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Claude 4 Opus (Extended Thinking)
Anthropic's most advanced frontier reasoning model with extended thinking capabilities, displaying detailed thought processes. Achieves 72.5% on SWE-bench (79.4% with parallel compute), 90% on AIME 2025 mathematics. Supports tool use during thinking process, considered world's best coding model. Suitable for professional applications requiring complex reasoning and high-quality code generation.
Intelligence(Relatively Strong)
Speed(Relatively Slow)
Input Supported Modalities
Yes
Is Reasoning Model
200,000
Context Window
128,000
Maximum Output Tokens
-
Knowledge Cutoff
Pricing
￥108 /M tokens
Input
￥540 /M tokens
Output
￥216 /M tokens
Blended Price
Quick Simple Comparison
Claude Opus 4.1
￥15
Claude 4 Sonnet
￥3
Claude 3.5 Haiku
￥0.8
Basic Parameters
Claude 4 Opus (Extended Thinking)Technical Parameters
Parameter Count
Not Announced
Context Length
200.00k tokens
Training Data Cutoff
Open Source Category
Proprietary
Multimodal Support
Text, Image
Throughput
120
Release Date
2025-05-22
Response Speed
64.8,139 tokens/s
Benchmark Scores
Below is the performance of Claude 4 Opus (Extended Thinking) in various standard benchmark tests. These tests evaluate the model's capabilities in different tasks and domains.
Intelligence Index
64.43
Large Language Model Intelligence Level
Coding Index
51.71
Indicator of AI model performance on coding tasks
Math Index
86.93
Capability indicator in solving mathematical problems, mathematical reasoning, or performing math-related tasks
MMLU Pro
87.3
Massive Multitask Multimodal Understanding - Testing understanding of text, images, audio, and video
GPQA
79.6
Graduate Physics Questions Assessment - Testing advanced physics knowledge with diamond science-level questions
HLE
11.7
The model's comprehensive average score on the Hugging Face Open LLM Leaderboard
LiveCodeBench
63.6
Specific evaluation focused on assessing large language models' ability in real-world code writing and solving programming competition problems
SciCode
39.8
The model's capability in code generation for scientific computing or specific scientific domains
HumanEval
-
Score achieved by the AI model on the specific HumanEval benchmark test set
Math 500 Score
98.2
Score on the first 500 larger, more well-known mathematical benchmark tests
AIME Score
75.7
An indicator measuring an AI model's ability to solve high-difficulty mathematical competition problems (specifically AIME level)
GPT 5 Mini
openai
￥1.8
Input tokens/million
￥14.4
Output tokens/million
400k
Context Length
GPT 5 Standard
openai
￥63
Input tokens/million
￥504
Output tokens/million
400k
Context Length
GPT 5 Nano
openai
￥0.36
Input tokens/million
￥2.88
Output tokens/million
400k
Context Length
GPT 5
openai
￥9
Input tokens/million
￥72
Output tokens/million
400k
Context Length
GLM 4.5
chatglm
￥0.43
Input tokens/million
￥1.01
Output tokens/million
131k
Context Length
Gemini 2.0 Flash Lite (Preview)
google
￥0.58
Input tokens/million
￥2.16
Output tokens/million
1M
Context Length
Gemini 1.0 Pro
google
￥3.6
Input tokens/million
￥10.8
Output tokens/million
33k
Context Length
Qwen2.5 Coder Instruct 32B
alibaba
￥0.65
Input tokens/million
￥0.65
Output tokens/million
131k
Context Length