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 is an extended‑context version of OpenAI’s GPT-3.5 Turbo model, originally released to support tasks that require processing larger amounts of text without splitting. Its context…
GPT-3.5 Turbo 16k excels at text‑based tasks where long context is advantageous. These include summarizing multi‑page documents, maintaining coherent multi‑turn conversations, answering questions over extended passages, and performing code review across larger codebases. Its 16k context window allows it to ingest entire chapters or long email threads in one go, reducing the need for manual text splitting. The model also handles standard generation tasks like drafting emails, writing creative content, and providing explanations. Because it is a GPT-3.5 class model, it is proficient at following instructions and producing fluent text, though it may not match GPT‑4 on complex reasoning or nuanced domain knowledge.
If your application does not require the extended context window, the standard GPT-3.5 Turbo 4k model (or other cheaper variants) may be more cost‑effective. For tasks that involve short prompts and outputs under 4,000 tokens, the 16k version offers no advantage and costs the same per token. You should also consider a smaller or faster model when your pipeline already works with chunked text and does not benefit from a large context. On OrcaRouter, you can switch between model IDs easily — for example, using "openai/gpt-3.5-turbo" (4k) when context needs are minimal. Evaluate your average input token count and choose the model that covers >95% of requests to avoid paying for unused capacity.
The primary benefit of the 16k context is the ability to process longer inputs in a single request, preserving coherence and eliminating issues from splitting text across windows. For instance, you can feed an entire 10,000‑word research paper into the model and ask it to extract key findings without having to manually stitch segments. In conversational applications, the model can remember the full dialogue history from a long customer support interaction, leading to more context‑aware replies. For code tasks, you can include multiple files or a complete function library in the prompt, enabling the model to understand cross‑file dependencies. This capability reduces engineering overhead for building chunking and state management logic.
GPT-3.5 Turbo 16k inherits the general limitations of the GPT-3.5 series. It can produce plausible‑sounding but incorrect or unsupported information (hallucination), especially in niche or extremely detailed domains. The model is text‑only and cannot process images, audio, or other modalities. Its reasoning depth is lower than GPT‑4, so it may struggle with complex multi‑step logic, mathematical proofs, or nuanced legal interpretation. The MMLU‑Pro score of 46.2, while respectable, is significantly below GPT‑4’s typical >80 score, indicating weaker factual accuracy on advanced topics. Additionally, the 16,384‑token context limit, though large, is not infinite; very long documents still require careful prompt engineering or chunking.
GPT-3.5 Turbo 16k achieves a score of 46.2 on the MMLU‑Pro benchmark. MMLU‑Pro is a curated subset of the Massive Multitask Language Understanding dataset, designed to evaluate a model’s depth of knowledge across 57 diverse subjects including STEM, social sciences, humanities, and law. The score represents the proportion of questions answered correctly. For comparison, the smaller GPT-3.5 Turbo (4k) typically scores around 43 on MMLU (standard), while GPT‑4 scores above 80. The 46.2 figure indicates that this model has a moderate grasp of complex factual material but is not state‑of‑the‑art in pure knowledge retrieval. It is best used for tasks where generating fluent text with acceptable factual accuracy is more important than top‑tier reasoning.
While specific reasoning benchmarks are not provided, GPT-3.5 Turbo 16k behaves similarly to the standard GPT-3.5 Turbo on logical deduction, arithmetic, and common‑sense reasoning. It can solve simple logic puzzles and multi‑step instructions, but it may fail on tasks requiring strict adherence to constraints or counterfactual reasoning. The increased context window does not improve reasoning capability per se — it only allows more input to be considered. In practice, users report that the model performs satisfactorily for summarization, translation, and chatbot applications, but should not be relied upon for high‑stakes decisions without human verification. The MMLU‑Pro score of 46.2 reinforces that domain‑specific expertise is moderate.
Speed and latency metrics for GPT-3.5 Turbo 16k are not explicitly given, but as a GPT-3.5 class model it is generally faster than GPT‑4 and suitable for real‑time applications. On OrcaRouter, the response time depends on OpenAI’s backend as well as network factors. For typical inputs under 4,000 tokens, the model often returns the first token within hundreds of milliseconds to a few seconds. Users should expect similar latency to the standard GPT-3.5 Turbo (4k) model, as the underlying architecture is identical except for the context limit. The 16k model may be slightly slower when processing very long prompts because the model needs to attend to more tokens, but the difference is usually marginal. For latency‑sensitive use cases, we recommend testing with your specific prompt lengths.
Pricing for GPT-3.5 Turbo 16k on OrcaRouter is $3.00 per 1M input tokens and $4.00 per 1M output tokens, billed at the exact OpenAI provider rate with zero markup. There are no additional platform fees, subscription tiers, or volume discounts applied — the rate is fixed and transparent. Charges are computed based on the number of tokens in each request: input tokens are the total tokens in the messages array, and output tokens are the tokens generated in the response. OrcaRouter does not add any overhead tokens. You pay only for what you use, and your usage is itemized in your OrcaRouter dashboard.
The main cost trade‑off is between paying for the large context window versus using a cheaper model with smaller context. If your average input rarely exceeds 4,000 tokens, the standard GPT-3.5 Turbo (4k) — priced at $1.50 input / $2.00 output per 1M tokens on OpenAI — would be more economical. However, once inputs regularly surpass 4,000 tokens, the 16k model prevents costly chunking and retrieval logic. The per‑token price of GPT-3.5 Turbo 16k is double that of the 4k variant. Therefore, you need to assess your token distribution: if more than 50% of requests require >4k tokens, the 16k model may be cheaper overall when factoring in engineering time to implement chunking.
