Ling-3.1-flash API
Ling-3.1-flash is a text-generation model from inclusionAI. In ServerNet, its chat route accepts conversation messages and returns a generated answer; the price separates input and output tokens.
AvailableToman per million tokens
The usage rate is fixed at request admission. Reported consumption determines the final usage amount.
- API model ID
- di-inclusionailing-31-flash-3ec2a3359f
- Model developer
- inclusionAI
- Service type
- Text chat
- Current availability
- Available
- Configured context limit
- 262,144 tokens
- Gateway output cap
- 4,096 tokens
Model features
Reasoning ✓
Tool calling ✓
Streaming ✓
What is Ling-3.1-flash?
Use the public identifier di-inclusionailing-31-flash-3ec2a3359f for Ling-3.1-flash in ServerNet. The gateway uses this identifier to select the configured route; the developer is inclusionAI.
Where to use Ling-3.1-flash
- Add Ling-3.1-flash to a support assistant that receives the relevant conversation as text.
- Test Ling-3.1-flash on summaries and extraction using your own documents, with a small output limit first.
- Compare answer quality, total token usage and cost per completed task before choosing Ling-3.1-flash for production.
Build your integration
Send system and user messages, set max_tokens and store X-Request-Id with your usage record. Stream only when this route supports it. Function calls describe actions for your application to run; the gateway does not execute your tools.
Inputs and service limits
The current chat route accepts text only and one completion per request. Images, audio and hosted paid tools are not enabled by the source model’s capabilities. The configured context and output limits may be below the developer’s maximum. Test language quality on your own dataset.
A request cost example
For 1,000 input tokens and 500 output tokens, without a cache discount. This is a catalogue estimate, not a fixed request fee. Reported consumption and the price frozen for your request determine the usage amount.
- Estimated usage, Toman
- 209
Connect to Ling-3.1-flash API
Enable ai:chat on your key and ai:models:read to check availability. Set SERVERNET_API_KEY in your server environment. Change the idempotency key for each new request; keep it unchanged only for an identical retry.
curl https://servernet.cloud/v1/chat/completions \
-H "Authorization: Bearer $SERVERNET_API_KEY" \
-H 'Content-Type: application/json' \
-H 'Idempotency-Key: CHANGE_FOR_EACH_NEW_REQUEST' \
-d '{
"model": "di-inclusionailing-31-flash-3ec2a3359f",
"messages": [
{
"role": "user",
"content": "Hello"
}
],
"max_tokens": 128
}'
Use the same Idempotency-Key when retrying an identical request. Successful responses are replayed without a second charge for 24 hours. Do not automatically retry when x-should-retry:false is present. The key is shown once. Store it in a server environment variable. Keep it out of browser code, GitHub and logs.
Keys, budgets and usage reportsFrequently asked questions
Is Ling-3.1-flash available through ServerNet?
Ling-3.1-flash is currently available on the configured route. Check GET /v1/models before sending a request: availability can change with provider capacity, catalogue freshness or account configuration.
Which model ID and API endpoint should I use for Ling-3.1-flash?
Use di-inclusionailing-31-flash-3ec2a3359f as the model field, the endpoint shown below and the matching key scope. ServerNet keys are project-specific; an SDK also needs https://servernet.cloud/v1 as its base URL.
How is Ling-3.1-flash usage charged?
Input and output tokens use separate tariffs. Reported reasoning tokens, where supported, count towards output consumption. Cached input receives a discount only when a verified cached tariff exists. The dashboard records consumption for each request.
What should I check before using Ling-3.1-flash in production?
The current chat route accepts text only and one completion per request. Images, audio and hosted paid tools are not enabled by the source model’s capabilities. The configured context and output limits may be below the developer’s maximum. Test language quality on your own dataset.
Compare models for the same task
claude-fable-5
Available Text chatConfigured context limit: 4,096 tokens
claude-fable-5
Available Text chatConfigured context limit: 1,000,000 tokens
claude-fable-5-1
Available Text chatConfigured context limit: 4,096 tokens
claude-haiku-4-5
Available Text chatConfigured context limit: 200,000 tokens
claude-haiku-4-5-20251001
Available Text chatConfigured context limit: 4,096 tokens
claude-haiku-5-5
Available Text chatConfigured context limit: 4,096 tokens