GPT-3.5 Turbo 16k API16K context

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

What is GPT-3.5 Turbo 16k?

This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. Training data: up...

GPT-3.5 Turbo 16k on the release timeline

GPT-3.5 Turbo 16k 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 16k is cheaper than 30% of the 309 models in the catalog. It is served by 2 providers; the best combined rate at our last snapshot was ≈ $3.00 / $4.00 per 1M via OpenAI.

What GPT-3.5 Turbo 16k 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 16k vs alternatives

When not to use GPT-3.5 Turbo 16k

• Its 16K context is small — not ideal for large documents or big codebases.

Specs

How to call GPT-3.5 Turbo 16k

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-16k",
    messages=[{"role": "user", "content": "Hello"}],
    # supports tools=[...] and tool_choice
)
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-16k","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-16k",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes GPT-3.5 Turbo 16k and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for GPT-3.5 Turbo 16k

• 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 16k follows concrete instructions better than abstract ones.
• Give it real tools. Pass a tools=[...] schema instead of asking it to "pretend" — let GPT-3.5 Turbo 16k emit tool calls, execute them, and feed results back for the next turn.
• 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 16k's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for GPT-3.5 Turbo 16k

Popular open-source frameworks and agents to build with GPT-3.5 Turbo 16k 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 16k

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-16k. Messages, streaming, tool calls 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; because GPT-3.5 Turbo 16k is served by 2 providers, requests can fail over to a healthy one automatically. See the error-code guide and failover setup.

Related reading

Frequently asked questions