Model profile
OpenAI

OpenAI: GPT-4o (2024-05-13)

OpenAI: GPT-4o (2024-05-13) is a mid-range-priced multimodal generalist from OpenAI with fast runtime profile, extended context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalFast latencyExtended contextMid-range pricing
Intelligence
14.5

Benchmark blend

Coding
24.2

Dev workflow signal

Context
128K Tokens

Extended

Input Price
$5.00

Mid-range tier

Decision snapshot
45

OpenAI: GPT-4o (2024-05-13) currently reads as a mid-range multimodal option with extended context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Multimodal
Latency tier
Fast
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
14.5
15

General reasoning and benchmark headroom.

Limited
Speed
95 tok/s
81

TTFT 0.56s

Above average
Context
128K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$5.00
62

$15.00 output / 1M

Competitive

Editorial Profile

OpenAI: GPT-4o (2024-05-13) 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 24Math score 79Vision enabled

GPT-4o ("o" for "omni") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as...

Identity

OpenAI multimodal profile

Positioning

Long-context research / Multimodal with extended context and fast 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.

  • Latency and throughput look responsive enough for interactive loops.

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

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
14.5
MMLU Pro
74.0%
GPQA
52.6%
HLE
2.8%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
24.2
LiveCodeBench
0.334
SciCode
30.9%
Math

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

AIME
11.0%
Math 500
79.1%

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
128K Tokens
Vision
Enabled
Modalities
file, image, text
Tokenizer
GPT
Max Completion
4096
Moderation
Yes
Supported Parameters
frequency_penaltylogit_biaslogprobsmax_completion_tokensmax_tokenspresence_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_logprobstop_pweb_search_options
Input Modalities
fileimagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$5.00
Output
per 1M output tokens
$15.00
Blended
AA 3:1 mix
$7.50

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

Metadata

Raw source tables at the end

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