Zolemba za API
Maziko a Nzeru Yopanda Zoletsa
API ya AI yopanda zoletsa: ma model a Shannon opanda kukana komanso opanda ma filter, ogwira ntchito pa ma GPU athu. Yogwirizana ndi OpenAI ndi Anthropic — ikani SDK yomwe mukugwiritsa kale, kiyi imodzi, streaming, tool calling ndi reasoning pa endpoint iliyonse.
- 22 ma model opanda zoletsa
- 256K context window
- kuyambira $0.50/1M
- 3 ma API ogwirizana
- 3 ma network: TLS, Tor, I2P
Yambani kumanga Yesani Playground
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://api.shannon-ai.com/v1"
)
response = client.chat.completions.create(
model="shannon-3",
messages=[
{"role": "user", "content": "Hello, Shannon!"}
],
max_tokens=1024
)
print(response.choices[0].message.content) import OpenAI from 'openai';
const client = new OpenAI({
apiKey: 'YOUR_API_KEY',
baseURL: 'https://api.shannon-ai.com/v1'
});
const response = await client.chat.completions.create({
model: 'shannon-3',
messages: [
{ role: 'user', content: 'Hello, Shannon!' }
],
max_tokens: 1024
});
console.log(response.choices[0].message.content); package main
import (
"context"
"fmt"
openai "github.com/sashabaranov/go-openai"
)
func main() {
config := openai.DefaultConfig("YOUR_API_KEY")
config.BaseURL = "https://api.shannon-ai.com/v1"
client := openai.NewClientWithConfig(config)
resp, err := client.CreateChatCompletion(
context.Background(),
openai.ChatCompletionRequest{
Model: "shannon-3",
Messages: []openai.ChatCompletionMessage{
{Role: "user", Content: "Hello, Shannon!"},
},
MaxTokens: 1024,
},
)
if err != nil {
panic(err)
}
fmt.Println(resp.Choices[0].Message.Content)
} curl -X POST "https://api.shannon-ai.com/v1/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "shannon-3",
"messages": [
{"role": "user", "content": "Hello, Shannon!"}
],
"max_tokens": 1024
}' Ma model ndi mitengo PA MA TOKEN 1M
Malire, ma id ndi momwe mitengo imapangidwiraMa hosted open-weight model context ya ma token 262,144 pa id iliyonse; input ndi output zimawerengedwa mosiyana; id iliyonse imalandira zomwe model yomwe imatsanzira imalandira
- Yopanda zoletsa
DeepSeek-V4-Pro-0813
3BIT-REAPDeepSeek · 1.6T MoE · 49B active
Model yaikulu yoganiza mozama komanso yolemba zazitali.
Model idDeepSeek-V4-Pro-0813-3BIT-REAP- Context
- 256K
- Input
- Text yolowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Reasoning effort
- Prompt caching
- Streaming
- In / cached / out pa 1M
- $1.95 / $0.488 / $3.90
- Yopanda zoletsa
GLM-5.2
3BIT-REAPZ.ai · 744B MoE · 40B active
Model yamphamvu ya zolinga zambiri yokhala ndi output yolemera ya zilankhulo zambiri.
Model idGLM-5.2-3BIT-REAP- Context
- 256K
- Input
- Text yolowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Reasoning effort
- Prompt caching
- Streaming
- In / cached / out pa 1M
- $0.73 / $0.183 / $2.34
- Yopanda zoletsa
Kimi-K3
3BIT-REAPMoonshot AI · 2.8T MoE · 104B active
Model yapamwamba yokonzedwa kuti ipereke mayankho athunthu komanso atsatanetsatane.
Model idKimi-K3-3BIT-REAP- Context
- 256K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Reasoning effort
- Prompt caching
- Streaming
- In / cached / out pa 1M
- $3.83 / $0.958 / $19.12
- Yopanda zoletsa
Nemotron3Ultra
3BIT-REAPNVIDIA · 550B hybrid Mamba-Attention MoE · 55B active
Model yaikulu yogwira ntchito bwino ndi reasoning yamphamvu pa dola iliyonse.
Model idNemotron3Ultra-3BIT-REAP- Context
- 256K
- Input
- Text yolowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Reasoning effort
- Prompt caching
- Streaming
- In / cached / out pa 1M
- $0.75 / $0.188 / $3.30
- Yopanda zoletsa
MiniMax-M3
3BIT-REAPMiniMax · 428B MoE · 23B active · sparse attention
Model yachangu komanso yotsika mtengo ya ntchito zambiri.
