Models & Pricing

Liaobots is committed to providing developers with fast, convenient, and affordable API interface solutions, building a stable and easy-to-use API platform that integrates almost all AI large models in one place.

Billing Rules

Billing Method: Billing follows OpenAI API’s token-based pricing method, where 1000 tokens ≈ 500-750 words. All prices in the table below are per 1 million (1M) tokens.

Cache Billing Method: The “Cache Read/Create” column consistently uses the main-site and standard OpenAI-compatible API credit rates, shown as read/create per 1M. Cache reads use a model-specific configured price when available; otherwise they fall back to point input price × 0.1. Standard endpoints do not charge for cache creation, shown as 0; fixed-price models (image/video) do not use caching, shown as -. Claude Code (/v1/messages) uses a separate official-ratio pricing path described under “Claude Code Billing Method” below and does not use the prices in this column.

Gemini 3.6/3.7 Flash promotion: The prices shown are valid through December 31, 2026 and will be adjusted to Google’s standard pricing starting January 1, 2027.

Tiered Billing Method: Some models are priced in tiers based on input length. For those models the “Point Price”, “Official Price (USD)” and “Cache Read/Create” columns below list every tier as ≤272K: … ; >272K: …, and the model list on the main site shows a “Tiered billing” badge. The tier is selected by the total input tokens of a single request (including cached tokens) and excludes output tokens. The upper bound is inclusive: exactly 272K stays on the standard tier, while 272K + 1 moves to the long-context tier. The whole request is billed at the matched tier — it is never split and summed across tiers. The GPT-5.6 family (Sol / Terra / Luna) uses tiered billing today.

Claude Code Billing Method: The billing algorithm is consistent with the official, with a dynamically adjusted price ratio. Current ratio: 1 USD = 3.5 credits. Cache reads are billed at official input price × current ratio × 0.1; 5-minute cache creation is × 1.25 and 1-hour cache creation is × 2. The -t discounts in the Point Price column apply only to the main site and standard API, not to Claude Code.

CodeX Billing Method: The billing algorithm is consistent with the official, with dynamically adjusted price ratio. Current ratio: 1 USD = 0.5 credits

Credits to RMB Ratio: Credits and RMB are at a 1:1 ratio. In this article, you can see the ratio between our prices and official prices.

Voice reading billing (main-site features, not API tokens):

Feature	Pricing
Multi-voice / streaming multi-voice / custom voice synthesis	By text length: 0.25 credits per 500 characters (partial blocks count as full)
Single-voice reading	Per generation: 0.05 credits

Long Conversation Billing Principle: The principle of long conversations with AI is that each conversation implicitly sends all previous content to the AI. As conversations get longer, the cumulative content sent also increases, so token consumption will also increase.

How to Integrate

We support API calls for various models. No format adaptation is needed. Use OpenAI API format, just modify base_url and api_key. api_key uses the main site Login Code, pay-as-you-go.

Chat Completions

OpenAI Official API Documentation

base_url: https://ai.liaobots.work/v1 (for backup URLs, see API Call Examples)

api_key: Login Code

curl --location 'https://ai.liaobots.work/v1/chat/completions' \
--header 'Authorization: Bearer Login Code' \
--header 'Content-Type: application/json' \
--data '{
    "model": "gpt-4o",
    "stream": false,
    "messages": [
        {
            "role": "user",
            "content": "hi"
        }
    ]
}'

Model List

The following prices are for 1M tokens (1 million tokens). Official prices are in USD, and credits can be simply calculated as RMB. Latest exchange rate (~7.3): usd-to-cny-rate

Prices may change with market fluctuations, but we will always insist on providing lower prices than official

