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Claude Opus 4.8 vs GPT-4.1 mini

Compare Claude Opus 4.8 and GPT-4.1 mini 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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OpenAIGPT-4.1 mini

GPT-4.1 mini is deprecated and can no longer be run. Details and evals are still available on its model page.

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Claude Opus 4.8 vs GPT-4.1 mini: 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.

GPT-4.1 mini

GPT-4.1 mini, released by OpenAI in April 2025, is a smaller, faster, and cheaper variant of GPT-4.1 designed for high-throughput and cost-sensitive applications. It is multimodal, handling both text and images, and inherits the full model’s strengths in coding, structured outputs, and long-context reasoning. With support for up to 1 million tokens, it enables reliable processing of extended documents, multi-file codebases, and lengthy conversations while keeping latency low.

GPT-4.1 mini offers an efficient alternative to GPT-4.1 and replaced GPT-4o mini as the default ChatGPT model in May 2025. Despite being smaller, it matches or outperforms GPT-4o on several benchmarks, particularly for instruction following and real-world coding tasks. Ideal use cases include large-scale conversational systems, affordable developer tools, document analysis, and interactive assistants where speed and cost are critical.

Claude Opus 4.8 vs GPT-4.1 mini Comparison Table

PropertyClaude Opus 4.8GPT-4.1 mini
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2026Apr 2025
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$5.00$0.400
Output $/1M$25.00$1.60
Vision Tasks
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
67.16%
Avg Response Time4.36s
Median input tokensincl. image tokens2.0K
Median output tokens92
Est. cost / taskon this benchmark$0.012
Defect Detection
66.7%(10/15)
Document Understanding
77.8%(7/9)
Object Counting
30%(3/10)
Object Understanding
85.7%(12/14)
Spatial Understanding
68.4%(13/19)
OCR
Overall Score
87.34%
Avg Response Time3.99s
Median input tokensincl. image tokens578
Median output tokens81
Est. cost / taskon this benchmark$0.0049
Focused Scene OCR
91.9%(91/99)
Handwritten Math
60%(6/10)
License Plate Recognition
90%(27/30)
Text Recognition
86.7%(26/30)
VQA & Extraction
83.3%(50/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology