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Anthropic Proprietary Released Aug 5, 2025 Updated

Claude Opus 4.1

ToolsGuide Scores, rankings, test results and price.

Model summary

  • Text #114 / 419

    351 / 1,000

    ToolsGuide Score· votes 417, tests 299

  • Coding #104 / 151

    276 / 1,000

    ToolsGuide Score· votes 276, tests 276

  • Price per 1M tokens

    No listed price

Summary

Claude Opus 4.1 is Anthropic's model, released Aug 5, 2025. It's ranked in 2 categories and does best at text: #114 of 419, with a ToolsGuide Score of 351 against a median of 252.

It also ranks #104 of 151 for coding (276).

It has published results on 8 tests.

Specifications

Creator
Anthropic
Licence
Proprietary
Released
Aug 5, 2025
Context window
–
Input price
–
Output price
–
Tests reported
8
Settings ranked
Claude Opus 4.1 (Thinking 16K, 20250805), Claude Opus 4.1 (20250805)

Model comparison

Against the ten best text models.

Text score

ToolsGuide Score out of 1,000 · Higher is better

Coding score

ToolsGuide Score out of 1,000 · Higher is better

Input price

USD per 1M input tokens · Lower is better

Leaderboards

Where Claude Opus 4.1 sits among the top 20 in each category.

Text

#114 of 419 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model

Full text leaderboard →

Coding

#104 of 151 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model

Full coding leaderboard →

By task

Scored from votes on each kind of prompt alone, on the same 1,000-point scale.

Against its nearest rivals

Text prompts by kind · ToolsGuide Score from votes · hover for values

Text

Claude Opus 4.1 (Thinking 16K, 20250805) · hover a bar for its rank

Coding

Claude Opus 4.1 (20250805) · hover a bar for its rank

Test results

Every test Claude Opus 4.1 has a published result on, out of 100%, with its rank among the models that took it.

  • GPQA

    #105 / 265

    Graduate-level science questions written to be hard to look up.

    80.9%

    Self-reported

  • AIME 2025

    #80 / 124

    Competition maths problems from the 2025 American Invitational Mathematics Exam.

    78.0%

    Self-reported

  • SWE-Bench Verified

    #43 / 123

    Fixing real bugs in real GitHub projects, checked by the projects' own tests.

    74.5%

    Self-reported

  • MMMLU

    #10 / 51

    Multilingual Massive Multitask Language Understanding dataset released by OpenAI, featuring professionally translated MMLU test questions across 14 languages including Arabic, Bengali, German, Spanish, French, Hindi, Indonesian, Italian, Japanese, Korean, Portuguese, Swahili, Yoruba, and Chinese. Contains approximately 15,908 multiple-choice questions per language covering 57 subjects.

    89.5%

    Self-reported

  • TAU-bench Retail

    #2 / 25

    A benchmark for evaluating tool-agent-user interaction in retail environments. Tests language agents' ability to handle dynamic conversations with users while using domain-specific API tools and following policy guidelines. Evaluates agents on tasks like order cancellations, address changes, and order status checks through multi-turn conversations.

    82.4%

    Self-reported

  • Terminal-Bench

    #5 / 25

    Terminal-Bench is a benchmark for testing AI agents in real terminal environments. It evaluates how well agents can handle real-world, end-to-end tasks autonomously, including compiling code, training models, setting up servers, system administration, security tasks, data science workflows, and cybersecurity vulnerabilities. The benchmark consists of a dataset of ~100 hand-crafted, human-verified tasks and an execution harness that connects language models to a terminal sandbox.

    43.3%

    Self-reported

  • TAU-bench Airline

    #10 / 23

    Part of τ-bench (TAU-bench), a benchmark for Tool-Agent-User interaction in real-world domains. The airline domain evaluates language agents' ability to interact with users through dynamic conversations while following domain-specific rules and using API tools. Agents must handle airline-related tasks and policies reliably.

    56.0%

    Self-reported

  • MMMU (validation)

    #2 / 4

    Validation set of the Massive Multi-discipline Multimodal Understanding and Reasoning benchmark. Features college-level multimodal questions across 6 core disciplines (Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, Tech & Engineering) spanning 30 subjects and 183 subfields with diverse image types including charts, diagrams, maps, and tables.

    77.1%

    Self-reported