25.00 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 7.4s. 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.