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Anthropic's Claude Sonnet, Haiku, Opus models compared on cost and speed

By PulseAugur Editorial · [1 sources] ·

A technical comparison of Anthropic's Claude models, Sonnet, Haiku, and Opus, was conducted over 90 days using 904 calls for a critique.flashcards job. Sonnet and Haiku had the same cost per call, but Sonnet achieved this through significant prompt caching, while Haiku incurred full rates on all tokens. Opus was the fastest model and the most expensive per call, with a similar caching rate to Sonnet. Haiku was discontinued from the job after six days, leaving Sonnet and Opus in use, with Sonnet being the preferred choice for cost-effectiveness when prompt caching is effective. AI

IMPACT Provides insights into cost and performance trade-offs for Anthropic's Claude models, aiding developers in selecting the most suitable option for specific use cases.

RANK_REASON Comparative analysis of existing models on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Anthropic's Claude Sonnet, Haiku, Opus models compared on cost and speed

How we ranked this

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37 / 100
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Newsworthiness bucket
Tool
Comparative analysis of existing models on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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product, infra
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High
Clearly on-topic for AI-industry coverage.
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Breaking (< 6h)
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Full methodology in our editorial standards.

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Robert ·

    Sonnet vs Haiku vs Opus on critique.flashcards: When To Reach For Which

    <p>If you are building a flashcard critique step and trying to pick between Sonnet, Haiku and Opus, the choice is not as simple as picking the cheapest model. Two of the three landed at the same cost per call, and the reason they did is different enough to matter.</p> <p>We ran t…

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