Model idMiniMax-M3-3BIT-REAP- Context
- 256K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Reasoning effort
- Prompt caching
- Streaming
- In / cached / out pa 1M
- $0.50 / $0.125 / $2.00
- Yopanda zoletsa
DeepSeek-V4-Flash-0731
W4A16-AUTOROUND-REAPDeepSeek · 284B MoE · 13B active
Model yachangu ya DeepSeek yolinganiza liwiro ndi khalidwe.
Model idDeepSeek-V4-Flash-0731-W4A16-AUTOROUND-REAP- Context
- 256K
- Input
- Text yolowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Reasoning effort
- Prompt caching
- Streaming
- In / cached / out pa 1M
- $0.50 / $0.125 / $2.00
- Yopanda zoletsa
Kimi-K2.6
W4A16-AUTOROUND-REAPMoonshot AI · 1T MoE · 32B active
Model yokhoza pa reasoning ya tsiku ndi tsiku ndi kulemba madraft.
Model idKimi-K2.6-W4A16-AUTOROUND-REAP- Context
- 256K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Reasoning effort
- Prompt caching
- Streaming
- In / cached / out pa 1M
- $0.78 / $0.195 / $3.67
- Yopanda zoletsa
Laguna-S-2.1
W4A16-AUTOROUND-REAPPoolside · 118B MoE · 8B active
Model yotsika mtengo kwambiri ya ntchito zopepuka.
Model idLaguna-S-2.1-W4A16-AUTOROUND-REAP- Context
- 256K
- Input
- Text yolowa
- Structured output
- Palibe structured output
- Ma tool
- Reasoning
- Reasoning effort
- Prompt caching
- Streaming
- In / cached / out pa 1M
- $0.50 / $0.125 / $2.00
- Yopanda zoletsa
inkling
W4A16-AUTOROUND-REAPThinking Machines · 975B MoE · 41B active
Model yopanga zatsopano yokhala ndi kalembedwe kake kapadera.
Model idinkling-W4A16-AUTOROUND-REAP- Context
- 256K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON object
- Ma tool
- Reasoning
- Reasoning effort
- Prompt caching
- Streaming
- In / cached / out pa 1M
- $1.42 / $0.355 / $6.07
- Yopanda zoletsa
MiMo-V2.5-Pro
W8A16Xiaomi · 1.02T MoE · 42B active · 8-bit
Model yaying'ono ya pro ya output yokonzedwa komanso yolondola.
Model idMiMo-V2.5-Pro-W8A16- Context
- 256K
- Input
- Text yolowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Reasoning effort
- Prompt caching
- Streaming
- In / cached / out pa 1M
- $0.50 / $0.125 / $2.00
- Yopanda zoletsa
MiMo-V2.5
W8A16Xiaomi · 8-bit multimodal · W8A16
Model yaying'ono komanso yachangu ya ntchito zosavuta pa mtengo wochepa kwambiri.
Model idMiMo-V2.5-W8A16- Context
- 256K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Reasoning effort
- Prompt caching
- Streaming
- In / cached / out pa 1M
- $0.50 / $0.125 / $2.00
- Yopanda zoletsa
Hy3
W8A16Tencent · 295B MoE · 21B active · 8-bit
Model yotsika mtengo yoyankha mwachidule pa kukambirana.
Model idHy3-W8A16- Context
- 256K
- Input
- Text yolowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Reasoning effort
- Prompt caching
- Streaming
- In / cached / out pa 1M
- $0.50 / $0.125 / $2.00
Ma model a Shannon mtengo umodzi wokhazikika wa input ndi output
-
Yopanda zoletsaShannon 1.6 Lite
Mayankho othamanga komanso ogwira ntchito pa ntchito za tsiku ndi tsiku
Model idshannon-1.6-lite- Context
- 192K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON schema
- Ma tool
- Web search
- Streaming
- Mtengo / ma token 1M
- $3.90
-
Yopanda zoletsaShannon 1.6 Pro
Reasoning yapamwamba pa zovuta
Model idshannon-1.6-pro- Context
- 192K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Web search
- Streaming
- Mtengo / ma token 1M
- $7.80
-
Yopanda zoletsaShannon 2 Lite
Shannon 2 Lite: pipeline yachangu ya kuganiza-kulemba-kuunikanso ndi reasoning trace yooneka. Chat ya tsiku ndi tsiku, ma summary ndi Q&A.
