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o3 Deep Research
- Text Generation
o3 Deep Research is an OpenAI model variant optimized for autonomous, long-horizon research tasks that combine web browsing, data analysis, and report generation. It focuses on producing thorough, sourced write‑ups rather than fast conversational responses.
About the model
What is o3 Deep Research?
o3 Deep Research is a specialized OpenAI reasoning model (based on the o3 family) designed to run multi-step research workflows that browse the web, analyze sources, and synthesize them into comprehensive reports. Its main use cases include in‑depth market or technical landscape reviews where the system must search widely, compare conflicting information, and return a structured, cited summary. It is also used for professional‑style briefing documents, such as consulting-style memos or policy analyses that require methodical source gathering and justification of claims. It builds on OpenAI’s o3 reasoning models, which themselves succeeded the earlier o1 line and power the ChatGPT Deep Research product.
Model capabilities
5 Core Capabilities
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Deep Web Research
Performs multi-step web research, aggregating, comparing, and citing sources to answer complex, open-ended questions thoroughly and transparently.
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Complex Reasoning
Builds detailed reasoning chains, tests alternative hypotheses, and explains how conclusions were reached for difficult analytical or investigative tasks.
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Evidence Synthesis
Reads across many documents, extracts key evidence, reconciles conflicts, and produces structured summaries with explicit source-backed claims.
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Multilingual Sources
Consults and combines information from sources in multiple languages, while returning a unified English explanation of findings and uncertainties.
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Result Auditing
Surfaces citations, reasoning steps, and limitations so users can verify facts, trace decisions, and understand confidence levels in results.
Use cases
6 Most Valuable Use Cases
- Financial market research
- Scientific literature reviews
- Legal and policy analysis
- Competitive business intelligence
- Technical due diligence
- Engineering design research
Transparent pricing
Cost Comparison
LLM API delivers the lowest cost and highest performance access to o3 Deep Research–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~180ms | ~80 tps | 99.99% | $2.00 | $10.00 | 200K tokens |
| OpenAI | Global | ~400ms | ~40 tps | 99.9% | ~$5.00 | ~$25.00 | 200K tokens |
| Azure OpenAI | US East / EU West | ~450ms | ~35 tps | 99.9% | ~$5.50 | ~$27.00 | 200K tokens |
| Anthropic (Claude Opus-equivalent) | Global | ~500ms | ~30 tps | 99.9% | ~$6.00 | ~$30.00 | 200K tokens |
| Google (Gemini 1.5 Pro-equivalent) | Global | ~480ms | ~32 tps | 99.9% | ~$5.50 | ~$28.00 | 200K tokens |
Performance benchmarks
Technical Specifications
| Metric | o3 Deep Research (OpenAI) | GPT-4.1 (OpenAI) | Claude 3.5 Sonnet (Anthropic) |
|---|---|---|---|
| Avg Latency | ~8s | ~2.5s | ~3s |
| Context Window | 200K | 128K | 200K |
| Input Price ($/1M) | ~$5.00 | $5.00 | $3.00 |
| Output Price ($/1M) | ~$15.00 | $15.00 | $15.00 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | ~8 tps | ~30 tps | ~25 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 38.5B
- Prompt tokens processed (last 30 days)
- 12.4M
- API requests served (last 30 days)
- 44.0B
- Completion tokens generated (last 30 days)
- 99.8%
- Avg API uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Intelligent Model Routing
Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying.
One endpoint, any model -
Cost-Aware Orchestration
Optimize spend by mixing premium and budget models per request, enforcing price caps, and simulating costs before deploying traffic at scale.
More output, less spend -
Resilient Fallback Flows
Design multi-provider fallback chains so timeouts, quota limits, or provider outages transparently fail over—keeping your AI features online and predictable.
Never go dark -
Deep LLM Observability
Get unified traces, logs, and metrics for every call—prompt, model, latency, and cost—so you can debug issues and optimize performance in production.
See every token -
Task-Level Abstractions
Define tasks like chat, RAG, or tools once, then swap underlying models and providers without rewriting business logic or prompt wiring.
Code to tasks, not models -
High-Throughput Batch APIs
Send large volumes of jobs in a single request with automatic partitioning, retries, and status tracking to cut coordination overhead and boost throughput.
Ship millions of calls
Decision guide
When to Use — When NOT to Use
Use it if...
- You need thorough, multi-step research on complex questions where accuracy matters more than speed.
- You need cross-checking multiple sources and synthesizing them into a rigorous written answer.
- Your use case involves drafting long-form reports, briefs, or memos that require citations.
- Your use case involves exploring unfamiliar domains and asking the model to investigate options.
- You need help designing or evaluating research plans, methodologies, or comparative analyses.
- Your use case involves explaining trade-offs and uncertainties rather than giving a quick heuristic answer.
- You need an agentic assistant that can iteratively refine reasoning and double-check prior conclusions.
Avoid if...
- You need low-latency responses for interactive chat, coding assistance, or real-time decision support.
- Your workload requires processing a very high volume of short queries at minimal cost.
- You need deterministic, fixed-time responses rather than variable latency from extended reasoning.
- Your workload requires streaming outputs token-by-token for live user interfaces or tools.
- You need on-device or edge inference where external web research is impossible or restricted.
- Your workload requires strict offline operation without reaching out to external information sources.
- You need a simple classification or extraction model where deep research is unnecessary overhead.
FAQ
Frequently Asked Questions
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What is o3 Deep Research?
o3 Deep Research is an OpenAI reasoning model optimized for long-horizon, tool-using research tasks, focusing on accuracy over speed.
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What is o3 Deep Research best suited for?
It excels at deep research, multi-step reasoning, reading large document sets, and producing sourced, structured reports rather than quick chat-style responses.
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How is o3 Deep Research priced on LLM.API?
Pricing is set by LLM.API as a pass-through or markup over OpenAI’s o3 Deep Research rates; check LLM.API’s pricing page for current per-token costs.
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What context window does o3 Deep Research support?
LLM.API exposes the maximum context window supported by OpenAI’s o3 Deep Research variant in use; consult the model docs for the latest token limit.
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How fast is o3 Deep Research compared to chat-optimized models?
o3 Deep Research is significantly slower and higher-latency than lightweight chat models, as it performs extensive internal reasoning and tool-calling steps.
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What input and output modalities does o3 Deep Research support via LLM.API?
Through LLM.API, o3 Deep Research is typically used for text-in, text-out workflows, with any tool calls or retrieval orchestrated by the gateway.
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How do I call o3 Deep Research through LLM.API?
Use the LLM.API completion or chat endpoint with the model name set to the configured o3 Deep Research identifier in your project or workspace settings.
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How does o3 Deep Research compare to o3-mini or other fast models?
o3 Deep Research usually delivers higher-quality, more thorough reasoning than o3-mini or similar fast models, but with higher cost and slower responses.
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Does o3 Deep Research support tools or retrieval when used via LLM.API?
Yes, LLM.API can orchestrate tools, retrieval, or custom agents around o3 Deep Research if you configure tool schemas or workflows in the platform.
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What are the main limitations of o3 Deep Research?
Limitations include higher latency, higher cost per request, possible outdated knowledge, and occasional reasoning errors that still require human review.