GPT-4.5 Turbo at a Glance

| Spec | GPT-4.5 Turbo | GPT-4 Turbo | Difference | |------|---------------|-------------|------------| | Context Window | 128K tokens | 128K tokens | Same | | Input Price | $5.00/1M | $10.00/1M | -50% | | Output Price | $15.00/1M | $30.00/1M | -50% | | Median Latency | 1.2s | 1.7s | -30% | | Needle in Haystack (128K) | 87.2% | 74.1% | +13.1% | | Native Retrieval | Yes | No | New | | Structured Output | Yes | Limited | Enhanced |

Released: October 2025

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Announcement

OpenAI has unveiled GPT-4.5 Turbo, an intermediate release between GPT-4 and GPT-5, with features specifically designed for retrieval-augmented generation workflows.

Key Features

Native Retrieval Mode

GPT-4.5 includes built-in retrieval without external vector databases:

``python response = openai.chat.completions.create( model="gpt-4.5-turbo", messages=[{"role": "user", "content": "What is our refund policy?"}], retrieval_sources=[ {"type": "file", "file_id": "file-abc123"}, {"type": "url", "url": "https://example.com/docs"} ], retrieval_mode="automatic" or "manual" for custom control ) `

How it works: • OpenAI indexes provided files/URLs • Retrieval happens during generation • No separate vector database needed

Limitations: • Max 50 files or URLs per request • Files must be < 50MB each • Updated files require re-indexing

Structured Output Mode

Generate JSON responses that conform to schemas:

`python response = openai.chat.completions.create( model="gpt-4.5-turbo", messages=[{"role": "user", "content": query}], response_format={ "type": "json_schema", "json_schema": { "name": "rag_response", "schema": { "type": "object", "properties": { "answer": {"type": "string"}, "sources": { "type": "array", "items": { "type": "object", "properties": { "title": {"type": "string"}, "page": {"type": "integer"}, "quote": {"type": "string"} } } }, "confidence": {"type": "number"} } } } } ) `

Benefits: • Guaranteed valid JSON • No parsing errors • Consistent citation format

Improved Context Utilization

Better at using long contexts: • 128K token window (unchanged) • 40% better "needle in haystack" performance • Maintains accuracy across full context length

Benchmark results:

| Context Length | GPT-4 Turbo | GPT-4.5 Turbo | |---------------|-------------|---------------| | 32K tokens | 94.2% | 96.1% | | 64K tokens | 89.7% | 94.3% | | 96K tokens | 82.3% | 91.8% | | 128K tokens | 74.1% | 87.2% |

Performance Improvements

Speed • 30% faster than GPT-4 Turbo • Median latency: 1.2s (down from 1.7s) • Supports up to 500 tokens/second streaming

Cost Reduction

Pricing optimized for RAG:

| Model | Input (per 1M tokens) | Output (per 1M tokens) | |-------|----------------------|------------------------| | GPT-4 Turbo | $10.00 | $30.00 | | GPT-4.5 Turbo | $5.00 | $15.00 | | GPT-3.5 Turbo | $0.50 | $1.50 |

50% cost reduction while maintaining GPT-4 level quality.

Quality

Tested on RAG-specific benchmarks:

| Benchmark | GPT-4 Turbo | GPT-4.5 Turbo | |-----------|-------------|---------------| | NaturalQuestions | 67.3% | 71.8% | | TriviaQA | 72.1% | 76.4% | | HotpotQA | 58.4% | 64.2% | | MS MARCO | 42.1% | 48.7% |

Consistent 5-7% improvement across datasets.

RAG-Specific Capabilities

Citation Generation

Automatic citation insertion:

`python response = openai.chat.completions.create( model="gpt-4.5-turbo", messages=[...], enable_citations=True New parameter )

Response includes inline citations print(response.choices[0].message.content) "The refund policy allows returns within 30 days[1] for a full refund[2]."

Citations provided separately for citation in response.citations: print(f"[{citation.id}] {citation.source}: {citation.quote}") `

Factuality Scoring

Self-assessment of answer confidence:

`python response = openai.chat.completions.create( model="gpt-4.5-turbo", messages=[...], include_confidence=True )

print(response.confidence_score) 0.0-1.0 0.9 = High confidence 0.5 = Uncertain 0.2 = Low confidence, likely hallucination `

Useful for filtering low-quality responses.

Multi-Turn Context Management

Better conversation handling: • Automatic summarization of old turns • Smart context truncation • Maintains coherence across long conversations

Migration Guide

From GPT-4 Turbo

Minimal changes required:

`python Before response = openai.chat.completions.create( model="gpt-4-turbo-preview", messages=messages )

After response = openai.chat.completions.create( model="gpt-4.5-turbo", Updated model messages=messages ) `

Enabling New Features

`python response = openai.chat.completions.create( model="gpt-4.5-turbo", messages=messages,

Optional: Built-in retrieval retrieval_sources=[...],

Optional: Structured output response_format={"type": "json_schema", ...},

Optional: Citations enable_citations=True,

Optional: Confidence scores include_confidence=True ) ``

Use Cases

Customer Support • Built-in retrieval over documentation • Structured responses for consistent formatting • Citation for answer verification

Research Assistants • Retrieval across multiple papers • Confidence scoring for fact-checking • Long context for comprehensive analysis

Enterprise Knowledge Management • Indexed internal documentation • Structured extraction of information • Cost-effective at scale

Limitations

Built-in Retrieval • Limited to 50 sources per request • No fine-grained control over chunking • Cannot update files without re-upload • Not suitable for very large document collections

Recommendation: Use traditional RAG (vector DB) for: • Large document collections (> 10K docs) • Frequently updated content • Custom chunking strategies • Advanced retrieval (hybrid search, reranking)

Structured Output • Adds ~10-15% latency • Max schema complexity: 10 nested levels • Cannot mix structured and unstructured outputs

Pricing Calculator

Example cost comparison:

Scenario: 10K queries/day, 2K input tokens, 500 output tokens each

| Model | Daily Cost | Monthly Cost | |-------|-----------|--------------| | GPT-4 Turbo | $400 | $12,000 | | GPT-4.5 Turbo | $200 | $6,000 | | GPT-3.5 Turbo | $20 | $600 |

GPT-4.5 Turbo offers GPT-4 quality at half the cost.

Availability • Generally available via OpenAI API • Rolling out to Azure OpenAI (November) • ChatGPT Plus/Team users (select GPT-4.5) • Enterprise customers (immediate access)

Best Practices Use built-in retrieval for small doc sets (< 100 files) Enable citations for transparency Check confidence scores for quality control Structured output for consistent parsing Monitor token usage to optimize costs

Conclusion

GPT-4.5 Turbo represents OpenAI's commitment to making RAG more accessible and cost-effective. While built-in retrieval won't replace vector databases for complex applications, it significantly lowers the barrier to entry for simpler RAG use cases.