Model idshannon-2-lite- Context
- 192K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Web search
- Streaming
- Mtengo / ma token 1M
- $3.90
-
Yopanda zoletsaShannon 2 Pro
Shannon 2 Pro: kuganiza-kulemba-kuunikanso mozama pa kusanthula, kulemba zazitali ndi reasoning ya masitepe ambiri.
Model idshannon-2-pro- Context
- 192K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Web search
- Streaming
- Mtengo / ma token 1M
- $5.85
-
Yopanda zoletsaShannon 3
Model ya Shannon ya m'badwo waposachedwa ndi reasoning yachangu pa mtengo wotsika wokhazikika.
Model idshannon-3- Context
- 192K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Web search
- Streaming
- Mtengo / ma token 1M
- $3.35
-
Yopanda zoletsaShannon 3 Pro
Gawo la Shannon 3 lamphamvu kwambiri pa ntchito zovuta za masitepe ambiri.
Model idshannon-3-pro- Context
- 192K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Web search
- Streaming
- Mtengo / ma token 1M
- $3.35
-
Yopanda zoletsaShannon 3.1
Shannon 3 pa hardware yathu, streaming pa liwiro lonse popanda rate gate.
Model idshannon-3.1- Context
- 192K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Web search
- Streaming
- Mtengo / ma token 1M
- $3.35
-
Yopanda zoletsaShannon 3.1 Pro
Reasoning yozama pa liwiro lonse ndi kafukufuku wakumbuyo pa mafunso ovuta.
Model idshannon-3.1-pro- Context
- 192K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Web search
- Streaming
- Mtengo / ma token 1M
- $3.35
-
Yopanda zoletsaShannon 3.5
One streaming answer that thinks, searches, draws charts and diagrams and writes files inline.
Model idshannon-3.5- Context
- 192K
- Input
- Text + chithunzi cholowa
- Structured output
- JSON schema
- Ma tool
- Streaming
- Mtengo / ma token 1M
- $3.35
-
Yopanda zoletsaShannon Coder 1
Katswiri wa code ndi ma tool: kusintha kwa agentic, ma file aatali ndi ma tool loop okhwima.
Model idshannon-coder-1- Context
- 128K
- Input
- Text yolowa
- Structured output
- JSON schema
- Ma tool
- Reasoning
- Streaming
- Mtengo / ma token 1M
- $8.00
Sakatulani zolemba
- Kuyamba mwachangu Pangani kiyi, lozani SDK yanu ku Shannon ndipo tumizani pempho loyamba.
- Ma model ndi mitengo Model id iliyonse ndi context window yake, ma input ndi mtengo pa ma token 1M.
- Kutsimikizira Kiyi imodzi ya endpoint iliyonse: Bearer header kapena x-api-key.
- Kutsitsa nthawi yeniyeni Server-sent events ndi ma reasoning delta; usage imafika mu chunk yomaliza.
- Kuyitana mafunkseni Fotokozani ma tool, lolani model kuwayitana, ndipo muiyendetse ndi tool_choice.
- Zotuluka zokonzedwa Mayankho ngati JSON object kapena motsatira JSON schema yanu.
- Reasoning effort Werengani reasoning stream; khazikitsani effort pa ma hosted open-weight model.
- Kusunga prompt Ma model osungidwa ndi ife amasunga okha ma prefix a prompt obwerezedwa, pa 25% ya mtengo wa input.
- Responses API Dialect ya OpenAI Responses pa ma model omwewo ndi kiyi yomweyo.
- Anthropic Sungani Anthropic SDK ndi code yanu ya Messages; sinthani base URL yokha.
- Zida za CLI zolembera code Claude Code ndi Codex CLI ndi Shannon ngati model yomwe ili kumbuyo kwawo.
- Zolakwika Ma status code, mitundu ya zolakwika ndi mawonekedwe a body ya cholakwika.
Wakonzeka kupanga?
Plan yaulere ikuphatikizidwa ndi akaunti iliyonse. Kiyi yanu ili pafupi kwambiri.