Model profile
OpenAI

OpenAI: GPT-4o-mini

OpenAI: GPT-4o-mini is a budget-priced multimodal generalist from OpenAI with balanced runtime profile, extended context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalBalanced latencyExtended contextBudget pricing
Intelligence
12.6

Benchmark blend

Coding
0.234

Dev workflow signal

Context
128K Tokens

Extended

Input Price
$0.15

Budget tier

Decision snapshot
46

OpenAI: GPT-4o-mini currently reads as a budget multimodal option with extended context and a balanced runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Multimodal
Latency tier
Balanced
Price tier
Budget
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
12.6
13

General reasoning and benchmark headroom.

Limited
Speed
52 tok/s
66

TTFT 0.56s

Competitive
Context
128K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.15
86

$0.60 output / 1M

Efficient

Editorial Profile

OpenAI: GPT-4o-mini 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 23Math score 15Vision enabled

GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable...

Identity

OpenAI multimodal profile

Positioning

Long-context research / Multimodal with extended context and balanced runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

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

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

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

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
12.6
MMLU Pro
64.8%
GPQA
42.6%
HLE
4.0%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.234
SciCode
22.9%
Math

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

Math Index
14.7
AIME
11.7%
AIME 2025
14.7%
Math 500
78.9%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
31.0%

Extra benchmark cuts are not available for this category 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
128K Tokens
Vision
Enabled
Modalities
file, image, text
Tokenizer
GPT
Max Completion
16384
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
$0.15
Output
per 1M output tokens
$0.60
Blended
AA 3:1 mix
$0.26

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

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.