GPT-3.5 Turbo Instruct API4K context

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

What is GPT-3.5 Turbo Instruct?

This model is a variant of GPT-3.5 Turbo tuned for instructional prompts and omitting chat-related optimizations. Training data: up to Sep 2021.

GPT-3.5 Turbo Instruct on the release timeline

GPT-3.5 Turbo Instruct pricing

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

On output price, GPT-3.5 Turbo Instruct is cheaper than 42% of the 309 models in the catalog.

What GPT-3.5 Turbo Instruct 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.

GPT-3.5 Turbo Instruct vs alternatives

When not to use GPT-3.5 Turbo Instruct

• It is served by a single provider, so there is less failover headroom during an outage.
• Its 4K context is small — not ideal for large documents or big codebases.

Specs

How to call GPT-3.5 Turbo Instruct

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="openai/gpt-3.5-turbo-instruct",
    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":"openai/gpt-3.5-turbo-instruct","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: "openai/gpt-3.5-turbo-instruct",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes GPT-3.5 Turbo Instruct and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for GPT-3.5 Turbo Instruct

• 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 — GPT-3.5 Turbo Instruct 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.
• Use the system role for rules. Put persistent instructions in the system message and keep user turns task-specific; pair with structured outputs for reliable automation.

Tips are derived from GPT-3.5 Turbo Instruct's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for GPT-3.5 Turbo Instruct

Popular open-source frameworks and agents to build with GPT-3.5 Turbo Instruct over the API — star counts pulled from GitHub (July 2026).

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

Migrating to GPT-3.5 Turbo Instruct

Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → openai/gpt-3.5-turbo-instruct. Messages, streaming and the rest of your code stay the same. Routing & failover guide.

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