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Anthropic · frontier

Is Claude Opus 4.7 good?

Claude Opus 4.7 places top-3 in 1 of 6 task areas we tested. It's strongest at chef / home cooking and weakest at landing pages. It's a top-tier (premium) option ($30/1M tokens).

Top-3 finishes
6/22
Task areas tested
$30.0
Per 1M tokens
Slow
Typical speed

Our take

Anthropic: Claude Opus 4.7 is a frontier-tier model that we tested across 6 task areas. It performs best at Chef / Home Cooking, and is weakest at Landing Pages.

Strengths

Clarity Specificity Usefulness Concision Structure Risk handling

Weak spots

No notable weaknesses.

How Claude Opus 4.7 ranks, task by task

Rank is the position in each field; Rating is the absolute quality bar. A model can be rated "Excellent" yet sit mid-table when the whole field is strong — and vice-versa.

Task area Rank
Chef / Home Cooking #2 of 50
Investor & Pitch #9 of 50
AI Strategy #10 of 50
Content & Brand #19 of 50
Executive Assistant #21 of 50
Landing Pages #30 of 56

Industry benchmarks

Standardized third-party scores, shown for context — these are independent of our real-task tests.

Artificial Analysis indices

53.5
Intelligence
73.6
Coding
44.4
Agentic

Design Arena — human preference (avg Elo 1307)

3d 1313 svg 1247 dataviz 1275 gamedev 1339 website 1324 asciiart 1278 uicomponent 1355 codecategories 1325

Headline vs. real-world

Claude Opus 4.7 looks strong on paper but underperforms on our real-world tasks relative to its Intelligence Index.

Source: Artificial Analysis (artificialanalysis.ai) via OpenRouter (openrouter.ai/rankings). (2026-06-29) · Source: Design Arena (www.designarena.ai) via OpenRouter (openrouter.ai/rankings). (2026-06-29)

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Frequently asked

Is Claude Opus 4.7 any good?

Claude Opus 4.7 places top-3 in 1 of 6 task areas we benchmarked. It performs best at Chef / Home Cooking.

What is Claude Opus 4.7 best at?

Its strongest task areas are Chef / Home Cooking, Investor & Pitch, AI Strategy.

Is there a cheaper alternative to Claude Opus 4.7?

Qwen3.7 Max is 83% cheaper and stays competitive on quality across our benchmarks.

How we test & rank

Each model is scored on real tasks across 6 task areas by an LLM judge with deterministic checks. "Percentile" is this model's standing within each area's field; the cross-area figures are rank-based so quality and Elo scores are never mixed.

This page is Spring Prompt, running

We just did this for every model. Do it for your prompt.

The rankings above come from running real tasks through real models and scoring every output. Spring Prompt is that same engine — pointed at your prompt, your test cases, and your definition of good.

  • Generate test cases from your prompt — no eval set required to start.
  • Compare models side by side with quality, cost and latency in one matrix.
  • Optimise the winner until the scores say it's ready to ship.
Experiment · Cold outreach email

Prompt × model results

12 test cases · 3 evals
Claude Opus
GPT-5
Gemini
v1
7.1
6.8
7.4
v2
8.3
7.9
8.0
v3
9.2
8.6
8.4
Best combo: v3 × Claude Opus
9.2 quality · $0.004/run · 1.8s