Phi 4 APIOPEN WEIGHTS16K context

Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).

What is Phi 4?

[Microsoft Research](/microsoft) Phi-4 is designed to perform well in complex reasoning tasks and can operate efficiently in situations with limited memory or where quick responses are needed. At 14 billion...

Phi 4 pricing

Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):

On output price, Phi 4 is cheaper than 94% of the 309 models in the catalog.

What Phi 4 costs per month

Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.

Phi 4 vs alternatives

When not to use Phi 4

• It is served by a single provider, so there is less failover headroom during an outage.
• Its 16K context is small — not ideal for large documents or big codebases.

Specs

How to call Phi 4

Copyfrom openai import OpenAI
client = OpenAI(base_url="https://api.datallmlab.com/v1", api_key="YOUR_DATALLM_LAB_KEY")
resp = client.chat.completions.create(
    model="microsoft/phi-4",
    messages=[{"role": "user", "content": "Hello"}],
)
print(resp.choices[0].message.content)

Copycurl https://api.datallmlab.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"microsoft/phi-4","messages":[{"role":"user","content":"Hello"}]}'

Copyimport OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.datallmlab.com/v1", apiKey: process.env.DATALLM_KEY });
const r = await client.chat.completions.create({
  model: "microsoft/phi-4",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes Phi 4 and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for Phi 4

• Be explicit and show the shape of the answer. Spell out format, tone and constraints up front, and include one short example of the output you want — Phi 4 follows concrete instructions better than abstract ones.
• Constrain JSON with a schema. Use response_format / structured outputs rather than "reply in JSON" to get valid, parseable objects every time.
• Cheap enough to batch. Great for high-volume classification, extraction and drafting — template the prompt, batch requests, and validate outputs programmatically.

Tips are derived from Phi 4's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for Phi 4

Popular open-source projects for running and building with Phi 4 — star counts pulled from GitHub (July 2026).

Listed by GitHub stars; inclusion is by ecosystem relevance (inference engines, agent frameworks and SDKs), not affiliation. Stars change — see each repo for current numbers.

Migrating to Phi 4

Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → microsoft/phi-4. Messages, streaming and the rest of your code stay the same. Routing & failover guide.

Self-host or use the API?

Phi 4 ships open weights (microsoft/phi-4, ~855.5K downloads and 2.3K likes in the last 30 days), with community quantizations (bf16) for smaller GPUs, so you can run it yourself. Most teams still use the API: no GPU to provision or keep warm, no inference ops, and instant access across 1 providers with automatic failover. Self-host when data residency or fixed per-token economics matter most.

Rate limits & reliability

Rate limits here are the DataLLM Lab gateway's, not the upstream vendor's. On 429 (rate limited) or 503 (provider busy), retry with exponential backoff. See the error-code guide and failover setup.

Related reading

Frequently asked questions