Prompt caching AI models: send the same prefix twice, pay once

Prompt caching
Services found: 147
#AI modelCapabilitiesAPI $/1M (in / out)Context windowPrice / moOutput $/1MOverviewVisit site
41

DeepSeek V4 Flash 0423

DeepSeek
Text & chatVisionReasoning
$0.088606 / $0.1772121.05M tokensAPI only0.177View
42

GPT-5.4 Mini

OpenAI
Text & chatVisionReasoning
$0.75 / $4.5400K tokensAPI only4.5View
43

MiniMax M2.7

MiniMax
Text & chatReasoningTool calling
$0.3 / $1.2204.8K tokensAPI only1.2View
44

Gemini 3.5 Flash-Lite

Google
Text & chatVisionAudio
$0.3 / $2.51.05M tokensAPI only2.5View
45

Qwen3.6 27B

Qwen
Text & chatVisionVideo
$0.3 / $2262.1K tokensAPI only2View
46

GPT-5.4 Nano

OpenAI
Text & chatVisionReasoning
$0.2 / $1.25400K tokensAPI only1.25View
47

Claude Sonnet 4.5

Anthropic
Text & chatVisionReasoning
$3 / $151M tokensAPI only15View
48

Qwen3.5 397B A17B

Qwen
Text & chatVisionVideo
$0.55 / $3.5262.1K tokensAPI only3.5View
49

Claude Haiku 4.5

Anthropic
Text & chatVisionReasoning
$1 / $5200K tokensAPI only5View
50

Qwen3.5-122B-A10B

Qwen
Text & chatVisionVideo
$0.29 / $2.4262.1K tokensAPI only2.4View
51

Gemma 4 31B

Google
Text & chatVisionVideo
$0.09 / $0.34262.1K tokensAPI only0.34View
52

DeepSeek V3.1 Terminus

DeepSeek
Text & chatStructured JSON outputPrompt caching
$0.27 / $1163.8K tokensAPI only1View
53

Mistral Medium 3.5

Mistral AI
Text & chatVisionReasoning
$1.5 / $7.5256K tokensAPI only7.5View
54

Mistral Small 4

Mistral AI
Text & chatVisionReasoning
$0.15 / $0.6256K tokensAPI only0.6View
55

Qwen3 Coder Next

Qwen
Text & chatCodingReasoning
$0.12 / $0.8262.1K tokensAPI only0.8View
56

Mistral Large 3 2512

Mistral AI
Text & chatVisionReasoning
$0.5 / $1.5262.1K tokensAPI only1.5View
57

Devstral 2 2512

Mistral AI
Text & chatCodingStructured JSON output
$0.4 / $2262.1K tokensAPI only2View
58

Ministral 3 14B 2512

Mistral AI
Text & chatVisionReasoning
$0.2 / $0.2262.1K tokensAPI only0.2View
59

Ministral 3 8B 2512

Mistral AI
Text & chatVisionReasoning
$0.15 / $0.15262.1K tokensAPI only0.15View
60

Ministral 3 3B 2512

Mistral AI
Text & chatVisionReasoning
$0.1 / $0.1131.1K tokensAPI only0.1View

Browse by need

Prompt caching stores an identical prompt prefix between calls, so repeated context is charged at a fraction of the base input rate - typically 10%, and as little as 2.5% on Claude Fable 5.1. If your application sends the same lengthy system prompt with every request, this is usually the biggest saving available to you.

147 models support it. The saving applies to input only, so the effect is largest where prompts are long and answers are short - support bots with detailed instructions, agents with big tool catalogues, RAG systems that resend the same corpus. Cache entries expire, so the economics favour high request frequency against a stable prefix.

Input onlyThe discount never touches output, so it favours long prompts and short answers.
Prefix must match exactlyOne changed character at the start invalidates the whole cached block.
Frequency beats sizeA modest prompt reused constantly saves more than a huge one called twice a day.

Models with this capability

How to choose

  • Put everything stable at the very front: system prompt, tool definitions, reference documents.
  • Keep user-specific content after the cached prefix.
  • Check the cache lifetime — short TTLs punish low-traffic applications.
  • Account for write cost: some vendors charge a premium to create the entry.

Common mistakes

Interleaving variables into the prefixA timestamp at the top of the prompt destroys the cache on every call.
Assuming it helps everywhereShort prompts gain almost nothing.

Who this suits

Agent platformsLarge tool catalogues resent with every step.
RAG systemsThe same corpus queried repeatedly by many users.
Support botsDetailed instructions in front of every short question.

Frequently asked questions

How much does caching save?

Cache reads typically cost 10% of base input, and 2.5% on Claude Fable 5.1. The saving applies to input only.

What has to stay identical?

The prefix — system prompt, tool definitions, reference documents. Anything after the cached portion can vary freely.

When is caching pointless?

Short prompts, or requests spaced far enough apart that the cache expires between them.

How much can I realistically save?

On a long stable prefix, up to 90% of the input portion of the bill.

Does caching change answers?

No. It changes billing and latency, not the computation.

Frequently searched

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