Provider	Model Name	Context	Point Price	Cache Read/Create	Price Ratio	Official Price (USD)
Anthropic	claude-sonnet-5-t	1M	Input 3/M, Output 15/M	0.3/0 per 1M	20%	Input 2/M, Output 10/M
Anthropic	claude-sonnet-5	1M	Input 7/M, Output 35/M	0.7/0 per 1M	47%	Input 2/M, Output 10/M
Anthropic	claude-sonnet-4-6	1M	Input 5.25/M, Output 26.25/M	0.525/0 per 1M	47%	Input 1.5/M, Output 7.5/M
Anthropic	claude-sonnet-4-5-20250929-thinking	200K	Input 5.25/M, Output 26.25/M	0.525/0 per 1M	47%	Input 1.5/M, Output 7.5/M
Anthropic	claude-sonnet-4-5-20250929-t	200K	Input 2.25/M, Output 11.25/M	0.225/0 per 1M	20%	Input 1.5/M, Output 7.5/M
Anthropic	claude-sonnet-4-5-20250929	200K	Input 5.25/M, Output 26.25/M	0.525/0 per 1M	47%	Input 1.5/M, Output 7.5/M
Anthropic	claude-opus-5-t	1M	Input 7.5/M, Output 37.5/M	0.75/0 per 1M	20%	Input 5/M, Output 25/M
Anthropic	claude-opus-5	1M	Input 17.5/M, Output 87.5/M	1.75/0 per 1M	47%	Input 5/M, Output 25/M
Anthropic	claude-opus-4-8	1M	Input 17.5/M, Output 87.5/M	1.75/0 per 1M	47%	Input 5/M, Output 25/M
Anthropic	claude-opus-4-7-t	1M	Input 7.5/M, Output 37.5/M	0.75/0 per 1M	20%	Input 5/M, Output 25/M
Anthropic	claude-opus-4-7	1M	Input 17.5/M, Output 87.5/M	1.75/0 per 1M	47%	Input 5/M, Output 25/M
Anthropic	claude-opus-4-6-thinking	1M	Input 17.5/M, Output 87.5/M	1.75/0 per 1M	47%	Input 5/M, Output 25/M
Anthropic	claude-opus-4-6-t	1M	Input 7.5/M, Output 37.5/M	0.75/0 per 1M	20%	Input 5/M, Output 25/M
Anthropic	claude-opus-4-6	1M	Input 17.5/M, Output 87.5/M	1.75/0 per 1M	47%	Input 5/M, Output 25/M
Anthropic	claude-opus-4-5-20251101-t	200K	Input 7.5/M, Output 37.5/M	0.75/0 per 1M	20%	Input 5/M, Output 25/M
Anthropic	claude-opus-4-1-20250805-t	200K	Input 22.5/M, Output 112.5/M	2.25/0 per 1M	20%	Input 15/M, Output 75/M
Anthropic	claude-opus-4-20250514	200K	Input 52.5/M, Output 262.5/M	5.25/0 per 1M	47%	Input 15/M, Output 75/M
Anthropic	claude-fable-5	1M	Input 35/M, Output 175/M	3.5/0 per 1M	47%	Input 10/M, Output 50/M
chat-server-video	veo-3.1-standard	0K	Input 0.8, Output 0	-	-	Input 0, Output 0
chat-server-video	veo-3.1-fast	0K	Input 0.6, Output 0	-	-	Input 0, Output 0
chat-server-video	seedance-2-5	0K	Input 0, Output 0	-	-	Input 0, Output 0
chat-server-video	seedance-2-0-fast	0K	Input 0, Output 0	-	-	Input 0, Output 0
chat-server-video	seedance-2-0	0K	Input 0, Output 0	-	-	Input 0, Output 0
chat-server-video	grok-imagine-video-1.5-preview	0K	Input 0, Output 0	-	-	Input 0, Output 0
DeepSeek	deepseek-v4-pro-0813	1M	Input 4.82/M, Output 14.45/M	0.16/0 per 1M	49%	Input 1.32/M, Output 3.96/M
