Model profile
OpenAI

OpenAI: o3

OpenAI: o3 is a mid-range-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 contextMid-range pricing
Intelligence
38.4

Benchmark blend

Coding
38.4

Dev workflow signal

Context
200K Tokens

Large

Input Price
$2.00

Mid-range tier

Decision snapshot
50

OpenAI: o3 currently reads as a mid-range 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
Mid-range
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
38.4
38

General reasoning and benchmark headroom.

Limited
Speed
97 tok/s
35

TTFT 7.86s

Limited
Context
200K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$2.00
62

$8.00 output / 1M

Competitive

Editorial Profile

OpenAI: o3 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 38Math score 88Vision enabled

o3 is a well-rounded and powerful model across domains. It sets a new standard for math, science, coding, and visual reasoning tasks. It also excels at technical writing and instruction-following....

Identity

OpenAI multimodal profile

Positioning

Long-context research / Multimodal with large context and heavy runtime.

Cost posture

Balanced spend profile. Easier to justify in mixed production and exploration workloads.

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

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

Tradeoffs
  • Costs look manageable, but still deserve attention in always-on agents or batch jobs.

  • 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
38.4
MMLU Pro
85.3%
GPQA
82.7%
HLE
20.0%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
38.4
LiveCodeBench
0.808
SciCode
41.0%
Math

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

Math Index
88.3
AIME
90.3%
AIME 2025
88.3%
Math 500
99.2%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
71.4%
TAU2
80.7%
TerminalBench Hard
37.1%
LCR
69.3%

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
$2.00
Output
per 1M output tokens
$8.00
Blended
AA 3:1 mix
$3.50

This model sits in a balanced spend range. It is easier to justify across both production and exploratory workflows.

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.