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Model profile
OpenAI

OpenAI: o3 Deep Research

OpenAI: o3 Deep Research is a premium-priced multimodal generalist from OpenAI with partial runtime data, large context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalN/A latencyLarge contextPremium pricing
Vision modelsAgentic models
Intelligence
N/A

Benchmark blend

Coding
N/A

Dev workflow signal

Context
200K Tokens

Large

Input Price
$10.00

Premium tier

Decision snapshot
63

OpenAI: o3 Deep Research currently reads as a premium multimodal option with large context and a partially published runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Multimodal
Latency tier
N/A
Price tier
Premium
Source coverage
OpenRouterVision signal
Open compare hubSee leaderboard

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
N/A
N/A

General reasoning and benchmark headroom.

Unavailable
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
200K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$10.00
38

$40.00 output / 1M

Expensive

Editorial Profile

OpenAI: o3 Deep Research in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Selective fitCoding score N/AMath score N/AVision enabled

o3-deep-research is OpenAI's advanced model for deep research, designed to tackle complex, multi-step research tasks. Note: This model always uses the 'web_search' tool which adds additional cost.

Identity

OpenAI multimodal profile

Positioning

Long-context research / Multimodal with large context and partially published runtime.

Cost posture

Premium spend profile. Best when the upside justifies tighter budget control.

Strengths

  • Large context headroom supports repo-wide prompts and long research sessions.

  • Vision-capable routing opens up multimodal review and extraction workflows.

Tradeoffs

  • Pricing sits in premium territory, so bulk usage needs tighter cost controls.

  • Latency data is incomplete, so interactive responsiveness is harder to rank confidently.

Best fit

  • Image-grounded review, multimodal extraction, and UI audit workflows.

  • Long-context summarization, repo analysis, and policy or document review.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

No benchmark data is available for this model yet.

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
200K Tokens
Vision
Enabled
Modalities
file, image, text
Tokenizer
GPT
Max Completion
100000
Moderation
Yes
Supported Parameters
frequency_penaltyinclude_reasoninglogit_biaslogprobsmax_tokenspresence_penaltyreasoningresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_logprobstop_p
Input Modalities
fileimagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$10.00
Output
per 1M output tokens
$40.00
Blended
AA 3:1 mix
N/A

This model trades into premium territory. It makes sense when capability upside matters more than raw volume efficiency.

OR Web Search Price
$0.0100
OR Cache Read
$0.00

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.