DeepSeek	deepseek-v4-pro	1M	Input 4.82/M, Output 14.45/M	0.16/0 per 1M	49%	Input 1.32/M, Output 3.96/M
DeepSeek	deepseek-v4-flash-0731	1M	Input 1.61/M, Output 4.82/M	0.05/0 per 1M	50%	Input 0.44/M, Output 1.32/M
DeepSeek	deepseek-v4-flash-preview	1M	Input 1.61/M, Output 4.82/M	0.05/0 per 1M	50%	Input 0.44/M, Output 1.32/M
DeepSeek	deepseek-v4-flash	1M	Input 1.61/M, Output 4.82/M	0.05/0 per 1M	50%	Input 0.44/M, Output 1.32/M
DeepSeek	deepseek-v3.2	128K	Input 1.568/M, Output 2.352/M	0.1568/0 per 1M	76%	Input 0.28/M, Output 0.42/M
GLM	glm-5.3-flash	1M	Input 0.2/M, Output 0.56/M	0.046/0 per 1M	15%	Input 0.15/M, Output 0.5/M
GLM	glm-5.2-fast	1M	Input 3/M, Output 8.4/M	0.3/0 per 1M	17%	Input 2.1/M, Output 6.6/M
GLM	glm-5.2	1M	Input 2/M, Output 5.6/M	0.2/0 per 1M	17%	Input 1.4/M, Output 4.4/M
GLM	glm-5.1	200K	Input 2/M, Output 5.6/M	0.2/0 per 1M	17%	Input 1.4/M, Output 4.4/M
GLM	glm-4.7	200K	Input 1.6/M, Output 7/M	0.16/0 per 1M	54%	Input 0.4/M, Output 1.75/M
Google	gemini-3.1-flash-image-preview	200K	Input 0.1, Output 0.1	-	6%	Input 0.2, Output 0.2
Google	gemini-3-pro-image-preview-openrouter	256K	Input 0.2, Output 0.2	-	6%	Input 0.4, Output 0.4
Google	gemini-3.1-flash-lite-image	200K	Input 0.12, Output 0.12	-	5%	Input 0.3, Output 0.3
Google	gemini-3-pro-image-preview	256K	Input 0.2, Output 0.2	-	6%	Input 0.4, Output 0.4
Google	gemini-2.5-flash-image	32K	Input 0.1, Output 0.1	-	6%	Input 0.2, Output 0.2
Google	imagen-4.0-ultra-generate-preview-06-06	100K	Input 0.2, Output 0.2	-	2%	Input 1, Output 1
Google	imagen-4.0-generate-preview-06-06	100K	Input 0.1, Output 0.1	-	1%	Input 1, Output 1
Google	gemini-3.7-flash	1M	Input 1.875/M, Output 9.375/M	0.1875/0 per 1M	34%	Input 0.75/M, Output 3.75/M
Google	gemini-3.6-flash	1M	Input 1.875/M, Output 9.375/M	0.1875/0 per 1M	34%	Input 0.75/M, Output 3.75/M
Google	gemini-3.5-flash	1M	Input 3.75/M, Output 22.5/M	0.375/0 per 1M	34%	Input 1.5/M, Output 9/M
Google	gemini-3.1-pro-preview-vertex	1M	Input 3.2/M, Output 19.2/M	0.32/0 per 1M	21%	Input 2/M, Output 12/M
Google	gemini-3.1-pro-preview	1M	Input 5/M, Output 30/M	0.5/0 per 1M	34%	Input 2/M, Output 12/M
Google	gemini-3.1-flash-lite-preview	1M	Input 0.625/M, Output 3.75/M	0.0625/0 per 1M	34%	Input 0.25/M, Output 1.5/M
Google	gemini-3-pro-preview-vertex	1M	Input 3.2/M, Output 19.2/M	0.32/0 per 1M	21%	Input 2/M, Output 12/M
Google	gemini-3-pro-preview-thinking	1M	Input 5/M, Output 30/M	0.5/0 per 1M	34%	Input 2/M, Output 12/M
Google	gemini-3-pro-preview	1M	Input 5/M, Output 30/M	0.5/0 per 1M	34%	Input 2/M, Output 12/M
