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Production Readiness Profile

GPT-3.5 Turbo

GPT-3.5 Turbo is a balanced model with standard context support, optimized for code generation and low-latency assistants in enterprise environments.

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Data checked: 2026-03-19

Context Window
16,385
Input / 1M
$0.50
Output / 1M
$1.50

Model Positioning

OpenAI lists GPT-3.5 Turbo as a standard context option with $0.50 per 1M tokens input pricing, $1.50 per 1M tokens output pricing, and text->text modality support for enterprise AI operations.

  • Latest profile indicates standard context capacity for enterprise prompts and documents.
  • Current pricing band is balanced: $0.50 per 1M tokens input and $1.50 per 1M tokens output.
  • Best-fit workloads include: Code generation, Low-latency assistants.
  • Enforce policy checks and output review on sensitive workflows.

Key Specs

Model ID
openai/gpt-3.5-turbo
Context Window
16,385 tokens
Modality
text->text
Input Modalities
text
Output Modalities
text
Input Price
$0.50 per 1M tokens
Output Price
$1.50 per 1M tokens
Provider
OpenAI
Listing Date
2023-05-28

Strengths

  • GPT-3.5 Turbo is suited for code generation.
  • Supports standard context for multi-step prompts and larger working sets.
  • Pricing profile is balanced, enabling predictable workload routing decisions.
  • Can be paired with policy guardrails for safer deployment at scale.

Tradeoffs

  • Prompt standards are still needed to keep output quality consistent across teams.
  • Standard context limits may require chunking or retrieval strategies for large documents.
  • Balanced-price tiers still need policy-based routing to protect monthly budgets.
  • Text-only modality can limit workflows that rely on image, audio, or document interpretation.

High-Fit Use Cases

  • GPT-3.5 Turbo for software delivery workflows with policy-enforced prompts.
  • GPT-3.5 Turbo for high-volume assistant traffic with low-response targets.
  • GPT-3.5 Turbo for governed enterprise assistant workflows across teams.
  • GPT-3.5 Turbo for governed enterprise assistant workflows across teams.

Deployment Checklist

  • Define where GPT-3.5 Turbo is default vs. fallback in your routing policy.
  • Enable role-based access and policy checks before opening access broadly.
  • Set spend guardrails by team and monitor weekly token consumption.
  • monitor quality and spend weekly during early deployment.
  • Re-run quality and cost benchmarks monthly as newer releases appear.

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Safe AI Use Case Selector

Choose your team and goals, then start with the AI use cases that fit best and carry the least risk.

You get

Recommended first use cases for your company.

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Parameter Guidance

frequency_penalty

Tune repetition control for long responses in multi-step workflows.

logit_bias

Use this parameter only with tested defaults in production workflows.

logprobs

Use this parameter only with tested defaults in production workflows.

max_tokens

Set completion limits to avoid unpredictable long-output spend.

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AI Risk Test

Test what can go wrong before teams start using AI loosely across the company.

You get

A short risk summary with the main gaps to close.

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Knowledge Hub

GPT-3.5 Turbo FAQs

Choose GPT-3.5 Turbo when the workload aligns with code generation, low-latency assistants and quality targets justify its pricing profile.
It depends on workload mix. Most organizations use routing policies so routine traffic stays on lower-cost tiers.
Validate quality on real internal prompts, token efficiency, latency, and policy compliance behavior.

Deploy This Model With Governance

Use policy controls, role-based access, and budget guardrails before enabling advanced model tiers at scale.

Use GPT-3.5 Turbo in your company