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OpenAI Fine-tuning

  • What it is:OpenAI Fine-tuning is a platform feature on platform.openai.com that lets users create customized models from base models like GPT-4 by training on uploaded JSONL datasets via supervised fine-tuning or reinforcement fine-tuning in the dashboard or API.
  • Best for:Enterprise AI teams needing top performance, Production ML applications, Teams already using OpenAI API
  • Pricing:Starting from $25.00 / 1M tokens
  • Rating:92/100Excellent
  • Expert's conclusion:Required for Production Grade Custom AI at Scale – However, Evaluate ROI Carefully for Smaller Workloads.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is OpenAI Fine-tuning and What Does It Do?

OpenAI is a U.S.-based artificial intelligence research group that creates artificially intelligent computers through its high-performance AI models and products such as ChatGPT. It has a hybrid business model in which a nonprofit foundation oversees a for-profit public-benefit corporation. Its focus is on developing leading edge AI technology, specifically using deep learning and large language models for consumers and enterprises.

Active
📍San Francisco, CA
📅Founded 2015
🏢Public Benefit Corporation (with nonprofit oversight)
TARGET SEGMENTS
EnterprisesDevelopersConsumersResearchers

What Are OpenAI Fine-tuning's Key Business Metrics?

🏢
1,500+
Employees
📊
$1B committed
Founding Funding
📊
Microsoft (major investor), PwC
Key Partnerships
Rating by Platforms
4.7/ 5
G2 (120 reviews)
Regulated By
Delaware PBC Approval(USA)California Regulatory Approval(USA)

How Credible and Trustworthy Is OpenAI Fine-tuning?

92/100
Excellent

A well-established AI player that is extremely large-scale; has a significant level of backing by way of funding; and has created highly competitive products based on a non-profit mission and has established major partnerships within the AI space.

BREAKDOWN
Product Maturity95/100
Company Stability90/100
Security & Compliance85/100
User Reviews90/100
Transparency80/100
Support Quality88/100
TRUST SIGNALS
Microsoft strategic partnershipNonprofit mission oversightUsed by Fortune 500 companiesRegulatory approval for PBC structure

What is the history of OpenAI Fine-tuning and its key milestones?

2015

Company Founded

Founded as a non-profit by Sam Altman, Elon Musk, Ilya Sutskever, Greg Brockman, and others with a $1 billion dollar funding commitment to advance the creation of safe Artificial General Intelligence (AGI).

2019

For-Profit Subsidiary Created

Developed a capped-profit subsidiary below the non-profit umbrella to enable the scaling of both research and commercialization of AI products.

2023

ChatGPT Launch

Launched ChatGPT, which spurred a global generative AI explosion and achieved massive user adoption.

2024

Restructuring Proposed

Announced it would be transitioning from a capped-profit subsidiary to a public-benefit corporation structure.

2025

PBC Transition Completed

Completed restructuring with a non-profit foundation owning approximately 26 percent of the OpenAI Group PBC, and was formally approved by the California and Delaware regulatory agencies.

Who Are the Key Executives Behind OpenAI Fine-tuning?

Sam Altman— CEO
Co-founder who was also the previous CEO of Y Combinator. Primary architect of OpenAI’s commercial transformation and growth strategy.. LinkedIn
Greg Brockman— President & Co-founder
Previous Chief Technology Officer of Stripe. Now leads engineering and product development at OpenAI.. LinkedIn
Mira Murati— CTO
Responsible for technical strategy and model development at OpenAI. Prior to OpenAI he worked at Tesla and Leap Motion.. LinkedIn
Ilya Sutskever— Chief Scientist & Co-founder
Early pioneer in AI research; formerly of Google Brain. Co-authored many influential papers in the field of deep learning.

What Are the Key Features of OpenAI Fine-tuning?

✨
Custom Model Training
Fine tune the base models, such as GPT-4o and GPT-4o mini, on proprietary data sets to achieve optimal results for specific domains.
✨
High-Performance Base Models
Use industry leading models to begin with, including GPT-4o (multimodal), o1 reasoning models, and cost-effective GPT-4o mini.
✨
Cost Efficiency
Achieve up to an 85% reduction in inference costs with optimized fine-tuned models when compared to their base models.
✨
Easy Deployment
Automatically deploy fine-tuned models instantly via the OpenAI API with automatic versioning and monitoring.
✨
Data Privacy Controls
Enterprise option to opt-out of using model training data and still utilize enterprise-grade security and compliance.
📊
Advanced Evaluation Tools
Built in framework for measuring improvement in fine-tuning through benchmarking as well as evaluating AI versus Human interaction.
🔗
Batch API Processing
Ability to process large datasets in an asynchronous manner at a cost of 50% compared to other products used for high volume inference needs.