Google	gemini-3-flash-preview	1M	Input 1.25/M, Output 7.5/M	0.125/0 per 1M	34%	Input 0.5/M, Output 3/M
Google	gemini-2.5-pro-vertex	1M	Input 2/M, Output 16/M	0.2/0 per 1M	21%	Input 1.25/M, Output 10/M
Google	gemini-2.5-pro	1M	Input 3.125/M, Output 25/M	0.3125/0 per 1M	34%	Input 1.25/M, Output 10/M
Google	gemini-2.5-flash	1M	Input 0.75/M, Output 6.25/M	0.075/0 per 1M	34%	Input 0.3/M, Output 2.5/M
Kimi	kimi-k3	1M	Input 16.8/M, Output 84/M	1.68/0 per 1M	76%	Input 3/M, Output 15/M
Kimi	kimi-k2.7-code	262K	Input 1.9/M, Output 8/M	0.19/0 per 1M	27%	Input 0.95/M, Output 4/M
Kimi	kimi-k2.6	262K	Input 1.9/M, Output 8/M	0.19/0 per 1M	27%	Input 0.95/M, Output 4/M
Kimi	kimi-k2.5	262K	Input 1.8/M, Output 8.8/M	0.18/0 per 1M	54%	Input 0.45/M, Output 2.2/M
Kimi	kimi-k2-thinking	256K	Input 2.4/M, Output 10/M	0.24/0 per 1M	54%	Input 0.6/M, Output 2.5/M
MiniMax	minimax-m3	1M	Input 2.4/M, Output 9.6/M	0.24/0 per 1M	54%	Input 0.6/M, Output 2.4/M
MiniMax	minimax-m2.7	205K	Input 1.2/M, Output 4.8/M	0.12/0 per 1M	54%	Input 0.3/M, Output 1.2/M
MiniMax	minimax-m2.5	205K	Input 1.2/M, Output 4.8/M	0.12/0 per 1M	54%	Input 0.3/M, Output 1.2/M
OpenAI	o4-mini	200K	Input 1.1/M, Output 4.4/M	0.11/0 per 1M	13%	Input 1.1/M, Output 4.4/M
OpenAI	o3-mini	200K	Input 1.1/M, Output 4.4/M	0.11/0 per 1M	13%	Input 1.1/M, Output 4.4/M
OpenAI	o3	200K	Input 4/M, Output 16/M	0.4/0 per 1M	27%	Input 2/M, Output 8/M
OpenAI	horizon-beta	256K	Input 0.01, Output 0.01	-	1%	Input 0.1, Output 0.1
OpenAI	gpt-image-2	128K	Input 0.15, Output 0.15	-	13%	Input 0.15, Output 0.15
OpenAI	gpt-5.6-terra	1.05M	≤272K: Input 1.6/M, Output 9.6/M; >272K: Input 3.2/M, Output 19.2/M	≤272K: 0.16/0; >272K: 0.32/0 per 1M	10%	≤272K: Input 2/M, Output 12/M; >272K: Input 4/M, Output 24/M
OpenAI	gpt-5.6-sol	1.05M	≤272K: Input 4/M, Output 24/M; >272K: Input 8/M, Output 36/M	≤272K: 0.4/0; >272K: 0.8/0 per 1M	10%	≤272K: Input 5/M, Output 30/M; >272K: Input 10/M, Output 45/M
OpenAI	gpt-5.6-luna	1.05M	≤272K: Input 0.16/M, Output 0.96/M; >272K: Input 0.32/M, Output 1.44/M	≤272K: 0.016/0; >272K: 0.032/0 per 1M	10%	≤272K: Input 0.2/M, Output 1.2/M; >272K: Input 0.4/M, Output 1.8/M
OpenAI	gpt-5.5	1.05M	Input 4/M, Output 24/M	0.4/0 per 1M	10%	Input 5/M, Output 30/M
OpenAI	gpt-5.4-nano	400K	Input 0.16/M, Output 1/M	0.016/0 per 1M	10%	Input 0.2/M, Output 1.25/M
OpenAI	gpt-5.4-mini	400K	Input 0.6/M, Output 3.6/M	0.06/0 per 1M	10%	Input 0.75/M, Output 4.5/M
OpenAI	gpt-5.4	1.05M	Input 2/M, Output 12/M	0.2/0 per 1M	10%	Input 2.5/M, Output 15/M
OpenAI	gpt-5.2	400K	Input 3.5/M, Output 28/M	0.35/0 per 1M	27%	Input 1.75/M, Output 14/M
