upcube

Prompt Caching: Cost & Performance Analysis Across Providers

Intelligence, Performance & Price Analysis

Source:Artificial Analysis scrape
Intelligence
not provided

This profile does not provide this metric.

Speed
not provided

This profile does not provide this metric.

Latency
not provided

This profile does not provide this metric.

Input Price
not provided

This profile does not provide this metric.

Output Price
not provided

This profile does not provide this metric.

Decision Overview

Ethen's intelligence-driven routing assessment for this model

Overall verdict

General purpose assistant

Route complex or high-value tasks here when the extra capability justifies the cost.

Best for

    Weak or unsuitable

    • See model-fit matrix below

    Routing recommendation

    1

    Review quality flags before production deployment

    Cost pressure

    Pricing data not available for automated cost analysis.

    Additional Benchmarks

    Further benchmark and comparison data.

    Pricing: Input, Cached Hit and Output

    Price (USD per M Tokens) · Evaluation results measured independently by Artificial Analysis

    Source and methodology are summarized below

    Chart source and provenance are listed in Methodology & sources below.

    $0.00$0.25$0.50$0.75$1.00DeepSeek V3.2: $0.00$0.00DDeepSeek V3.2GPT-5 mini (high): $0.00$0.00AIGPT-5 mini (high)Kimi K2.6: $0.00$0.00KKimi K2.6Gemini 2.5 Pro: $0.00$0.00GGemini 2.5 ProGPT-5 (high): $0.00$0.00AIGPT-5 (high)Claude Sonnet 4.6 (max): $0.00$0.00AClaude Sonnet 4.6 (max)Claude Opus 4.6 (max): $0.00$0.00AClaude Opus 4.6 (max)

    Specifications

    Technical Specifications

    Prompt caching is a critical new innovation for language model inference - saving developers up to 90% and making long context inputs suddenly viable. Compare features and pricing across all major AI providers below.

    Methodology & Provenance

    This page is rendered from the normalized profile and page JSON for Prompt Caching: Cost & Performance Analysis Across Providers.

    Benchmark values are preserved as normalized; only layout, disclosure ordering, and typography are adjusted for readability.