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
101

GPT-5.6 Terra Pro

OpenAI
Text & chatVisionReasoning
$2 / $121.05M tokensAPI only12View
102

GPT-5.1

OpenAI
Text & chatVisionReasoning
$1.25 / $10400K tokensAPI only10View
103

Qwen3.6 Plus

Qwen
Text & chatVisionVideo
$0.325 / $1.951M tokensAPI only1.95View
104

Claude Opus 4.5

Anthropic
Text & chatVisionReasoning
$5 / $25200K tokensAPI only25View
105

GLM 4.7 Flash

Z.ai
Text & chatVisionStructured JSON output
$0.0605 / $0.4202.8K tokensAPI only0.4View
106

Qwen3 VL 8B Instruct

Qwen
Text & chatVisionReasoning
$0.117 / $0.455262.1K tokensAPI only0.455View
107

GLM 4.6

Z.ai
Text & chatStructured JSON outputPrompt caching
$0.43 / $1.75204.8K tokensAPI only1.75View
108

Qwen3.6 Flash

Qwen
Text & chatVisionVideo
$0.1875 / $1.1251M tokensAPI only1.125View
109

Qwen3.5-27B

Qwen
Text & chatVisionVideo
$0.195 / $1.56262.1K tokensAPI only1.56View
110

Qwen3 Next 80B A3B Instruct

Qwen
Text & chatReasoningAdjustable thinking
$0.09 / $1.1262.1K tokensAPI only1.1View
111

Qwen3 VL 30B A3B Instruct

Qwen
Text & chatVisionReasoning
$0.15 / $0.6262.1K tokensAPI only0.6View
112

gpt-oss-safeguard-20b

OpenAI
Text & chatStructured JSON outputCode execution
$0.075 / $0.3131.1K tokensAPI only0.3View
113

GLM 5V Turbo

Z.ai
Text & chatVisionVideo
$1.2 / $4202.8K tokensAPI only4View
114

Qwen3 Max

Qwen
Text & chatReasoningAdjustable thinking
$0.78 / $3.9262.1K tokensAPI only3.9View
115

Qwen3 Coder Flash

Qwen
Text & chatVisionCoding
$0.195 / $0.9751M tokensAPI only0.975View
116

GLM 5 Turbo

Z.ai
Text & chatReasoningTool calling
$1.2 / $4202.8K tokensAPI only4View
117

Kimi K2 Thinking

Moonshot AI
Text & chatReasoningTool calling
$0.6 / $2.5262.1K tokensAPI only2.5View
118

GPT-5.2-Codex

OpenAI
Text & chatVisionCoding
$1.75 / $14400K tokensAPI only14View
119

Qwen3.5 Plus 2026-02-15

Qwen
Text & chatVisionVideo
$0.26 / $1.561M tokensAPI only1.56View
120

Qwen3.5 Plus 2026-04-20

Qwen
Text & chatVisionVideo
$0.3 / $1.81M tokensAPI only1.8View

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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