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Model profile
openai

OpenAI: GPT Audio Mini

OpenAI: GPT Audio Mini is a budget text-first model from openai with a heavy runtime profile, extended context posture, and the clearest fit around long-context research / reasoning.

Best for: Long-context research / ReasoningHeavy latencyExtended contextBudget pricing
Intelligence
N/A

Benchmark blend

Coding
N/A

Dev workflow signal

Context
128K Tokens

Extended

Input Price
$0.60

Budget tier

Decision snapshot
56

OpenAI: GPT Audio Mini currently reads as a budget text-first option with extended context and a heavy runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Reasoning
Latency tier
Heavy
Price tier
Budget
Source coverage
OpenRouter
Compare this modelSee 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
44

General reasoning and benchmark headroom.

Situational
Speed
N/A
46

Latency data is partial.

Situational
Context
128K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.60
86

$2.40 output / 1M

Efficient

Editorial Profile

OpenAI: GPT Audio Mini in one narrative

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

Selective fitCoding score 40Math score 36

A cost-efficient version of GPT Audio. The new snapshot features an upgraded decoder for more natural sounding voices and maintains better voice consistency. Input is priced at $0.60 per million tokens and output is priced at $2.40 per million tokens.

Identity

openai text-first profile

Positioning

Long-context research / Reasoning with extended context and heavy 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.

Tradeoffs

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

  • Latency profile is better for deliberate runs than rapid back-and-forth chat.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit

  • 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
128K Tokens
Vision
Text-first
Modalities
text, audio->text, audio
Tokenizer
GPT
Max Completion
16384
Moderation
Yes
Supported Parameters
frequency_penaltylogit_biaslogprobsmax_tokenspresence_penaltyresponse_formatseedstopstructured_outputstemperaturetop_logprobstop_p
Input Modalities
textaudio
Output Modalities
textaudio
Price architecture
Input
per 1M input tokens
$0.60
Output
per 1M output tokens
$2.40
Blended
AA 3:1 mix
N/A

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

OR Audio Price
$0.0000

Metadata

Raw source tables at the end

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