Community Sentiment
Mostly Positive
Based on Reddit reviewsCommunity Verdict
Best for Deep Research
Input Modalities
Text, ImagesOutput Modalities
TextPrice / 1M tokens
$10$40
InputOutput
Best For
In-depth legal research and synthesis
Academic and scientific literature reviews
Market analysis with multi-source aggregation
Large-scale internal data reporting
Complex reasoning with web access
MCP connector tasks using private data
File search across internal vector stores
High-stakes analysis where depth matters
Max output needs (up to 100k tokens)
Avoid For
Time-sensitive tasks (slow completion)
High-volume, cost-sensitive workloads
Simple queries (overkill)
Tasks needing precise confidence levels
Apps requiring source reliability checks
Real-time or interactive use
When o4-mini-deep-research suffices
200,000 context window
100,000 max output tokens
Jun 1, 2024 knowledge cutoff
Reasoning supported
Parameters
While OpenAI documents a unified parameter set for the Chat Completions and Responses APIs, each model supports only a limited subset of model-specific parameters and values. This table lists the supported model-specific o3 Deep Research API parameters and allowed values.
| Parameter | Description | Path | Supported Values |
|---|---|---|---|
| Model | Selects the model version for the request | model | o3-deep-research |
| Message roles | Defines the role of the message in the input | messages[].role | developersystemuserassistant |
| Reasoning effort | Controls the depth of internal reasoning used by the model | reasoning_effort | medium |
| Reasoning summary | Controls whether the model produces a concise or detailed reasoning summary | reasoning_summary | detailedauto |
| Max output tokens | Max output tokens the model may generate | max_completion_tokens | 16 .. 100,000 |
| Verbosity | Controls how brief or detailed the generated response is | verbosity | medium |
| Output format | Specifies the output format, including structured JSON schemas | response_format | textjson_objectjson_schema |
| Temperature | Controls how random or deterministic the output is | temperature | Not supported |
| Top P | Controls how diverse the output tokens are | top_p | Not supported |
| Presence penalty | Encourages the model to introduce new topics | presence_penalty | Not supported |
| Frequency penalty | Reduces repetition of the same words or phrases | frequency_penalty | Not supported |
Pricing
o3 Deep Research API pricing is based on token usage for input and output. Prices are listed per 1M tokens, with lower rates for cached input. Tool-specific features may add per-call fees.
Text tokens
Per 1M tokens
Input$10.00
Cached input$2.50
Output$40.00
Example costs (o3 Deep Research)
TASK
APPROX COST
Comprehensive legal research
~$5.00–$20.00
Scientific literature review
~$3.00–$15.00
Market analysis report
~$2.00–$10.00
Modalities
What the model can accept and produce
TextInput and output
ImagesInput only
AudioNot supported
VideoNot supported
Features
Platform-level capabilities
StreamingSupported
Function callingNot supported
Structured outputsNot supported
Fine-tuningNot supported
DistillationNot supported
Predicted outputsNot supported
Tools
Tools supported by this model when using the Responses API.
Web searchSupported
File searchSupported
Image generationNot supported
Code interpreterSupported
MCPSupported
Computer useNot supported
Snapshots
o3 Deep Research model snapshots ensure stable behavior by locking a specific version. See all available snapshots and aliases below.
o3-deep-research
↪ o3-deep-research-2025-06-26o3-deep-research-2025-06-26
FAQs
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