Service, Credit & Pricing
Last updated:2024-06-01

Service, Credit & Pricing

Service

GPTBots currently offers two different service modes, and customers can choose to use either the "GPTBots Key" or the "Own Key" according to their needs.

  • GPTBots Key: GPTBots officially provides the service directly, developers do not need to register for keys from platforms like OpenAI or Claude themselves, and can directly use their services on GPTBots;
  • Own Key: If developers have their own keys from platforms like OpenAI, they can also use them directly on the GPTBots platform, and GPTBots will charge a small amount of credits as a service fee.

You can choose and set the service mode you need within "Organization - LLMs".

Credit

All services within GPTBots are consumed using "credits".

Different services consume different amounts of credits, and the specific calculation of consumption is detailed below.

Note: Points are not refundable or exchangeable.

Pricing

LLM

Note: The following prices are measured in "credits / 1K Tokens".

Brand Model Iuput (Platform Key) Output (GPTBots Key) Iuput (Own Key) Output (Own Key)
OpenAI GPT-4o-mini-128k 0.0165 0.0665 0.0015 0.006
OpenAI GPT-3.5-turbo-16k 0.055 0.165 0.005 0.015
OpenAI GPT-4-8k 3.3 6.6 0.3 0.6
OpenAI GPT-4-turbo-128k 1.1 3.3 0.1 0.3
OpenAI GPT-4o-128k 0.55 1.65 0.05 0.15
Google Gemini-1.5-Flash 0.039 0.116 0.03 0.15
Google Gemini-1.5-Pro 0.385 1.106 0.035 0.106
Anthropic Claude-3.0-Haiku-200k 0.028 0.138 0.003 0.013
Anthropic Claude-3.0-Sonnet-200k 0.33 1.65 0.03 0.15
Anthropic Claude-3.0-Opus-200k 1.65 8.25 0.15 0.75
Azure GPT-3.5-turbo-16k 0.055 0.165 0.005 0.015
Azure GPT-4-8k 3.3 6.6 0.3 0.6
Azure GPT-4-32k 6.6 13.2 0.6 1.2
Meta llama-3.0-8b-8k 0.028 0.028 0.002 0.002
Meta llama-3.0-70b-8k 0.099 0.099 0.009 0.009
Mixtral open-mistral-7b 0.028 0.028 0.003 0.003
Mixtral open-mixtral-8x7b 0.077 0.077 0.007 0.007
Mixtral mistral-small-latest 0.220 0.660 0.020 0.060
Mixtral mistral-medium-latest 0.297 0.891 0.027 0.081
Mixtral mistral-large-latest 0.880 2.640 0.080 0.240
Tencent Hunyuan-lite- 4k free free free free
Tencent Hunyuan-standard-32k 0.0707 0.0786 0.0064 0.0071
Tencent Hunyuan-standard-256k 0.2357 0.9429 0.0214 0.0857
Tencent Hunyuan-pro-32k 0.472 1.572 0.042 0.142
Ali Qwen-turbo-8k 0.13 0.13 0.01 0.01
Ali Qwen-plus-32k 0.32 0.32 0.02 0.02
Ali Qwen-max-8k 1.88 1.88 0.17 0.17
Ali Qwen-max-longcontext-30k 1.88 1.88 0.17 0.17
Ali Qwen1.5-72b-32k 0.32 0.32 0.02 0.02
Ali Qwen1.5-14b-8k 0.13 0.13 0.01 0.01
Ali Qwen1.5-7b-8k 0.095 0.095 0.008 0.008
Baidu ERNIE-4.0-8K 1.76 1.76 0.16 0.16
Baidu ERNIE-3.5-8K 0.18 0.18 0.02 0.02
Baidu ERNIE-Speed-128K free free free free
ZhiPu GLM-3.0-6B-8K 0.095 0.095 0.008 0.008
ZhiPu GLM-3.0-Turbo-128k 0.017 0.08 0.0015 0.0015
ZhiPu GLM-4.0-128K 1.56 1.56 0.14 0.14

Embedding

Note: The following prices are measured in "credits / 1K Tokens".

Brand
Model
GPTBots Key
Own Key
OpenAI text-embedding-ada-002 0.0120 0.0010
OpenAI text-embedding-3-large 0.0156 0.0013
OpenAI text-embedding-3-small 0.0024 0.0002

Rerank

Note: The following prices are measured in "credits / 1K Tokens".

Brand
Model
GPTBots Key
Own Key
Jina reranker-v1-base-en 0.0022 0.0001
Jina reranker-v1-turbo-en 0.0022 0.0001
Jina reranker-v1-tiny-en 0.0022 0.0001
Baai bce-rerank 0.0022 0.0001
NteEase bgep-rerank 0.0022 0.0001

ASR

Note: The following prices are measured in "credits / 60 secs".

Brand
Model
GPTBots Key
Own Key
OpenAI Whisper Large-V2 0.66 0.06
OpenAI Whisper Large-V3 0.88 0.08

TTS

Note: The following prices are measured in "credits / 1000 chars".

Brand
Model
Platform Key
Own Key
OpenAI TTS 1.65 0.15
Ali Sambert 1.56 0.14

Vector Storage

Note: The following prices are measured in "credits / 1K Tokens/ day".

Service
Charge
Vector Storage 0.001

FAQ

Credits and Characters, how are they converted?

Taking the service of OpenAI's large language model (LLM) ChatGPT-3.5-turbo-16K (input) as an example:

$$ 1000 credits ≈ 2.5 million English words ≈ 1.25 million Chinese characters/Japanese kana/Korean Hangul $$

How are credits calculated when using image inputs?

When using the "GPT" model with image content, the calculation method for image Tokens is as follows:

  1. Obtain the length and width values in "px", for example: "1024px * 1024px".
  2. Calculate the "Tiles" value, which is obtained by dividing both the width and height by 512, rounding up, and then multiplying the two resulting values.
  3. Calculate the "Tokens", using the formula "85 + 170 * Tiles".

The complete calculation formula is as follows:

$$ Tiles = ⌈(width÷512)⌉×⌈(height÷512)⌉ $$

$$ Tokens = 85 + 170 × Tiles $$

Python code is as follows:

import math def calculate_tokens(width, height): tiles = math.ceil(width/512) * math.ceil(height/512) tokens = 85 + 170 * tiles return tokens # Test print(calculate_tokens(2000, 500))
          import math

def calculate_tokens(width, height):
    tiles = math.ceil(width/512) * math.ceil(height/512)
    tokens = 85 + 170 * tiles
    return tokens

# Test
print(calculate_tokens(2000, 500))

        
This code block in the floating window

For example, if the input image dimensions are 2000px * 500px, then its Tiles value is 4*1=4, and the input Tokens for that image would be 85 + 170 * 4 = 765.