4,854 个接口 · 11,195 个价格 · 最近采集 10 小时前 · 30 天内 442 条需关注的调价
每个平台公布的每一个价格
采集器定时抓取 10 个平台公开的全部价格(默认每日一次),并完整保存原始页面快照,绝无任何手工篡改。搜索具体模型,即可查看当前各单位下的最低价格。
平台
第 8401–8500 条,共 11,195 条
每百万缓存读取 token100
| 平台 | 模型 · 计价项 | 美元 | 平台原文 | 生效于 |
|---|---|---|---|---|
| Runware未披露 | Gemini 3.8 FlashPer 1M | $0.075/百万缓存读 | $7.5e-8 / cachedInputToken | |
| Aiberm未披露 | o3 Mini | $0.076/百万缓存读 | model_ratio 0.55 × $2/M · group default ×0.23 · cache_ratio 0.3 | |
| Runware未披露 | DeepSeek-V4-Pro-0813Per 1M | $0.079/百万缓存读 | $7.9e-8 / cachedInputToken | |
| KIE未披露 | Kimi K3.1 Pro | $0.08/百万缓存读 | 16 credits per million tokens ($0.08)平台声称官方或 Fal 价 $0.2/百万缓存读 | |
| OpenRouter第三方 | Thinking Machines: Inkling Small (batch) | $0.08/百万缓存读 | input_cache_read: 0.00000008 / token (endpoint deepinfra/fp8) | |
| OpenRouter第三方 | Xiaomi: MiMo-V2.5 | $0.08/百万缓存读 | input_cache_read: 0.00000008 / token (endpoint venice/fp8) | |
| OpenRouter第三方 | Z.ai: GLM 4.6via Venice · fp4 | $0.08/百万缓存读 | input_cache_read: 0.00000008 / token (endpoint venice/fp4) | |
| OpenRouter第三方 | Z.ai: GLM 4.7via DeepInfra · fp4 | $0.08/百万缓存读 | input_cache_read: 0.00000008 / token (endpoint deepinfra/fp4) | |
| Aiberm未披露 | Kimi K2.5 | $0.083/百万缓存读 | model_ratio 0.9 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| Aiberm未披露 | Kimi K2.6 | $0.083/百万缓存读 | model_ratio 0.9 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| Aiberm未披露 | GPT-5.2 Codex | $0.083/百万缓存读 | model_ratio 0.6 × $2/M · group default ×0.23 · cache_ratio 0.3 | |
| Aiberm未披露 | Kimi K2.5 | $0.083/百万缓存读 | model_ratio 0.9 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| Aiberm未披露 | Kimi K2.5 Thinking | $0.083/百万缓存读 | model_ratio 0.9 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| Aiberm未披露 | GPT-5.2 Codex | $0.083/百万缓存读 | model_ratio 0.6 × $2/M · group default ×0.23 · cache_ratio 0.3 | |
| OpenRouter官方 | OpenAI: GPT-5.2 (batch) | $0.088/百万缓存读 | input_cache_read: 0.0000000875 / token (endpoint openai) | |
| Aiberm未披露 | Claude Sonnet | $0.088/百万缓存读 | model_ratio 1.917 × $2/M · group default ×0.23 · cache_ratio 0.1 | |
| OpenRouter第三方 | Z.ai: GLM 5.1via Chutes · fp8 | $0.09/百万缓存读 | input_cache_read: 0.00000009 / token (endpoint chutes/fp8) | |
| Aiberm未披露 | MiniMax M2.7 | $0.091/百万缓存读 | model_ratio 0.9852 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| Aiberm未披露 | Gemini 3.5 Flash | $0.092/百万缓存读 | model_ratio 1 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| Aiberm未披露 | Gemini 3.6 Flash | $0.092/百万缓存读 | model_ratio 1 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| Aiberm未披露 | Gemini 3.7 Flash | $0.092/百万缓存读 | model_ratio 1 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| Aiberm未披露 | Gemini 3.8 Flash | $0.092/百万缓存读 | model_ratio 1 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| Aiberm未披露 | MiMo V2 Omni | $0.092/百万缓存读 | model_ratio 1 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| Aiberm未披露 | Grok 4.5 | $0.095/百万缓存读 | model_ratio 0.8261 × $2/M · group default ×0.23 · cache_ratio 0.25 | |
| Aiberm未披露 | Grok 4.6 | $0.095/百万缓存读 | model_ratio 0.8261 × $2/M · group default ×0.23 · cache_ratio 0.25 | |