What Technology Stack and Infrastructure Does OpenAI Fine-tuning Use?

Infrastructure

Multi-cloud with Microsoft Azure primary hosting and dedicated GPU clusters

Technologies

PythonPyTorchJAXKubernetesGPUs/TPUs

Integrations

REST APIMicrosoft AzureLangChainLlamaIndexEnterprise SSO

AI/ML Capabilities

Proprietary GPT architecture with transformer-based large language models supporting text, vision, and reasoning capabilities up to 200K+ context windows

Based on official documentation, API references, and engineering publications

What Are the Best Use Cases for OpenAI Fine-tuning?

Enterprise Customer Support
Fine tune models on support ticket and knowledge base data in order to develop domain specific agents capable of resolving 40% more issues without escalation.
Software Developers
Develop custom code generation models from proprietary codebases and style guides in order to accelerate development 2-3 times faster than traditional methods while meeting quality standards.
Content & Marketing Teams
Model customization to fit a company's brand voice, industry terminology and preferred audience to produce high quality content at scale.
Research Scientists
Fine tune models on specialized datasets in order to create research assistant models that are able to comprehend and analyze niche domains to speed up hypothesis testing.
NOT FORHigh-Frequency Trading Systems
Not Applicable - Latency associated with fine tuning is too high for decision making to be made within sub-millisecond time frames.
NOT FORRegulated Medical Diagnosis
Limited Applicability - In order to use this product, clinical validation will need to take place and/or specific compliance to health care regulations must occur outside of what the current offering can provide.

How Much Does OpenAI Fine-tuning Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
GPT-4.1 Fine-tuning Training$25.00 / 1M tokensCost to train the model on your dataset—
GPT-4.1 Fine-tuning Inference Input$3.00 / 1M tokensInput tokens for fine-tuned model (standard)—
GPT-4.1 Fine-tuning Inference Cached Input$0.75 / 1M tokensCached input tokens for fine-tuned model—
GPT-4.1 Fine-tuning Inference Output$12.00 / 1M tokensOutput tokens for fine-tuned model—
GPT-4.1-mini Fine-tuning Training$5.00 / 1M tokens——
GPT-4.1-mini Fine-tuning Inference Input$0.80 / 1M tokens——
GPT-4.1-mini Fine-tuning Inference Output$3.20 / 1M tokens——
o4-mini Reinforcement Fine-tuning$100.00 / training hour—Third-party sources
GPT-4.1 Fine-tuning Training$25.00 / 1M tokens
Cost to train the model on your dataset
GPT-4.1 Fine-tuning Inference Input$3.00 / 1M tokens
Input tokens for fine-tuned model (standard)
GPT-4.1 Fine-tuning Inference Cached Input$0.75 / 1M tokens
Cached input tokens for fine-tuned model
GPT-4.1 Fine-tuning Inference Output$12.00 / 1M tokens
Output tokens for fine-tuned model
GPT-4.1-mini Fine-tuning Training$5.00 / 1M tokens
GPT-4.1-mini Fine-tuning Inference Input$0.80 / 1M tokens
GPT-4.1-mini Fine-tuning Inference Output$3.20 / 1M tokens
o4-mini Reinforcement Fine-tuning$100.00 / training hour
Third-party sources
💡Pricing Example: Fine-tune GPT-4.1-mini on 10M training tokens, then 1M input + 1M output inference monthly
Training Cost$50
$5.00 x 10M tokens
Monthly Inference$4.00
$0.80 input + $3.20 output (1M each)

How Does OpenAI Fine-tuning Compare to Competitors?

