Phi-3-medium-4k-instruct API
Phi-3-medium-4k-instruct is a text-generation model from microsoft. In ServerNet, its chat route accepts conversation messages and returns a generated answer; the price separates input and output tokens.
DiscontinuedToman per million tokens
Sale prices will appear after provider configuration is complete.
- API model ID
- di-microsoftphi-3-medium-4k-instruct-a08fd4b664
- Model developer
- microsoft
- Service type
- Text chat
- Current availability
- Discontinued
- Configured context limit
- 4,096 tokens
- Gateway output cap
- 4,096 tokens
Model features
No additional gateway capabilities have been confirmed for this model.
What is Phi-3-medium-4k-instruct?
Use the public identifier di-microsoftphi-3-medium-4k-instruct-a08fd4b664 for Phi-3-medium-4k-instruct in ServerNet. The gateway uses this identifier to select the configured route; the developer is microsoft.
Where to use Phi-3-medium-4k-instruct
- Add Phi-3-medium-4k-instruct to a support assistant that receives the relevant conversation as text.
- Test Phi-3-medium-4k-instruct 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 Phi-3-medium-4k-instruct 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.
Connect to Phi-3-medium-4k-instruct 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.
This example documents the request format. Send it only after this model appears in GET /v1/models.
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-microsoftphi-3-medium-4k-instruct-a08fd4b664",
"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 Phi-3-medium-4k-instruct available through ServerNet?
Phi-3-medium-4k-instruct is currently unavailable for API use. A catalogue listing does not activate billing or access. Choose an available related model or check this page again after the route is verified.
Which model ID and API endpoint should I use for Phi-3-medium-4k-instruct?
Use di-microsoftphi-3-medium-4k-instruct-a08fd4b664 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 Phi-3-medium-4k-instruct 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 Phi-3-medium-4k-instruct 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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