| Aiberm未披露 | GPT-5.6 Sol | $0.097/百万缓存读 | model_ratio 1.413 × $2/M · group default ×0.23 · cache_ratio 0.15 | |
| OpenRouter未披露 | DeepSeek: DeepSeek Pro Latest | $0.1/百万缓存读 | input_cache_read: 0.0000000999 / token | |
| OpenRouter官方 | Anthropic: Claude Fable 5.2 (batch) | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint anthropic) | |
| OpenRouter官方合作 | Anthropic: Claude Haiku 4.5via Azure · global | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint azure/global) | |
| OpenRouter官方 | Anthropic: Claude Opus 5.5 (batch) | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint anthropic) | |
| OpenRouter官方合作 | Anthropic: Claude Sonnet 5.5via Google · global | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint google-vertex/global) | |
| OpenRouter官方 | Anthropic: Claude Sonnet 5 (batch) | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint anthropic) | |
| OpenRouter第三方 | ByteDance: UI-TARS 7B | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint parasail/bf16) | |
| OpenRouter官方 | Google: Gemini 2.5 Flashvia Google AI Studio | $0.1/百万缓存读 | input_audio_cache: 0.0000001 / token (endpoint google-ai-studio) | |
| OpenRouter官方 | Google: Nano Banana (Gemini 2.5 Flash Image) | $0.1/百万缓存读 | input_audio_cache: 0.0000001 / token | |
| OpenRouter官方 | Google: Gemini 3 Flash Previewvia Google AI Studio | $0.1/百万缓存读 | input_audio_cache: 0.0000001 / token (endpoint google-ai-studio) | |
| OpenRouter官方 | Google: Gemini 4 Argon | $0.1/百万缓存读 | input_audio_cache: 0.0000001 / token | |
| OpenRouter官方 | Google: Gemini 4 Argon | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token | |
| OpenRouter官方 | Google: Gemini 4 Carbonvia Google AI Studio | $0.1/百万缓存读 | input_audio_cache: 0.0000001 / token (endpoint google-ai-studio) | |
| OpenRouter官方 | Google: Gemini 4 Carbonvia Google AI Studio | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint google-ai-studio) | |
| OpenRouter未披露 | Google: Gemma 4 31Bvia KT | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint kt) | |
| OpenRouter未披露 | NVIDIA: Nemotron 3 Ultravia KT | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint kt) | |
| OpenRouter官方合作 | OpenAI: GPT-4.1 Minivia Azure | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint azure) | |
| OpenRouter官方 | OpenAI: GPT-5.6 Terra Pro (batch) | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint openai) | |
| OpenRouter官方 | OpenAI: GPT-5.6 Terra (batch) | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint openai) | |
| OpenRouter官方 | OpenAI: GPT-6 Sol Pro (batch) | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint openai) | |
| OpenRouter官方 | OpenAI: GPT-6 Sol (batch) | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint openai) | |
| OpenRouter官方合作 | OpenAI: GPT-6.1 Solvia Amazon Bedrock · global | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint amazon-bedrock/global) | |
| OpenRouter官方合作 | OpenAI: GPT-6.1 Sol Provia Azure | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint azure) | |
| OpenRouter官方 | OpenAI: GPT-6.1 Sol Pro (batch)提示词超过 272K | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (>272000; endpoint openai) | |