FeatureOpenAI Fine-tuningAnthropic Fine-tuningGoogle Vertex AI TuningAzure OpenAI Fine-tuning
Core FunctionalityYes (GPT-4.1, 4.1-mini)Yes (Claude 3.5 Sonnet)Yes (Gemini models)Yes (o4-mini regional)
Training Pricing$25/1M tokens (GPT-4.1)$8-15/1M tokens$0.001/char (~$1/1M)$1.21/1M tokens (o4-mini)
Free TierNoNoLimited creditsNo
Enterprise FeaturesUsage-based enterpriseEnterprise plansFull enterpriseAzure enterprise contracts
API AvailabilityYesYesYesYes
Supported ModelsGPT-4.1 seriesClaude 3.5Gemini 1.5OpenAI models on Azure
Support OptionsAPI docs + enterpriseEnterprise supportGoogle Cloud supportAzure support
Security CertificationsSOC 2, enterprise securitySOC 2ISO, SOC complianceAzure compliance
Core Functionality
OpenAI Fine-tuningYes (GPT-4.1, 4.1-mini)
Anthropic Fine-tuningYes (Claude 3.5 Sonnet)
Google Vertex AI TuningYes (Gemini models)
Azure OpenAI Fine-tuningYes (o4-mini regional)
Training Pricing
OpenAI Fine-tuning$25/1M tokens (GPT-4.1)
Anthropic Fine-tuning$8-15/1M tokens
Google Vertex AI Tuning$0.001/char (~$1/1M)
Azure OpenAI Fine-tuning$1.21/1M tokens (o4-mini)
Free Tier
OpenAI Fine-tuningNo
Anthropic Fine-tuningNo
Google Vertex AI TuningLimited credits
Azure OpenAI Fine-tuningNo
Enterprise Features
OpenAI Fine-tuningUsage-based enterprise
Anthropic Fine-tuningEnterprise plans
Google Vertex AI TuningFull enterprise
Azure OpenAI Fine-tuningAzure enterprise contracts
API Availability
OpenAI Fine-tuningYes
Anthropic Fine-tuningYes
Google Vertex AI TuningYes
Azure OpenAI Fine-tuningYes
Supported Models
OpenAI Fine-tuningGPT-4.1 series
Anthropic Fine-tuningClaude 3.5
Google Vertex AI TuningGemini 1.5
Azure OpenAI Fine-tuningOpenAI models on Azure
Support Options
OpenAI Fine-tuningAPI docs + enterprise
Anthropic Fine-tuningEnterprise support
Google Vertex AI TuningGoogle Cloud support
Azure OpenAI Fine-tuningAzure support
Security Certifications
OpenAI Fine-tuningSOC 2, enterprise security
Anthropic Fine-tuningSOC 2
Google Vertex AI TuningISO, SOC compliance
Azure OpenAI Fine-tuningAzure compliance

How Does OpenAI Fine-tuning Compare to Competitors?

vs Anthropic Fine-tuning

OpenAI offers a greater selection of models, the GPT-4.1 Series, however their training costs are significantly higher at $25 per 1 million words when compared to Anthropic's prices of $8-$15 per 1 million words. Anthropic focuses on Constitutional AI Safety, whereas OpenAI has a larger overall ecosystem.

Use OpenAI for applications where performance is critical, use Anthropic for applications that require a focus on AI Safety within the enterprise.

vs Google Vertex AI Tuning

Google offers a significantly lower training cost for their models at approximately $1 per 1 million tokens with support for multimodal Gemini. While OpenAI has more mature tools available to developers, they lack the enterprise level cloud integration that Google currently offers.

Use Google for applications that require low cost and scale, use OpenAI for applications that require a developer friendly API experience.

vs Azure OpenAI Fine-tuning

The Azure version of Anthropic has varying regional pricing options at $1.21 per 1 million for o4-mini and enterprise compliance advantages. However, utilizing the direct OpenAI API offers developers more model choices with the highest benchmark performance, but also limits the amount of infrastructure control developers have over the deployment of the models.

The Azure service is ideal for regulated industries; a direct API is perfect for rapid prototyping.

vs AWS Bedrock Custom Models

AWS offers a multi-LLM marketplace that allows developers to fine-tune models across different vendors. While OpenAI offers proprietary models with the highest benchmark performance, the models are fully locked into the OpenAI vendor and cannot be used with other LLMs.

AWS provides a high degree of flexibility for multiple models; OpenAI is best for high-performance models.

What are the strengths and limitations of OpenAI Fine-tuning?

Pros

  • We have the best industry model performance — our GPT-4.1 base models are consistently the highest on benchmark charts.
  • Our fine-tuning system includes all you need to train and run your models including caching; we provide three different pricing levels.
  • We have the most mature developer experience — our API documentation and SDKs are among the best in every language.
  • We can scale our infrastructure — it can handle large volumes from an enterprise reliably.
  • We continually update our models — we make continuous improvements to our fine-tuning functionality.
  • We have flexible pricing options — we offer Batch, Standard, Priority and Flex pricing for both cost and performance.
  • We have battle tested our service at the largest scales — we power production applications for Fortune 500 Companies.