OrcaRouter does not cache model outputs or prompt completions by default. Each request is sent fresh to OpenAI. This means you incur full token costs on every call, including repeated inputs. To reduce cost, consider implementing prompt caching on your side — for example, caching the system message or using a vector database to retrieve relevant context instead of re‑sending the full document each time. Because the model’s pricing is per‑token and based on total tokens sent, any reduction in input length directly reduces cost. The zero‑markup policy ensures that any caching strategy you employ yields savings at the same rate as direct OpenAI usage.
OrcaRouter applies zero markup to GPT-3.5 Turbo 16k. The prices quoted — $3.00 per 1M input tokens and $4.00 per 1M output tokens — are exactly the rates set by OpenAI for this model. OrcaRouter does not add any fees on top. This policy makes OrcaRouter a straightforward reseller: you pay the provider’s rate without any platform surcharges. There is no minimum spend or commitment. However, be aware that if OpenAI changes their pricing in the future, OrcaRouter will pass through those changes. You can monitor your costs in real time via the OrcaRouter dashboard, which shows token usage and cost per model.
To use GPT-3.5 Turbo 16k through OrcaRouter, set your API client’s base URL to https://api.orcarouter.ai/v1 and use the model ID "openai/gpt-3.5-turbo-16k". All OpenAI chat‑completion parameters are supported, including messages, temperature, top_p, frequency_penalty, presence_penalty, max_tokens, stop, and stream. You must authenticate with a valid OrcaRouter API key provided in the Authorization header (Bearer <key>). Example using curl: curl https://api.orcarouter.ai/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"}],"max_tokens":1024}'. OrcaRouter returns the same JSON structure as OpenAI.
All OpenAI chat‑completion parameters are available when using GPT-3.5 Turbo 16k via OrcaRouter. Key parameters include: messages (array of role/content objects), temperature (0‑2, default 1), top_p (0‑1, default 1), n (number of completions to generate, default 1), stream (boolean, default false), max_tokens (up to 4096), stop (one or more strings), frequency_penalty (‑2‑2, default 0), presence_penalty (‑2‑2, default 0), and logit_bias (map of token IDs to bias). The model ID must be exactly "openai/gpt-3.5-turbo-16k". Note that max_tokens cannot exceed 4096, and the total prompt + completion tokens must stay within 16,384. OrcaRouter validates these limits and returns an error if exceeded.
Migrating from a direct OpenAI integration to OrcaRouter for GPT-3.5 Turbo 16k requires only three changes: update the base URL from https://api.openai.com/v1 to https://api.orcarouter.ai/v1, replace the API key with your OrcaRouter key, and change the model ID from "gpt-3.5-turbo-16k" to "openai/gpt-3.5-turbo-16k". All other code, including message formatting, parameter handling, and response parsing, remains exactly the same. If you were previously using the 4k version, you can switch to the 16k model by only changing the model ID. No schema or rate limit modifications are necessary; OrcaRouter mirrors OpenAI’s API specification. Testing can be done by making a single request with a short prompt to confirm authentication and response structure.
GPT-3.5 Turbo 16k and GPT-3.5 Turbo 4k (model id "openai/gpt-3.5-turbo") share the same underlying architecture and capabilities. The only differences are context window (16,384 vs. 4,096 tokens) and pricing. The 4k variant is cheaper: on OpenAI, it costs $1.50/1M input tokens and $2.00/1M output tokens; through OrcaRouter the same rates apply. The 16k version costs exactly double. Performance on benchmarks like MMLU (standard) is nearly identical — GPT-3.5 Turbo 4k scores around 43 on MMLU, while the 16k version scores 46.2 on MMLU‑Pro (a harder variant). For tasks that fit within 4k tokens, the 4k model is more economical. For longer tasks, the 16k model avoids context truncation errors.
Compared to GPT‑4 (e.g., "openai/gpt-4"), GPT-3.5 Turbo 16k is significantly weaker on reasoning, factual accuracy, and safety alignment. GPT‑4 typically scores >80 on MMLU and handles complex multi‑step logic and nuanced instructions far better. GPT‑4 also supports images in some variants and has a context window up to 8,192 or 32,768 tokens. However, GPT‑4 is much slower and more expensive — often 10‑20x per token. Claude models (e.g., "anthropic/claude-2") also offer large context windows (100k tokens) and strong reasoning, but are priced higher than GPT-3.5 Turbo and may have different API semantics. GPT-3.5 Turbo 16k is a cost‑effective choice when you only need extended context without top‑tier reasoning. OrcaRouter lets you try any model easily.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-3.5-turbo-16k",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltylogit_biaslogprobsmax_tokenspresence_penaltyresponse_formatseedstopstreamtemperaturetool_choicetoolstop_logprobstop_p| Input / 1M tokens | $3.00 |
| Output / 1M tokens | $4.00 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/openai/gpt-3.5-turbo-16kOpen @misc{orcarouter_gpt_3_5_turbo_16k,
title = {GPT-3.5 Turbo 16k API},
author = {OpenAI},
year = {2023},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-3.5-turbo-16k}
}OpenAI. (2023). GPT-3.5 Turbo 16k API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-3.5-turbo-16k