Claude Opus 4.7

Made by Anthropic, released 16 Apr 2026. Epoch AI lists its access as “API access”. Appears strongest at coding and writing among the models listed here.

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In short

What it does well

  • Coding: Top tier (7th of 77 listed models)
  • Writing: Top tier (10th of 77 listed models)
  • Knowledge: Strong (21st of 43 listed models)
  • Maths: Strong (21st of 43 listed models)

Where it falls short

The four main ratings show no weakness. That is not evidence it has none.

What we don't know yet

Every test we use has a result.

The four ratings

  • Top tier
  • Strong
  • Middle
  • Behind the leaders
  • Not enough data

Ratings compare only the 77 models listed here, in quarters, best first. “Not enough data” means no result yet. It is never a low score.

Writing
Top tierHow often people preferred its answers to writing prompts. 1,486 on Arena's scale · Source for Claude Opus 4.7 writing score (lmarena)
Coding
Top tierHow often people preferred its answers to coding questions. 1,519 on Arena's scale · Source for Claude Opus 4.7 coding score (lmarena)
Maths
StrongHow many very hard maths problems it solved. 70.2% · Source for Claude Opus 4.7 maths score (epoch)
Knowledge
StrongHow many short factual questions it answered correctly. 51.7% · Source for Claude Opus 4.7 knowledge score (epoch)

Evidence completeness: Broad evidence, 13 of 13 tests have a result.

Where it is relatively stronger or weaker

This compares each kind of request with the model's own overall position among the 77 listed models, using Arena's votes. It is about performance on different languages, kinds of request and topics, not about political or social bias.

Relatively stronger

No category stands out as clearly stronger than its overall position.

Relatively weaker

No category stands out as clearly weaker than its overall position.

Show every category we can see
Position in each Arena category
CategoryPositionArena scoreVotesSource
Expert-level prompts
Kind of request
11th of 771,519
95% range 1,511 to 1,527
6,918Source for Expert-level prompts
Hard prompts
Kind of request
10th of 771,506
95% range 1,501 to 1,510
43,653Source for Hard prompts
Long prompts
Kind of request
8th of 771,505
95% range 1,500 to 1,510
30,080Source for Long prompts
Longer conversations
Kind of request
3rd of 771,505
95% range 1,498 to 1,512
11,414Source for Longer conversations
Chinese
Language
13th of 761,531
95% range 1,521 to 1,541
4,172Source for Chinese
English prompts
Language
10th of 771,496
95% range 1,491 to 1,501
30,177Source for English prompts
French
Language
9th of 721,503
95% range 1,489 to 1,517
2,292Source for French
German
Language
8th of 551,497
95% range 1,478 to 1,515
1,022Source for German
Japanese
Language
15th of 541,473
95% range 1,449 to 1,497
720Source for Japanese
Korean
Language
12th of 541,451
95% range 1,432 to 1,470
1,110Source for Korean
Non-English prompts
Language
11th of 771,480
95% range 1,476 to 1,485
34,931Source for Non-English prompts
Russian
Language
13th of 771,494
95% range 1,486 to 1,502
6,860Source for Russian
Spanish
Language
6th of 701,494
95% range 1,480 to 1,509
2,049Source for Spanish
Entertainment, sport and media
Topic
10th of 771,474
95% range 1,467 to 1,480
15,117Source for Entertainment, sport and media
Law and government
Topic
8th of 761,504
95% range 1,495 to 1,513
5,251Source for Law and government
Medicine and health
Topic
11th of 751,495
95% range 1,486 to 1,505
4,955Source for Medicine and health
Science
Topic
11th of 771,508
95% range 1,501 to 1,514
11,046Source for Science
Software and IT
Topic
7th of 771,516
95% range 1,511 to 1,521
26,193Source for Software and IT

Bias: what we can and can't see

Our sources do not measure political lean, stereotyping, flattery (agreeing with you too readily) or safety behaviour, so we cannot say how any model does on those. What we can show is above: where a model is relatively stronger or weaker across languages, kinds of request and topics. Also, people voting in Arena can favour answer style, such as length and formatting, and our numbers do not adjust for that. See what AI bias means and what these tests can tell you.

Best-supported use cases

This model is not among the options we recommend for any of the five tasks. That reflects the rules and the models listed, not a verdict on the model.

Where an alternative might be more suitable

What it is

Maker
Anthropic
Release date (Epoch AI)
16 Apr 2026
Access, as listed by Epoch AI
API access
Licence, as listed by Arena
Proprietary
Maker's country (Epoch AI)
United States of America
Last observation in our data
28 Oct 2026
Data last changed
29 Oct 2026, 14:36 UTC

We have not verified an official product page for this model, so we do not link to one.

How you can use it

Proprietary. The model's files are not published, so it is generally available only through its maker or partners the maker chooses.

How you can reach it (an app, a website, a developer API or a third-party tool) and what that costs are not covered by our benchmark data. Where we could verify prices from the maker's own pages, they are shown under Price. Epoch AI lists access as “API access”. See models, apps and harnesses and how AI pricing works.

Price

Price not independently verified

What Anthropic's own page says about the Opus family (USD ($ as shown; the page does not state a currency code); checked 28 Oct 2026)

  • Free ($0): Opus not available
  • Pro ($20 per month billed monthly, or $17 per month billed annually ($200 up front)): Opus available
  • Max (From $100 per month): Opus available

The page marks plans against model families, not versions, so it does not say which version of a family a plan includes. Check that before relying on it.

See Claude plans and pricing (checked 28 Oct 2026). Page opened and read.

We never infer subscription prices from API prices. New to “tokens” and “per million”? See how AI pricing works.

