OpenAI's fast, low-cost chat model for high-volume simple tasks and legacy integrations with fine-tuning support.
Modality:
Text
model ID
openai/gpt-3.5-turbo
Output Speed
N/A
tok/s
Intelligence Index
3.2
/ 100
Context Window
16385
tokens
Input price
3
Anytoken
Output price
9
Anytoken
GPT-3.5 Turbo: Fast, Low-Cost Text Generation for High-Volume Chat
GPT-3.5 Turbo is OpenAI's text-only chat model, originally released in 2023 and optimized for the Chat Completions API. It sits below the GPT-4 and GPT-4o families as a fast, economical option for straightforward conversational and completion tasks. Its distinguishing traits are low latency, low per-token cost, and fine-tuning support that can lift narrow-task accuracy toward much larger models. It fits high-volume, simple workloads—classification, short-form chat, and routing—rather than complex reasoning. OpenAI now recommends GPT-4o mini for most new projects, so GPT-3.5 Turbo is primarily relevant for legacy systems and existing fine-tunes.
Access GPT-3.5 Turbo through the AnyAPI.ai unified API.
Performance
Where GPT-3.5 Turbo Still Earns Its Place: Speed and Cost
GPT-3.5 Turbo's advantage is throughput and price on simple tasks. In independent latency testing it measures marginally faster than comparable small models, and its per-token cost sits in the low tier of OpenAI's lineup. That makes it viable for high-volume chat, classification, and routing where deep reasoning is unnecessary. Fine-tuning further strengthens narrow tasks: published experiments show a fine-tuned GPT-3.5 Turbo can rival much larger models on domain-specific extraction. The production consequence is clear—use it for cheap, fast, well-scoped workloads, not for tasks requiring current knowledge or complex multi-step reasoning.
Benchmarks
How GPT-3.5 Turbo Scores Against Modern Small Models
On Artificial Analysis, GPT-3.5 Turbo scores 3 on the Intelligence Index, placing it at the lower end of comparable models (median around 12). Task-level testing shows a mixed picture: independent evaluations found GPT-3.5 Turbo competitive with GPT-4o mini on some structured data-extraction tasks, while GPT-4o mini pulls ahead on verbal reasoning and SQL/JSON generation. Latency benchmarks show GPT-3.5 Turbo marginally faster than GPT-4o mini and Claude 3 Haiku, though real-world latency varies with prompt size and load. Net interpretation: raw speed remains a strength, general intelligence does not.
Output Speed
N/A
tok/s
Intelligence Index
3.2
/ 100
MMLU
Broad world knowledge and problem-solving
46
%
GPQA
PhD-level scientific reasoning across physics, biology, chemistry.
30
%
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 is text-in, text-out with no vision, audio, or document modalities. The current gpt-3.5-turbo-0125 snapshot supports a 16,385-token context window with a 4,096-token maximum output—note these are distinct limits, and long outputs are capped well below the input window. It supports function calling and JSON-mode structured outputs. The most production-relevant constraints are the September 2021 knowledge cutoff, which excludes recent events, and the absence of any multimodal input. For anything requiring images or current knowledge, a newer model is required.
Verified Specifications —
GPT-3.5 Turbo
Input modalities
Text
output modalities
Text
Context window
16385
tokens
Maximum output tokens
4096
Reasoning
No
Knowledge cutoff
March 2023
Pricing (standard)
3
AnyTokens in
/
9
AnyTokens out
Quickstart
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
headers = {
"Authorization": "Bearer AnyAPI_API_KEY",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())import requests url = "https://api.anyapi.ai/v1/chat/completions" payload = { "model": "gpt-3.5-turbo", "messages": [ { "role": "user", "content": "Hello" } ]
}
headers = { "Authorization": "Bearer AnyAPI_API_KEY", "Content-Type": "application/json"
} response = requests.post(url, json=payload, headers=headers) print(response.json())
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Copy
Code is copied
const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = {
method: 'POST',
headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'},
body: '{"model":"gpt-3.5-turbo","messages":[{"role":"user","content":"Hello"}]}'
};
try {
const response = await fetch(url, options);
const data = await response.json();
console.log(data);
} catch (error) {
console.error(error);
}const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = { method: 'POST', headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'}, body: '{"model":"gpt-3.5-turbo","messages":[{"role":"user","content":"Hello"}]}'
}; try { const response = await fetch(url, options); const data = await response.json(); console.log(data);
} catch (error) { console.error(error);
}
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Copy
Code is copied
curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer AnyAPI_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'curl --request POST \ --url https://api.anyapi.ai/v1/chat/completions \ --header 'Authorization: Bearer AnyAPI_API_KEY' \ --header 'Content-Type: application/json' \ --data '{ "model": "gpt-3.5-turbo", "messages": [ { "role": "user", "content": "Hello" } ] }'
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Code is copied
View docs
Limitations & Trade-offs
1
September 2021 knowledge cutoff. The model's training data ends in September 2021, so it has no awareness of later events, libraries, APIs, or facts. Any workload touching current information requires retrieval augmentation or in-context injection. For fact-sensitive or up-to-date use cases, newer models with later cutoffs are strictly preferable.
2
Text only, no multimodality. GPT-3.5 Turbo accepts and produces only text—no image, audio, document, or video input. Applications needing vision, OCR-style document parsing, or multimodal understanding cannot use it and should choose GPT-4o mini or another multimodal model instead.
3
Low general intelligence and no reasoning mode. It scores at the low end of independent intelligence indices and has no extended-thinking or reasoning configuration. Complex multi-step reasoning, hard math, and intricate code generation trail modern small models. For reasoning-heavy work, a dedicated reasoning model or GPT-4o mini is a better fit.
4
Legacy status and constrained output length. OpenAI officially steers new development toward GPT-4o mini, which is cheaper and more capable, so GPT-3.5 Turbo primarily serves existing integrations. Its 4,096-token maximum output also limits long-form generation, and long documents can exceed the 16K context window entirely.
Best-Fit Workloads
Where this model earns its place
01
High-volume simple chat and classification
For short conversational replies, intent detection, and text classification at scale, GPT-3.5 Turbo's low cost and fast responses are well matched. These tasks rarely need deep reasoning or current knowledge, so the model's core limitations are less impactful. Independent testing shows it remains competitive with newer small models on some structured tasks, keeping it viable where cost-per-request dominates.
02
Fine-tuned narrow-task models
GPT-3.5 Turbo has mature fine-tuning support, and published experiments show a fine-tuned version can approach much larger models on domain-specific tasks such as targeted data extraction and specialized classification. Teams that have already invested in a fine-tuned snapshot get strong, cheap, low-latency inference on that narrow task—often the most defensible reason to keep using this model.
03
Legacy production maintenance
Systems already built and validated around GPT-3.5 Turbo benefit from its behavioral stability via pinned snapshots. Keeping it in place avoids re-testing prompts and re-validating outputs. This is a maintenance-driven fit rather than a capability-driven one; new features requiring current knowledge or multimodality still warrant migration to GPT-4o mini.
04
Latency-sensitive lightweight routing
Where a fast, cheap model must classify or route requests before handing off to a larger model, GPT-3.5 Turbo's marginally faster measured latency and low cost fit the pre-processing role. Because routing decisions are simple, the model's weaker reasoning is rarely a bottleneck, and per-request savings compound across high traffic volumes.
Integration
01
One API integration
02
Easy model switching
03
Flexible for production
04