OpenAI	gpt-5-codex	400K	Input 2.5/M, Output 20/M	0.25/0 per 1M	27%	Input 1.25/M, Output 10/M
OpenAI	gpt-5-chat-latest	128K	Input 4/M, Output 16/M	0.4/0 per 1M	27%	Input 2/M, Output 8/M
OpenAI	gpt-5-mini	400K	Input 0.5/M, Output 4/M	0.05/0 per 1M	27%	Input 0.25/M, Output 2/M
OpenAI	gpt-5	400K	Input 2.5/M, Output 20/M	0.25/0 per 1M	27%	Input 1.25/M, Output 10/M
OpenAI	gpt-4o-mini-free	8K	Input 0.01/M, Output 0.01/M	0.001/0 per 1M	0%	Input 0.15/M, Output 0.6/M
OpenAI	gpt-4o-mini-2024-07-18	128K	Input 0.3/M, Output 1.2/M	0.03/0 per 1M	27%	Input 0.15/M, Output 0.6/M
OpenAI	gpt-4o-mini	128K	Input 0.3/M, Output 1.2/M	0.03/0 per 1M	27%	Input 0.15/M, Output 0.6/M
OpenAI	gpt-4o-free	8K	Input 0.01/M, Output 0.01/M	0.001/0 per 1M	13%	Input 0.01/M, Output 0.01/M
OpenAI	gpt-4o-2024-11-20	128K	Input 5/M, Output 20/M	0.5/0 per 1M	27%	Input 2.5/M, Output 10/M
OpenAI	gpt-4o-2024-08-06	128K	Input 5/M, Output 20/M	0.5/0 per 1M	27%	Input 2.5/M, Output 10/M
OpenAI	gpt-4o-2024-05-13	128K	Input 10/M, Output 30/M	1/0 per 1M	27%	Input 5/M, Output 15/M
OpenAI	gpt-4o-2024-11-20	128K	Input 5/M, Output 20/M	0.5/0 per 1M	27%	Input 2.5/M, Output 10/M
OpenAI	gpt-4.1-nano	1M	Input 0.7/M, Output 2.8/M	0.07/0 per 1M	95%	Input 0.1/M, Output 0.4/M
OpenAI	gpt-4.1-nano-2025-04-14	1M	Input 0.7/M, Output 2.8/M	0.07/0 per 1M	95%	Input 0.1/M, Output 0.4/M
OpenAI	gpt-4.1-mini	1M	Input 2.8/M, Output 11.2/M	0.28/0 per 1M	95%	Input 0.4/M, Output 1.6/M
OpenAI	gpt-4.1-mini-2025-04-14	1M	Input 2.8/M, Output 11.2/M	0.28/0 per 1M	95%	Input 0.4/M, Output 1.6/M
OpenAI	gpt-4.1	1M	Input 4/M, Output 16/M	0.4/0 per 1M	27%	Input 2/M, Output 8/M
OpenAI	gpt-4.1-2025-04-14	1M	Input 4/M, Output 16/M	0.4/0 per 1M	27%	Input 2/M, Output 8/M
OpenAI	gpt-4-turbo-2024-04-09	128K	Input 20/M, Output 60/M	2/0 per 1M	27%	Input 10/M, Output 30/M
OpenAI	gpt-4-turbo	128K	Input 20/M, Output 60/M	2/0 per 1M	27%	Input 10/M, Output 30/M
qwen	qwen3.7-max	1M	Input 9.125/M, Output 27.375/M	0.9125/0 per 1M	50%	Input 2.5/M, Output 7.5/M
qwen	qwen3-max-2026-01-23	262K	Input 4/M, Output 16/M	0.4/0 per 1M	99%	Input 0.55/M, Output 2.21/M
x.ai	grok-4-6	500K	Input 2/M, Output 6/M	0.5/0 per 1M	13%	Input 2/M, Output 6/M
x.ai	grok-4-5	256K	Input 2/M, Output 6/M	0.2/0 per 1M	13%	Input 2/M, Output 6/M
x.ai	grok-4-fast	2M	Input 0.2/M, Output 0.5/M	0.02/0 per 1M	13%	Input 0.2/M, Output 0.5/M
x.ai	grok-3	128K	Input 1.2/M, Output 6/M	0.12/0 per 1M	5%	Input 3/M, Output 15/M
xAI	grok-imagine-image-quality	128K	Input 0.1, Output 0.1	-	13%	Input 0.1, Output 0.1
xAI	grok-imagine-image	128K	Input 0.06, Output 0.06	-	13%	Input 0.06, Output 0.06