Price Per TokenPrice Per Token
OpenAI

GPT 3.5 Turbo 16k API Pricing 2026

Compare pricing, benchmarks, and providers for GPT 3.5 Turbo 16k. Find the best value for your use case.

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Last updated: February 15, 2026 at 08:07 AM

Overview

GPT 3.5 Turbo 16k was released on August 28, 2023. Pricing starts at $3.00 per million input tokens and $4.00 per million output tokens. The model supports a context window of up to 16K tokens. API access is available through OpenAI.

Context Window
16K
tokens
Pricing
Input$3.00
Output$4.00
per 1M tokens
Speed
Output-
TTFT-
median latency
Capabilities
Text only

Pricing Comparison

Compare GPT 3.5 Turbo 16k with 10 similar models by price.

Current Pricing (per 1M tokens)

11 models

Provider
Model
Input $/M
Output $/M
Coding
MMLU
GPQA
Context
Actions
$3.000
$4.000
16,385
Try
$2.500
$2.000
400,000
Try
$2.500
$10.000
128,000
Try
$2.500
$10.000
28.7
71.2
52.7
256,000
Try
$2.500
$10.000
128,000
Try
$2.500
$10.000
8,000
Try
$2.500
$10.000
8,000
Try
$2.500
$12.500
31.7
73.3
56.9
1,000,000
Try
$2.500
$10.000
31.7
52.1
128,000
Try
$3.000
$15.000
131,072
Try
$3.000
$15.000
200,000
Try

* Some models use tiered pricing based on prompt length. Displayed prices are for prompts ≤ 200k tokens.

Compare Providers

GPT 3.5 Turbo 16k is available from multiple providers with different pricing and availability.

2
Providers
$3.000
Cheapest Input/M
$4.000
Cheapest Output/M
OpenAI
Best Price

Provider Pricing

2 models

Provider
Input $/M
Output $/M
Uptime
OpenAI
OpenAI
Best Price
$3.000
$4.000
-
A
Azure
Best Price
$3.000
$4.000
-
Provider pricing data sourced from OpenRouter and Helicone

Community Rankings

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Cost vs. Quality

Compare GPT 3.5 Turbo 16k's benchmark performance against all models.

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Frequently Asked Questions

GPT 3.5 Turbo 16k costs $0.003000 per 1,000 input tokens and $0.004000 per 1,000 output tokens.
GPT 3.5 Turbo 16k is available from 2 provider(s). OpenAI offers the best price.
We compare GPT 3.5 Turbo 16k with 10 similarly-priced models. See the benchmark scatter plot above to compare quality vs cost.

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