Good to remember

  • Benchmarks are samples of tasks. None proves performance in every situation.
  • Arena scores come from people choosing between answers. That measures preference, not correctness.
  • Ratings are relative to the 77 models listed here. A different set would change them.
  • Sources test models at different settings. Each result shows the setting used, and we do not adjust it.

The evidence, for the curious

Show all 13 tests with settings and sources
All published results for Claude Opus 4.7. Ratings compare only the 77 models on this site.
TestResultRatingSetting usedSource identifierObservedLinks
Arena text: overall preference
Arena 'text' leaderboard (Bradley-Terry rating, no style control)
1,490
Arena score
95% range 1,486 to 1,493, 65,110 votes
Top tier effort: High, bradley-terry rating Claude Opus 4.7 (High)
lmarena · text/latest
28 Oct 2026 Source for Claude Opus 4.7 Arena text: overall preference score (lmarena)
Arena text: creative writing
Arena 'text' leaderboard, category creative_writing
1,486
Arena score
95% range 1,479 to 1,493, 11,769 votes
Top tier effort: High, bradley-terry rating Claude Opus 4.7 (High)
lmarena · text/latest
28 Oct 2026 Source for Claude Opus 4.7 Arena text: creative writing score (lmarena)
Arena text: coding
Arena 'text' leaderboard, category coding
1,519
Arena score
95% range 1,513 to 1,525, 18,587 votes
Top tier effort: High, bradley-terry rating Claude Opus 4.7 (High)
lmarena · text/latest
28 Oct 2026 Source for Claude Opus 4.7 Arena text: coding score (lmarena)
Arena text: maths
Arena 'text' leaderboard, category math
1,499
Arena score
95% range 1,488 to 1,509, 3,286 votes
Top tier effort: High, bradley-terry rating Claude Opus 4.7 (High)
lmarena · text/latest
28 Oct 2026 Source for Claude Opus 4.7 Arena text: maths score (lmarena)
Arena text: following instructions
Arena 'text' leaderboard, category instruction_following
1,498
Arena score
95% range 1,492 to 1,503, 22,726 votes
Top tier effort: High, bradley-terry rating Claude Opus 4.7 (High)
lmarena · text/latest
28 Oct 2026 Source for Claude Opus 4.7 Arena text: following instructions score (lmarena)
Arena text: business and management
Arena 'text' leaderboard, category industry_business_and_management_and_financial_operations
1,490
Arena score
95% range 1,484 to 1,497, 13,043 votes
Top tier effort: High, bradley-terry rating Claude Opus 4.7 (High)
lmarena · text/latest
28 Oct 2026 Source for Claude Opus 4.7 Arena text: business and management score (lmarena)
Arena WebDev: building web apps
Arena 'webdev' leaderboard (Code Arena), category overall
1,558
Arena score
95% range 1,552 to 1,563, 17,859 votes
Strong effort: High, bradley-terry rating Claude Opus 4.7 (High)
lmarena · webdev/latest
28 Oct 2026 Source for Claude Opus 4.7 Arena WebDev: building web apps score (lmarena)
FrontierMath Tiers 1-3 (v2)
FrontierMath Tiers 1-3 v2 (private set), benchmark released 2026-06-12
70.2%
share of problems solved
standard error ±2.7 points
Strong effort: max, best score across scorers claude-opus-4-7_max
epoch · frontiermath_tiers_1_3_v2.csv
10 Jun 2026 Source for Claude Opus 4.7 FrontierMath Tiers 1-3 (v2) score (epoch) · Run details for FrontierMath Tiers 1-3 (v2)
OTIS Mock AIME 2024-2025
OTIS Mock AIME 2024-2025, benchmark released 2024-12-19
86.7%
share of problems solved
standard error ±5.1 points
Behind the leaders effort: max, best score across scorers claude-opus-4-7_max
epoch · otis_mock_aime_2024_2025.csv
6 Aug 2026 Source for Claude Opus 4.7 OTIS Mock AIME 2024-2025 score (epoch) · Run details for OTIS Mock AIME 2024-2025
GPQA Diamond
GPQA Diamond, benchmark released 2023-11-20
86.4%
share of questions correct
standard error ±2.4 points
Behind the leaders effort: max, best score across scorers claude-opus-4-7_max
epoch · gpqa_diamond.csv
6 Aug 2026 Source for Claude Opus 4.7 GPQA Diamond score (epoch) · Run details for GPQA Diamond
SimpleQA Verified
SimpleQA Verified, benchmark released 2025-09-09
51.7%
share of questions correct
standard error ±1.6 points
Strong effort: xhigh, best score across scorers claude-opus-4-7_xhigh
epoch · simpleqa_verified.csv
27 Aug 2026 Source for Claude Opus 4.7 SimpleQA Verified score (epoch) · Run details for SimpleQA Verified
SWE-bench Verified (Epoch runs)
SWE-bench Verified, benchmark released 2024-08-13, as run by Epoch AI
83.5%
share of issues resolved
standard error ±1.7 points
Top tier effort: max, best score across scorers claude-opus-4-7_max
epoch · swe_bench_verified.csv
20 Apr 2026 Source for Claude Opus 4.7 SWE-bench Verified (Epoch runs) score (epoch) · Run details for SWE-bench Verified (Epoch runs)
FrontierCode (reported by Cognition)
FrontierCode, benchmark released 2026-06-08, aggregation Mean@5
38.5%
mean score over 5 attempts
no uncertainty figure published
Not rated (results use different tools) effort: max, tool: claude-code, mean@5 claude-opus-4-7_unknown
epoch · frontiercode_external.csv
not provided Source for Claude Opus 4.7 FrontierCode (reported by Cognition) score (epoch) · Run details for FrontierCode (reported by Cognition)

Missing evidence

Every test we use has a result for this model.

Where a source gives no public run log, we cannot show the run behind the score. See data sources.

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