GPT-3.5 Turbo
GPT-3.5 Turbo is a chat-oriented language model from OpenAI, positioned as an older, lower-cost tier in the company's GPT lineup for general text and conversational tasks.
API Pricing
| Provider | Input / 1M | Output / 1M |
|---|---|---|
| $1.00 | $2.00 |
Prices updated daily. Last check: Nov 5, 2026
Compare API pricing for every OpenAI model →GPT-3.5 Turbo pricing by provider
Input, output, and batch rates, plus alternatives, for one provider at a time.
Model Details
General
- Creator
- OpenAI
- Modalities
- Text
Capabilities
- Open Source
- No
Strengths & Limitations
Strengths
- Long-established model with extensive public prompt patterns, tutorials, and community-tested integrations
- Uses OpenAI's standard chat completions message format, so code written for it ports easily to later GPT models
- Available in multiple dated snapshots, which lets teams pin a specific revision for reproducibility
- Generally low latency and high throughput relative to larger GPT-4 class models, suiting bulk text processing
- Well suited to bounded tasks like classification, tagging, summarization, and format conversion
- Broad third-party tooling and framework support due to years of ecosystem adoption
Limitations
- Superseded in capability by OpenAI's GPT-4 and GPT-4o generations, including their smaller variants
- Weaker at multi-step reasoning, complex code generation, and long-form instruction following than newer models
- Proprietary — served via API only, with no published weights for self-hosting
- Context window varies by dated snapshot and is small compared with modern long-context models
- Our database has no confirmed benchmark scores or throughput measurements for this entry
Key Features
About GPT-3.5 Turbo
Common Use Cases
GPT-3.5 Turbo fits high-volume, cost-sensitive text workloads where the task is narrow and well-defined: intent classification, sentiment and topic tagging, short summarization, data cleanup and reformatting, template-driven copy, FAQ-style chat responses, and synthetic data generation for evaluation sets. It is also commonly retained in legacy production systems whose prompts and guardrails were tuned specifically against it, where swapping models would require re-validation. It is a weaker choice for agentic pipelines, difficult coding tasks, math-heavy reasoning, or long-document analysis; those workloads generally point toward the GPT-4o generation or a dedicated reasoning model.
Frequently Asked Questions
How much does GPT-3.5 Turbo cost to run?
Costs depend on which provider serves the model, whether you are billed for input or output tokens, and whether you use batch, cached, or fine-tuned variants. Because those rates change frequently, see the pricing table on this page for current per-provider numbers rather than relying on figures quoted elsewhere.
What is GPT-3.5 Turbo best used for?
Narrow, high-throughput text tasks — classification, tagging, extraction, short summaries, format conversion, and simple conversational replies. It is a reasonable default when the prompt is tightly specified and volume matters more than reasoning depth.
Should I use GPT-3.5 Turbo or a newer OpenAI model?
For new projects, most teams compare it against the compact tiers of OpenAI's GPT-4o generation, which generally handle reasoning, instruction following, and code better. GPT-3.5 Turbo remains relevant mainly for legacy deployments already tuned to it, or where a specific pinned snapshot is required for reproducibility.
Can GPT-3.5 Turbo process images?
We do not track confirmed image-input support for this model. Vision capability arrived in OpenAI's later GPT-4 class models, so if your workload requires image understanding, verify modality support on your provider's model card before building against GPT-3.5 Turbo.
How large is the GPT-3.5 Turbo context window?
Our database does not carry a confirmed context length for this entry. GPT-3.5 Turbo has shipped in several dated snapshots with different context limits, so check the specific revision your provider serves before designing long-prompt workflows.