Cons

  • Our fine-tuning training costs are high — $25 per one million tokens is much more expensive than that of competitors.
  • Fine-tuned models are 1.5 to 3 times as expensive as base models for inference — this makes fine-tuned models rarely less expensive than the base model.
  • We do not have a free experimentation tier — you must pay to test if fine-tuning will be possible.
  • We have complex token accounting — this makes it difficult to understand how to bill for training + inference + caching.
  • We only offer the GPT-4.1 series of models for fine-tuning — we do not offer any other models for fine-tuning.
  • There is a high level of vendor lock-in with our service — there are significant costs to switch to another service after you have invested time into fine-tuning models.
  • The prices of tokens are volatile and unpredictable — this makes it impossible to predictably budget for monthly expenses.

Who Is OpenAI Fine-tuning Best For?

Best For

  • Enterprise AI teams needing top performance — GPT-4.1 always has the highest results on benchmark charts for complex reasoning tasks.
  • Production ML applications — Our service is battle tested and scalable with enterprise Service Level Agreements (SLA) and scaling capabilities.
  • Teams already using OpenAI API — Integrating our service seamlessly with your current GPT workflow and token accounting systems is easy.
  • Startups with VC funding — The price is justified by the performance when using the service during a growth period.
  • Companies with proprietary datasets — Fine-tuning is performed securely so that sensitive information remains in OpenAI's infrastructure.

Not Suitable For

  • Cost-sensitive startups — If you don't want to spend high token prices, you can use alternative services such as Google Vertex AI or DeepSeek for lower priced solutions.
  • Academic research teams — Consider using open source fine tuning frameworks like Hugging Face instead of paying the high prices for our service since there is no free tier.
  • Low-volume experimentation — A minimum viable fine-tuning solution is very expensive in terms of tokens; therefore, try using prompt engineering before fine-tuning.
  • Multi-cloud enterprises — Once you start fine-tuning models, there is full vendor lock-in; therefore, consider using AWS Bedrock for model portability.

Are There Usage Limits or Geographic Restrictions for OpenAI Fine-tuning?

Training Minimum Dataset
Requires sufficient examples for quality (typically 50-100+)
File Size Limits
Individual files ≤ 512MB, ≤ 2GB total per job
Supported Models
GPT-4.1, GPT-4.1-mini, GPT-4.1-nano only
Concurrent Jobs
Limited by account tier and quota
API Rate Limits
Tier-dependent RPM/TPM limits apply
Data Retention
Training data deleted after job completion
Geographic Availability
Global with sanctioned country restrictions
Compliance Restrictions
Prohibited content per Usage Policies

Is OpenAI Fine-tuning Secure and Compliant?

SOC 2 Type IIComprehensive security controls audit completed
Data EncryptionTraining data encrypted at rest (AES-256) and in transit (TLS 1.3)
Access ControlsAPI key authentication, organization-level permissions, role-based access
GDPR ComplianceData residency options, deletion rights, DPA available for enterprise
Usage PoliciesStrict content prohibitions enforced via automated + human moderation
Audit LoggingComplete API usage logs retained per enterprise agreements
Model IsolationFine-tuned models fully isolated; training data not used for base model updates

What Customer Support Options Does OpenAI Fine-tuning Offer?

Channels
help@openai.com for billing and account issues24/7 self-service documentation and guidesOpenAI Developer Community for technical supportReal-time API and service status updates
Hours
24/7 self-service, email support business hours
Response Time
Email: 24-48 hours typical, priority for enterprise customers
Satisfaction
Mixed - 3.8/5 on support forums and review sites
Specialized
Dedicated technical account managers for enterprise customers
Business Tier
Priority support, custom SLAs available for enterprise
Support Limitations
•No phone or live chat support
•Free tier relies entirely on community forums
•High-volume users report delayed responses

What APIs and Integrations Does OpenAI Fine-tuning Support?

API Type
REST API with OpenAPI 3.0 specification
Authentication
API Keys (Organization and Project scoped)
Webhooks
Available for usage events and billing alerts
SDKs
Official: Python, Node.js, Java. Community: all major languages
Documentation
Comprehensive API reference with code samples and interactive playground
Sandbox
Playground available, $5 free credit for new accounts
SLA
99.9% monthly uptime SLA for paid tiers
Rate Limits
Tiered RPM/TPM limits (100-10K+), burst allowances
Use Cases
Fine-tuning job management, model deployment, inference with custom models

What Are Common Questions About OpenAI Fine-tuning?

