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OpenAI: GPT-3.5 Turbo 16k
OpenAI
•
gpt-3.5-turbo-16k
•
Released 
June 2023

OpenAI
gpt-3.5-turbo-16k

Extended 16K-context variant of GPT-3.5 Turbo, built for summarizing and processing longer documents in a single request.

Modality:
Text
model ID
openai/gpt-3.5-turbo-16k
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Output Speed

N/A
tok/s

Intelligence Index

N/A
/ 100

Context Window

16385
tokens

Input price

18
Anytoken

Output price

24
Anytoken
GPT-3.5 Turbo 16k: Four Times the Context of Base GPT-3.5 GPT-3.5 Turbo 16k is OpenAI's extended-context variant of GPT-3.5 Turbo, released in June 2023. It quadrupled the original 4K context to roughly 16K tokens, letting a single request hold long documents, transcripts, and multi-turn histories. Positioned as a fast, low-cost workhorse rather than a reasoning model, it was widely used for summarization and lightweight RAG. Note the dated snapshot (gpt-3.5-turbo-16k-0613) was deprecated by OpenAI, with the 16K window later folded into the standard gpt-3.5-turbo. Treat this page as a technical reference and migration guide. Integrate GPT-3.5 Turbo 16k and its successors through one API on AnyAPI.ai.

Performance

Why the 16K Window Mattered for GPT-3.5

GPT-3.5 Turbo 16k's defining strength was capacity, not intelligence. Extending the context to roughly 16,385 tokens let a single call absorb about 20 pages of text, enabling document summarization and longer chat histories that the original 4K model could not hold. In practice this made it a fast, inexpensive engine for high-volume text tasks. The trade-off is real: independent testing places its reasoning and coding near the bottom of current models, so it suits throughput-driven pipelines rather than accuracy-critical work. For any new build, a modern small model now delivers more capability at lower cost.

Benchmarks

How GPT-3.5 Turbo 16k Scores Against Modern Models

Independent evaluation from Artificial Analysis is unambiguous about where GPT-3.5 sits today. <cite index="13-17">GPT-3.5 Turbo scores 3 on the Artificial Analysis Intelligence Index, placing it at the lower end among comparable models (median: 12).</cite> <cite index="13-15">GPT-3.5 Turbo is among the least intelligent models and particularly expensive when comparing to other non-reasoning models of similar price.</cite> This composite covers reasoning, knowledge, mathematics, and coding. The interpretation for developers is direct: the 16K context is the only dimension where this model was competitive, and newer small models outperform it on intelligence per dollar across the board.

Output Speed

N/A
tok/s

Intelligence Index

N/A
/ 100

MMLU

Broad world knowledge and problem-solving
0
%

GPQA

PhD-level scientific reasoning across physics, biology, chemistry.
0
%

HLE

Adherence to multi-step structured instructions.
0
%

LiveCodeBench

Tool-calling reliability in long agentic loops.
0
%

Technical Specifications

What the model supports

GPT-3.5 Turbo 16k is text-in, text-out only, with no image, audio, or document parsing. <cite index="11-2">It has a 16,385 token context window and a maximum output of 4,096 tokens.</cite> The most important production detail is the output cap: even with a large input window, generations are limited to about 4K tokens, so long-document workflows must chunk or stream output. It supports function calling and streaming through the chat completions endpoint. <cite index="22-2">As of June 17, 2024, only existing users of the -0613 snapshot could continue using it,</cite> making version pinning a live concern.
Verified Specifications — 
gpt-3.5-turbo-16k
Input modalities
Text
output modalities
Text
Context window
16385
 tokens
Maximum output tokens
4096
Reasoning
No
Knowledge cutoff
June 2023
Pricing (standard)
18
 AnyTokens in
 / 
24
 AnyTokens out

Limitations & Trade-offs

Where gpt-3.5-turbo-16k falls short

1
Deprecated dated snapshot. The gpt-3.5-turbo-16k-0613 snapshot has been retired by OpenAI. <cite index="24-1">Back in June 2023 and November 2023, OpenAI announced that gpt-3.5-turbo-0301, gpt-3.5-turbo-0613 and gpt-3.5-turbo-16k-0613 would be deprecated on June 13th, 2024.</cite> This matters because production systems pinned to the explicit 16k model ID face forced migration. Teams relying on the standalone endpoint should move to gpt-3.5-turbo or a modern small model before decommissioning.
2
Low intelligence versus modern models. On independent evaluation the model sits near the bottom of the current field. <cite index="13-15">GPT-3.5 Turbo is among the least intelligent models and particularly expensive when comparing to other non-reasoning models of similar price.</cite> For any workload involving reasoning, coding, math, or nuanced instruction following, a newer small model will produce materially better output, making GPT-3.5 a poor default for accuracy-sensitive tasks.
3
Output capped at 4K tokens. Despite the 16K input window, <cite index="11-2">the model has a maximum output of 4,096 tokens.</cite> Long inputs cannot produce proportionally long outputs, so summarizing a large document into a detailed multi-section report requires chunking or multiple calls. Applications that need long-form generation should prefer a model with a higher output ceiling.
4
Dated knowledge and text-only. The model's training data stops at September 2021 and it accepts no images, audio, or files. For questions about recent events or any multimodal input, it is unusable without external retrieval. Modern small models add image input and later cutoffs, removing this constraint for most current applications.

