OpenAIOpenAI·text

GPT-4o Mini

VisionWeb searchFunction calling
Advertised as gpt-4o-minigpt-4o-mini-2024-07-18openai-gpt-4o-mini-2024-07-18
Quick reference
GPT-4o Mini — TLDR
  • 🧠 Brings much of GPT-4o capability to cost-efficient small-model workloads
  • 📏 128K-token context window, up to 16K output tokens
  • 👁️ Accepts text and image inputs, produces text outputs
  • 🔧 Strong function calling and structured outputs support
  • 🌐 Built-in web search capability in this catalog deployment
  • 📚 Knowledge cutoff October 2023; shares GPT-4o tokenizer
  • ⚡ Optimized for high-volume, low-latency chained and real-time tasks
  • 🆕 Surpasses GPT-3.5 Turbo on academic and multimodal benchmarks
💰 Best price on AntSeed
$0.017 / $0.068
per 1M · cheapest in / out
📏 Context
128K tokens
🐜 Sellers
8
advertising on AntSeed
Provider

OpenAI is an American artificial intelligence research organization headquartered in San Francisco, structured as both a for-profit public benefit corporation and a nonprofit foundation. The lab developed the GPT family of large language models, the DALL-E image generation…

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About this model

GPT-4o Mini is OpenAI's compact, cost-efficient member of the GPT-4 "omni" family, designed to bring much of GPT-4o's capability to high-volume, latency-sensitive workloads. OpenAI positions it as a fast, affordable small model for focused tasks, accepting both text and image inputs and producing text outputs including Structured Outputs. It carries a 128K-token context window, supports up to 16,384 output tokens per request, and has knowledge up to October 2023.

Compared with its own predecessor in the small-model line, GPT-3.5 Turbo, OpenAI reports that GPT-4o Mini surpasses it on academic benchmarks across both textual intelligence and multimodal reasoning, adds vision support, and delivers improved long-context and function-calling performance. It also shares the improved tokenizer used by GPT-4o, making non-English text handling more efficient, and model outputs from larger models can be distilled into it for similar results at lower cost.

Within this catalog's family lineage, GPT-4o Mini has since been succeeded by GPT-5.4 Mini, which OpenAI describes as one of its most capable small models with a 400K context window and broader tool support including web search, file search, and computer use. According to OpenAI, GPT-5.4 mini consistently outperforms earlier small models at similar latencies, reflecting the family's generational progress.

For developers, GPT-4o Mini remains suited to chaining or parallelizing multiple model calls, processing large context volumes, and powering real-time chatbots where cost and speed matter.

Sources
developers.openai.comGPT-4o mini Model | OpenAI API· developers.openai.comopenai.comGPT-4o mini: advancing cost-efficient intelligence | OpenAI· openai.complatform.openai.como4-mini Model | OpenAI API· platform.openai.com

This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.

Usage on AntSeed
Tokens served
1.26M
input + output
Requests
483
settled calls
Buyers
7
distinct, on this model
Sellers used
6
of 8 advertising
Settled
$0.12
gross USDC, this model
Sellers serving GPT-4o Mini (8)compare on the network explorer →
SellerReputation↓RoutingInput $/MCached $/MOutput $/MCategoriesAPI

"Best price" and the seller table are live AntSeed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = buyer trust score (0-100, the AntSeed SDK's own formula). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on AntSeed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.