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Claude Opus 4.8 vs Claude Opus 5.5

Claude Opus 5.5 and Claude Opus 4.8 score 82.0% vs 58.3% averaged across five Vision Evals tasks (#4 and #40 of 65 models), so Claude Opus 5.5 leads overall.

  • Claude Opus 5.5 wins all 5 tasks, with its widest lead on object detection (74.5% vs 38.6%).
  • Claude Opus 5.5 is cheaper: $0.015 vs $0.021 overall average cost per sample.

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AnthropicClaude Opus 4.8
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Claude Opus 4.8 vs Claude Opus 5.5 on Vision Evals

Score by taskClaude Opus 4.8Claude Opus 5.5
Object Detection
38.6
74.5
Reasoning
53.0
83.0
Counting
54.0
80.6
OCR
61.5
78.3
Identification
84.4
93.8

Roboflow Vision Evals. Full breakdown below.

Claude Opus 4.8 vs Claude Opus 5.5 Comparison Table

Evals updated October 26, 2026Pricing updated November 7, 2026

PropertyClaude Opus 4.8Claude Opus 5.5
OrganizationAnthropicAnthropic
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2026Sep 2026
Context Window1.0M1.0M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$5.00$4.00
Output $/1M$25.00$20.00
Vision Tasks
Both support 10 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
58.3%
82.0%
Avg cost / sample$0.021$0.015
Avg speed / sample7.66s7.63s
By task
Object Detection (low)
38.6%
$0.026
74.5%
±0.5, Mean of 3 runs, range 74.0 to 75.0
$0.021
Object Detection (high)–
76.0%
±1.1, Mean of 3 runs, range 74.7 to 77.0
$0.030
Counting (low)
54.0%
$0.0076
80.6%
±2.0, Mean of 3 runs, range 78.4 to 82.4
$0.0081
Counting (high)–
82.0%
±2.0, Mean of 3 runs, range 79.7 to 83.8
$0.0098
Identification (low)
84.4%
$0.0067
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0058
Identification (high)–
95.8%
±1.6, Mean of 3 runs, range 93.8 to 96.9
$0.0067
OCR (low)
61.5%
$0.025
78.3%
$0.016
by category
Single value
53.0%
Transcription
70.8%
Structured JSON
82.4%
Text localization
24.2%
Single value
67.8%
Transcription
96.7%
Structured JSON
91.5%
Text localization
61.0%
OCR (high)
62.1%
$0.038
79.9%
$0.023
by category
Single value
54.8%
Transcription
67.0%
Structured JSON
83.5%
Text localization
23.6%
Single value
69.6%
Transcription
96.1%
Structured JSON
94.1%
Text localization
61.1%
Reasoning (low)
53.0%
$0.0078
83.0%
±1.0, Mean of 3 runs, range 82.1 to 84.1
$0.0090
Reasoning (high)
52.3%
$0.0078
85.9%
±2.6, Mean of 3 runs, range 82.8 to 88.1
$0.011

Claude Opus 4.8 vs Claude Opus 5.5: 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. 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 5.5

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. 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.

Frequently Asked Questions

On Roboflow's Vision Evals, Claude Opus 5.5 performed better. It scores higher on all five vision tasks and averages 82.0% (#4 of 65) against 58.3% (#40 of 65) 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, Claude Opus 5.5 leads with 74.5% against 38.6%. This is the widest gap between the two models across the benchmark's tasks.

Claude Opus 5.5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.015 per sample against $0.021. Claude Opus 4.8 is priced at $5.00 per 1M input tokens and $25.00 per 1M output; Claude Opus 5.5 is priced at $4.00 per 1M input tokens and $20.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Claude Opus 5.5 is faster. Across Roboflow's Vision Evals it averaged 7.6s per inference against 7.7s. 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.