| OpenRouter官方 | OpenAI: GPT-6.1 Sol (batch)提示词超过 272K | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (>272000; endpoint openai) | |
| OpenRouter官方合作 | OpenAI: GPT-6.1 Terra提示词超过 272Kvia Azure | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (>272000; endpoint azure) | |
| OpenRouter官方合作 | OpenAI: GPT-6.1 Terra Pro提示词超过 272Kvia Azure | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (>272000; endpoint azure) | |
| OpenRouter第三方 | Qwen: Qwen3 Coder 480B A35BTurbovia DeepInfra | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint deepinfra/turbo) | |
| OpenRouter第三方 | StepFun: Step 5 Previewvia StepFun · fp8 | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint stepfun/fp8) | |
| OpenRouter第三方 | Thinking Machines: Inkling Small | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (endpoint deepinfra/fp8) | |
| OpenRouter未披露 | Anthropic: Claude Sonnet Latest | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token | |
| OpenRouter未披露 | Google: Gemini Pro Latest | $0.1/百万缓存读 | input_audio_cache: 0.0000001 / token | |
| OpenRouter未披露 | Google: Gemini Pro Latest | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token | |
| OpenRouter未披露 | OpenAI: GPT Sol Latest | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token | |
| OpenRouter未披露 | OpenAI: GPT Terra Latest提示词超过 272K | $0.1/百万缓存读 | input_cache_read: 0.0000001 / token (>272000) | |
| Runware未披露 | Claude Haiku 4.5Per 1M | $0.1/百万缓存读 | $1e-7 / cachedInputToken | |
| Runware未披露 | Gemini 4 ArgonThrough Nov 30 · 2026 | $0.1/百万缓存读 | $1e-7 / cachedInputToken · Through Nov 30, 2026 | |
| Runware未披露 | GPT-6.1 Sol≤ 272K context | $0.1/百万缓存读 | $1e-7 / cachedInputToken · ≤ 272K context | |
| Runware未披露 | GPT-6.1 Terra> 272K context | $0.1/百万缓存读 | $1e-7 / cachedInputToken · > 272K context | |
| OpenRouter官方 | Qwen: Qwen3 Coder Flash提示词超过 128K | $0.104/百万缓存读 | input_cache_read: 0.000000104 / token (>128000; endpoint alibaba) | |
| OpenRouter未披露 | Z.ai: GLM 5.2via DeepInfra · fp4 | $0.105/百万缓存读 | input_cache_read: 0.000000105 / token (endpoint deepinfra/fp4) | |
| Aiberm未披露 | Kimi K3 | $0.106/百万缓存读 | model_ratio 2.3 × $2/M · group default ×0.23 · cache_ratio 0.1 | |
| OpenRouter第三方 | Qwen: Qwen3 VL 235B A22B Instructvia DeepInfra · fp8 | $0.11/百万缓存读 | input_cache_read: 0.00000011 / token (endpoint deepinfra/fp8) | |
| OpenRouter官方 | Z.ai: GLM 4.5 | $0.11/百万缓存读 | input_cache_read: 0.00000011 / token (endpoint z-ai/fp8) | |
| OpenRouter第三方 | Z.ai: GLM 4.5Vvia Novita · fp8 | $0.11/百万缓存读 | input_cache_read: 0.00000011 / token (endpoint novita/fp8) | |
| Aiberm未披露 | GLM-5.3 | $0.11/百万缓存读 | model_ratio 2.4 × $2/M · group default ×0.23 · cache_ratio 0.1 | |
| Aiberm未披露 | GLM-5 Turbo | $0.113/百万缓存读 | model_ratio 1.224 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| OpenRouter官方 | Baidu: ERNIE-Light-DT-1.0 | $0.113/百万缓存读 | input_cache_read: 0.000000113 / token (endpoint baidu) | |
| Aiberm未披露 | Claude Fable 5.1 | $0.115/百万缓存读 | model_ratio 5 × $2/M · group default ×0.23 · cache_ratio 0.05 | |