Best-Fit Workloads

Where this model earns its place

01

Long-document summarization

‍
The 16K window was built for exactly this: condensing multi-page documents, meeting transcripts, or articles into short summaries in one request. Early testing showed it handling 12,000+ word documents successfully. It remains functional for cost-sensitive batch summarization pipelines already built on it, though output length is limited near 4K tokens and modern models summarize more accurately.

02

Lightweight RAG

‍
The extended context allowed retrieved passages plus a query to fit in a single call, which made GPT-3.5 Turbo 16k a common choice for early retrieval-augmented generation. It suits simple factual lookups over small retrieved chunks where speed and cost matter more than reasoning depth. For grounded answers requiring synthesis across many documents, a higher-intelligence model reduces hallucination risk.

03

High-volume chat and classification

‍
As a fast, low-cost text engine, the model fits high-throughput tasks like ticket classification, tagging, and basic conversational responses where each call is short and simple. Function calling supports routing to downstream tools. Its low intelligence score means edge cases and ambiguous inputs are handled poorly, so complex or safety-sensitive classification should use a stronger model.

04

Legacy pipeline maintenance

‍
For teams with existing production systems validated against GPT-3.5 behavior, the model remains a known quantity for maintenance and controlled migration testing. It is useful as a baseline when benchmarking replacement models. Given the deprecation of dated snapshots, treat any such use as transitional rather than a long-term commitment.

Pricing in anytokens via AnyAPI
Input
18
₳
Output
24
₳
Cache write
—
₳
Cache read
—
₳
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PerformanceBenchmarksTechnical SpecificationsLimitations & Trade-offsBest-Fit WorkloadsIntegrationFAQ

Integration

Access gpt-3.5-turbo-16k via AnyAPI.ai

Access gpt-3.5-turbo-16k through AnyAPI.ai using a unified API built for multi-model AI applications. Integrate gpt-3.5-turbo-16k without maintaining a separate provider-specific connection, and keep the flexibility to test, switch, or combine models as your application requirements evolve.

01

One API integration

Access gpt-3.5-turbo-16k and other AI models through the same API workflow instead of maintaining separate integrations for every provider.

02

Easy model switching

Test gpt-3.5-turbo-16k against alternative models or switch models as your performance, capability, or cost requirements change without rebuilding your application around another provider API.

03

Flexible for production

Use gpt-3.5-turbo-16k from experimentation through production while keeping your AI stack flexible as workloads, traffic, and model requirements evolve.

04

Multi-model applications

Use gpt-3.5-turbo-16k for the workloads where it performs best and combine it with other models for tasks that require different capabilities, performance, or efficiency.

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Frequently Asked Questions

Answers to common questions about integrating and using this AI model via AnyAPI.ai

GPT-3.5 Turbo 16k has a context window of 16,385 tokens, roughly four times the original 4K GPT-3.5 Turbo. This lets a single request hold approximately 20 pages of text. However, maximum output is capped at 4,096 tokens, so the large input window does not translate into proportionally long generations.

The standalone dated snapshot gpt-3.5-turbo-16k-0613 was deprecated by OpenAI, with access restricted to existing users from June 17, 2024 and later decommissioned. The 16K context window was folded into the standard gpt-3.5-turbo model. New projects should not depend on the standalone 16k model ID and should plan migration to a modern small model.

No. GPT-3.5 Turbo 16k is text-only, accepting text input and producing text output. It cannot process images, audio, documents, or video. Applications needing image input should use a multimodal model such as GPT-4o mini, which OpenAI positions as the cheaper, more capable replacement.

Its strongest fit is long-document summarization, lightweight RAG, and high-volume text classification where speed and cost outweigh reasoning depth. The 16K window was its main advantage. Because it scores low on independent intelligence benchmarks, it is not suited to complex reasoning, coding, or accuracy-critical tasks, where modern small models perform substantially better.

OpenAI recommends GPT-4o mini as the direct replacement, describing it as cheaper, more capable, and multimodal. GPT-5-tier small models such as mini and nano are also strong candidates. All offer larger context, better benchmarks, and image support, so replacements typically improve both quality and cost while removing deprecation risk.