Anthropic Proprietary Released Jul 24, 2026 Updated
Claude Opus 5
ToolsGuide Scores, rankings, test results and price.
Model summary
- Text #21 / 416
567 / 1,000
ToolsGuide Score· votes only
- Vision #21 / 167
558 / 1,000
ToolsGuide Score· votes only
- Coding #6 / 147
552 / 1,000
ToolsGuide Score· votes 538, tests 561
- Agents #7 / 53
540 / 1,000
ToolsGuide Score· votes 528, tests 544
- Price per 1M tokens
In $5.00· Out $25.00
$5.00
Input · median coding model $0.75
Summary
Claude Opus 5 is Anthropic's model, released Jul 24, 2026. It's ranked in 4 categories and does best at coding: #6 of 147, with a ToolsGuide Score of 552 against a median of 316.
It also ranks #21 of 416 for text (567), #21 of 167 for vision (558), #7 of 53 for agents (540).
Its text and vision score uses votes only, because it has published fewer than two of the tests used for those categories.
It costs $5.00 per million input tokens and $25.00 per million output tokens, about 6.7× the median coding model ($0.75).
It has published results on 13 tests and has the top score on 2 of them, including BioMysteryBench and Frontier-Bench v0.1.
Specifications
- Creator
Anthropic- Licence
- Proprietary
- Released
- Jul 24, 2026
- Context window
- 1M tokens
- Input price
- $5.00 / 1M
- Output price
- $25.00 / 1M
- Tests reported
- 13
- Settings ranked
- Claude Opus 5 (Max), Claude Opus 5 (High)
Model comparison
Against the ten best coding models.
Coding score
ToolsGuide Score out of 1,000 · Higher is better
Text score
ToolsGuide Score out of 1,000 · Higher is better
Input price
USD per 1M input tokens · Lower is better
Leaderboards
Where Claude Opus 5 sits among the top 20 in each category.
Text
#21 of 416 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model
Vision
#21 of 167 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model
Coding
#6 of 147 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model
Agents
#7 of 53 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model
By task
Scored from votes on each kind of prompt alone, on the same 1,000-point scale.
Score against price
Coding · the 10 priced models ranked closest · up and to the left is better value
Against its nearest rivals
Text prompts by kind · ToolsGuide Score from votes · hover for values
-
Claude Opus 5 -
Claude Fable 5.1 (Max) -
Gemini 4 Argon (High)
Text
Claude Opus 5 (Max) · hover a bar for its rank
Vision
Claude Opus 5 (High) · hover a bar for its rank
Coding
Claude Opus 5 (Max) · hover a bar for its rank
Test results
Every test Claude Opus 5 has a published result on, out of 100%, with its rank among the models that took it.
Humanity's Last Exam
#2 / 108Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions
64.7%
Self-reported · In the text score· In the vision score
BrowseComp
#3 / 68Finding hard-to-locate facts by browsing the web.
90.8%
Self-reported · In the agents score
DeepSWE 1.1
#22 / 50DeepSWE 1.1 evaluates software engineering agents using the mini-swe-agent harness.
68.8%
Self-reported · In the coding score
Terminal-Bench 4.0
#9 / 28Terminal-Bench 4.0 evaluates AI agents on real-world work in terminal and command-line containerized environments across 66 tasks, with emphasis on science-adjacent and frontier engineering problems. Relative to earlier Terminal-Bench releases, 4.0 increases timeouts and adaptively raises RAM/CPU on selected tasks to reduce harness and resource confounds.
51.8%
Self-reported · In the coding score· In the agents score
AutomationBench
#17 / 24AutomationBench is a tool-use benchmark that evaluates AI agents on automating real-world workflows, testing their ability to orchestrate tools and complete multi-step automation tasks.
26.0%
Self-reported · In the agents score
FrontierCode 1.1
#3 / 23FrontierCode 1.1 evaluates whether coding-agent changes are mergeable, using unit tests, maintainer-defined rubrics, and verifiers. Runs flagged for unfair internet use receive a zero score.
53.4%
Self-reported · In the coding score
HealthBench Professional
#12 / 19HealthBench Professional evaluates model capability and safety for clinician use cases using real clinician-style chats and physician-authored grading rubrics.
59.8%
Self-reported
OSWorld 2.0
#5 / 17OSWorld 2.0 is a benchmark of 108 long-horizon, real-world computer-use workflows spanning everyday and professional tasks. Each task is an end-to-end workflow that takes human users a median of about 1.6 hours, scored with a binary-completion metric, and targets challenges such as dynamic environments, cross-source reasoning, and implicit-state inference.
70.6%
Self-reported
Legal Agent Benchmark
#2 / 15The Legal Agent Benchmark (LAB) is Harvey's open-source benchmark for evaluating AI agents on complex, long-horizon legal work. Tasks are scored under an all-pass standard against expert-curated rubrics, where a task passes only if every required rubric criterion (facts, conclusions, citations, structure, and analytical moves) passes.
11.7%
Self-reported
BioMysteryBench
#1 / 7BioMysteryBench evaluates a model's ability to reason through challenging molecular biology problems, reporting performance on a hard subset and on the subset of problems solved by human experts.
90.1%
Self-reported
ARC-AGI-3
#2 / 5ARC-AGI-3 is the third-generation Abstraction and Reasoning Corpus benchmark, an interactive-reasoning evaluation designed to measure fluid, novel problem-solving ability that remains far from saturated for frontier models.
30.2%
Self-reported
FrontierCode
#2 / 4FrontierCode is Cognition's coding evaluation that tests whether models can pass difficult coding tasks while meeting the standards of high-quality production codebases. The Diamond subset contains the hardest problems.
53.4%
Self-reported
Frontier-Bench v0.1
#1 / 1Frontier-Bench v0.1 evaluates agentic terminal coding. Anthropic reports results using the mini-SWE-agent harness and a GKE backend, measured as mean reward across five attempts per task.
43.3%
Unverified