| Aiberm未披露 | MiniMax M2.1 | $0.115/百万缓存读 | model_ratio 0.5 × $2/M · group default ×0.23 · cache_ratio 0.5 | |
| KIE未披露 | grok-4-5 | $0.12/百万缓存读 | 24 credits per million tokens ($0.12)平台声称官方或 Fal 价 $0.3/百万缓存读 | |
| OpenRouter第三方 | MoonshotAI: Kimi K2.7 Codevia CoreWeave · int4 | $0.12/百万缓存读 | input_cache_read: 0.00000012 / token (endpoint coreweave/int4) | |
| OpenRouter第三方 | Qwen: Qwen3 VL 8B Instruct | $0.12/百万缓存读 | input_cache_read: 0.00000012 / token (endpoint parasail/bf16) | |
| OpenRouter第三方 | Z.ai: GLM 5via StreamLake · fp8 | $0.12/百万缓存读 | input_cache_read: 0.00000012 / token (endpoint streamlake/fp8) | |
| Runware未披露 | MiniMax M3> 512K context | $0.12/百万缓存读 | $1.2e-7 / cachedInputToken · > 512K context | |
| Aiberm未披露 | GPT-4o | $0.124/百万缓存读 | model_ratio 0.9 × $2/M · group default ×0.23 · cache_ratio 0.3 | |
| OpenRouter官方 | Anthropic: Claude Fable 5.1 (batch) | $0.125/百万缓存读 | input_cache_read: 0.000000125 / token (endpoint anthropic) | |
| OpenRouter官方 | Google: Gemini 2.5 Provia Google AI Studio | $0.125/百万缓存读 | input_audio_cache: 0.000000125 / token (endpoint google-ai-studio) | |
| OpenRouter官方 | Google: Gemini 2.5 Provia Google AI Studio | $0.125/百万缓存读 | input_cache_read: 0.000000125 / token (endpoint google-ai-studio) | |
| OpenRouter官方 | Google: Gemini 2.5 Pro Preview 06-05via Google AI Studio | $0.125/百万缓存读 | input_audio_cache: 0.000000125 / token (endpoint google-ai-studio) | |
| OpenRouter官方 | Google: Gemini 2.5 Pro Preview 06-05via Google AI Studio | $0.125/百万缓存读 | input_cache_read: 0.000000125 / token (endpoint google-ai-studio) | |
| OpenRouter官方合作 | OpenAI: GPT-5via Azure | $0.125/百万缓存读 | input_cache_read: 0.000000125 / token (endpoint azure) | |
| OpenRouter官方合作 | OpenAI: GPT-5.1-Codex-Max | $0.125/百万缓存读 | input_cache_read: 0.000000125 / token (endpoint azure) | |
| OpenRouter官方 | OpenAI: GPT-5.4 (batch) | $0.125/百万缓存读 | input_cache_read: 0.000000125 / token (endpoint openai) | |
| Aiberm未披露 | MiniMax M2.5 | $0.128/百万缓存读 | model_ratio 0.5555 × $2/M · group default ×0.23 · cache_ratio 0.5 | |
| Aiberm未披露 | GPT-5.6 | $0.13/百万缓存读 | model_ratio 1.413 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| OpenRouter第三方 | DeepSeek: DeepSeek V3.1via DeepInfra · fp4 | $0.13/百万缓存读 | input_cache_read: 0.00000013 / token (endpoint deepinfra/fp4) | |
| OpenRouter官方合作 | OpenAI: GPT-5.1via Azure | $0.13/百万缓存读 | input_cache_read: 0.00000013 / token (endpoint azure) | |
| OpenRouter官方合作 | OpenAI: GPT-5.1-Codex | $0.13/百万缓存读 | input_cache_read: 0.00000013 / token (endpoint azure) | |
| Aiberm未披露 | DeepSeek V4 Pro | $0.132/百万缓存读 | model_ratio 1.43478 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| OpenRouter官方 | OpenAI: o4 Mini (batch) | $0.138/百万缓存读 | input_cache_read: 0.0000001375 / token (endpoint openai) | |
| Aiberm未披露 | GPT-5.1 | $0.138/百万缓存读 | model_ratio 1 × $2/M · group default ×0.23 · cache_ratio 0.3 | |
| Aiberm未披露 | GPT-5.4 | $0.138/百万缓存读 | model_ratio 1.5 × $2/M · group default ×0.23 · cache_ratio 0.2 | |
| Aiberm未披露 | GPT-5.5 xhigh | $0.138/百万缓存读 | model_ratio 1.5 × $2/M · group default ×0.23 · cache_ratio 0.2 |