Add your training data set (in JSONL file format) and begin the fine tuning process by selecting a base model from the dashboard or API. The time it takes to train will depend upon how many records are included in the data set (minutes to hours).

Cost is as follows: training: 25 dollars for every one million tokens; inference: 3.75 dollars per million input tokens and 15 dollars per million output tokens. A minimum of about 10 samples (data points) is needed for training and cost increases with each million tokens used during the training process.

Fine-tuning a model adjusts the model's weights to be more suitable to your specific task or domain and this tends to reduce the number of tokens used and increase consistency. Prompt Engineering uses a base model and adds detailed instructions to the prompt to improve the quality of the response. Fine-tuning is best suited for repetitive, high volume applications.

Yes, OpenAI uses an encryption method that prevents them from using their customer's fine-tuning data to fine tune their own base models. All data sent to OpenAI for fine tuning purposes is encrypted both in transit and at rest. Enterprise customers can also request that OpenAI retain no data from their fine tuning activities.

No, all fine tuning is done on OpenAI's servers. There is no on premises capability at present. However, OpenAI has partnered with Microsoft to offer private Azure OpenAI services to their enterprise customers.

Fine Tuning requires a minimum of 10 training examples and maximum of 1 million tokens for most models. Currently there is no ability to fine tune using images or audio in GPT-4o. Additionally, once a model is fine tuned it cannot be fine tuned again.

For small data sets less than 100,000 tokens, fine tuning typically takes 5-30 minutes. For larger data sets of approximately 1 million tokens, fine tuning may take 1-4 hours. Fine tuning will automatically run validation jobs during the fine tuning process.

No, the dashboard provides a no code experience. Advanced users can access the API and jobs endpoints if they prefer. The user must prepare their data in JSONL format.

Is OpenAI Fine-tuning Worth It?

Fine Tuning provides OpenAI customers with unparalleled ability to customize their models for improved performance. The dashboard and API provided provide easy access to the fine tuning capabilities. Although costs for fine tuning can quickly add up for large scale fine tuning projects, the increased efficiency and consistency of the fine tuned models make the costs well worth the investment for production applications. This is why OpenAI remains the gold standard in fine tuning even with the emergence of new competitors.

Recommended For

  • Companies that have over 100,000 repetitive calls per month where consistency is key
  • Enterprises looking to create a domain specific model to perform at scale
  • Developers creating customer facing AI products that require a stable and consistent experience for end users.
  • Organizations that have experience in instruction-tuning paradigms among their machine learning engineers

!
Use With Caution

  • Small-scale projects (<10K inferences/month) – Prompting is generally a lower-cost alternative
  • Highly-regulated industries – Verify data residency and compliance requirements
  • Rapid Prototyping – Fine-tuning iterations are generally faster than base model iterations
  • Budget-constrained Startups – Training costs can add up rapidly

Not Recommended For

  • One-off Inference Needs – Prompting is generally sufficient
  • Applications where real-time processing of inferences is required to meet latency
  • Teams that do not have Data Engineering resources for preparing datasets
  • Simple Classification Tasks – Function Calling or Few-Shot Prompting will be sufficient
Expert's Conclusion

Required for Production Grade Custom AI at Scale – However, Evaluate ROI Carefully for Smaller Workloads.

Best For
Companies that have over 100,000 repetitive calls per month where consistency is keyEnterprises looking to create a domain specific model to perform at scaleDevelopers creating customer facing AI products that require a stable and consistent experience for end users.

What do expert reviews and research say about OpenAI Fine-tuning?

Key Findings

Fine-Tuning Platform Supporting GPT-4o, GPT-4.1 Variants with Comprehensive Dashboard/API Access. Token-Based Pricing (Training: $25/M, Inference: $3.75-15/M) Scales Predictably. Industry Leading Performance – Requires Data Engineering Investment. No On-Premises Option; Available for Enterprises on Azure.

Data Quality

Excellent - official pricing/docs from OpenAI platform. Costs verified across multiple 2025 sources. Feature details comprehensive from developer portal.

Risk Factors

!
Rapidly Changing Pricing (Multiple Tiers Updated 2024-2025)
!
Dependence on Availability of OpenAI Infrastructure
!
Complexity of Preparing Datasets for Non-Technical Users
!
No Fine-Tuning on Customer Hardware
Last updated: February 2026

What Are the Best Alternatives to OpenAI Fine-tuning?

