Model profile
OpenAI

OpenAI: o1

OpenAI: o1 is a premium-priced multimodal generalist from OpenAI with heavy runtime profile, large context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalHeavy latencyLarge contextPremium pricing
Intelligence
30.8

Benchmark blend

Coding
20.5

Dev workflow signal

Context
200K Tokens

Large

Input Price
$15.00

Premium tier

Decision snapshot
40

OpenAI: o1 currently reads as a premium multimodal option with large context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Multimodal
Latency tier
Heavy
Price tier
Premium
Source coverage
OpenRouterArtificial AnalysisVision signal

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
30.8
31

General reasoning and benchmark headroom.

Limited
Speed
100 tok/s
36

TTFT 20.18s

Limited
Context
200K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$15.00
38

$60.00 output / 1M

Expensive

Editorial Profile

OpenAI: o1 in one narrative

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

Use-case specificCoding score 21Math score 97Vision enabled

The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...

Identity

OpenAI multimodal profile

Positioning

Long-context research / Multimodal with large context and heavy 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 profile is better for deliberate runs than rapid back-and-forth chat.

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.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
30.8
MMLU Pro
84.1%
GPQA
74.7%
HLE
7.7%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
20.5
LiveCodeBench
0.679
SciCode
35.8%
Math

Formal reasoning, structured problem solving, and competition-style math.

AIME
72.3%
Math 500
97.0%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
70.3%
TAU2
62.6%
TerminalBench Hard
12.9%
LCR
59.3%

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
include_reasoningmax_tokensreasoningresponse_formatseedstructured_outputstool_choicetools
Input Modalities
fileimagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$15.00
Output
per 1M output tokens
$60.00
Blended
AA 3:1 mix
$26.25

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

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.