inclusionAI · Text chat

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.

Available

Toman per million tokens

Input83,507
Output250,520

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 reports

Frequently 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.

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Only available models are exposed by the API. Availability requires a configured provider, valid prices and a supported execution path.