  • •
    Anthropic Claude Fine-tuning: Competitive Pricing ($15/M Training, $3-15 Per Million Inference) – Strong Safety Focus. Instruction-Tuning Approach Similar to OpenAI. Best For Teams Focusing on Alignment/ML Research. (anthropic.com)
  • •
    Google Vertex AI Tuning: Enterprise-Focused with VPC/Private Endpoints, Integrated with Google Cloud. Supports Supervised Fine-Tuning and Parameter-Efficient Methods. Best For GCP Customers Needing Compliance/Data Residency. (cloud.google.com)
  • •
    Azure OpenAI Service: OpenAI Models With Microsoft Enterprise Controls (Private Endpoint, RBAC). Same Fine-Tuning Capabilities with Azure Billing/Security. Best For Microsoft Ecosystem Enterprises Avoiding Direct OpenAI Vendor Lock. (azure.microsoft.com)
  • •
    Hugging Face AutoTrain: The ability to no-code fine tune over 100,000+ public models, lower cost for alternative open source versions of GPT. Best option for budget constrained teams that are willing to work with public weights. (huggingface.co)
  • •
    Cohere Command Tuning: RAG optimized fine tuning at $3/M training, strong multilingual capabilities and enterprise grade retrieval features. Best option for international teams developing semantic search/classification solutions. (cohere.com)

What Additional Information Is Available for OpenAI Fine-tuning?

Developer Community

Over 500,000 active members in OpenAI's developer forum, regular fine tuning tutorials, examples of datasets, and troubleshooting. This is the official discord channel for real time support.

Recent Improvements

OpenAI announced the launch of GPT-4o fine tuning in August 2024. In addition to this, OpenAI released the GPT-4.1 mini/nano variant in 2025 as a low-cost version of GPT-4 for use cases that require less compute power. Also in 2025, reinforcement fine tuning was also introduced for advanced control.

Enterprise Features

SOC 2 Type II compliant, options to store zero data, custom model hosting allows for up to 100 fine tuned models per org, provisioned throughput available through Azure.

Dataset Resources

There are several example fine tuning datasets for GPT-4 on GitHub. Additionally, there are several data preparation tools such as tokenizers and format converters. OpenAI has created a best practices guide for instruction following.

Monitoring Tools

The fine-tuning dashboard will show you your training curves, validation loss, etc. The usage analytics for GPT-4 will track the total inference cost across all of your fine-tuned models. They have a cost calculator available.

What Are OpenAI Fine-tuning's Model Training Compute?

Fully managed GPU infrastructure
Managed Compute
NVIDIA GPUs (specifics not disclosed)
GPU Types
Distributed training available
Multi-GPU Support
1M tokens/day through Sep 23 (historical)
Free Training Tokens

What Finetuning Techniques Does OpenAI Fine-tuning Support?

Full Fine-TuningSupervised Fine-TuningModel DistillationMultimodal Fine-TuningVision Fine-TuningAudio Fine-Tuning (Beta)

What Supported Models Does OpenAI Fine-tuning Offer?

GPT-4o

Their flagship multimodal model supports vision fine-tuning.

GPT-4o-mini

A smaller model ideal for distillation and edge devices.

o1-mini

A reasoning model for distillation.

gpt-oss

Models that can be used with open-source software.

What Is OpenAI Fine-tuning's Training Pricing?

Pricing Model
Token-based pricing (training + inference)
Free Tier
1M training tokens/day (limited time offer)
Prompt Caching
50% discount on cached tokens
Vision Training Discount
Discounted rates available (October promo)

What Training Features Does OpenAI Fine-tuning Offer?

Real-time Monitoring

Through their API, you can track your epochs, loss, and job status.

Model Distillation Suite

OpenAI has an end-to-end process for creating smaller models.

Prompt Caching

GPT-4 will automatically reduce your cost by half when you repeat a prompt.

Low Data Requirements

With fewer than a couple dozen examples, they get very good results.

API-based Workflow

They have seamless integration with the OpenAI API.

How Do You Deploy Models with OpenAI Fine-tuning?

API Deployment
Direct API access to fine-tuned models
Production Ready
Optimized for low-latency production use
Custom Model Versions
Unlimited fine-tuned model deployments
Realtime API
Low-latency audio support (beta)

How Does OpenAI Fine-tuning Handle Data Management, Storage, and Governance?

Multimodal Datasets

Training data for text, image, and audio.

Custom Datasets

Your proprietary training data can be uploaded.

JSONL Format

OpenAI uses the standard fine tuning format that you would expect from them.

Low Data Requirements

For vision tasks, it is effective with 100 images.

Data Privacy

OpenAI's data handling meets the needs of an enterprise grade solution.

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