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GPT-4o mini

OpenAI · GPT-4o

Lower-cost GPT-4o tier for high-volume multimodal assistant and automation workloads.

Part of GPT family · Other versions: GPT-5.3-Codex , GPT-5.3 , GPT-5.4-Pro , GPT-5.4 , GPT-5.2-Codex , GPT-5.2-Pro , GPT-5.2 , GPT-5-Codex , GPT-5 nano , GPT-5 mini , GPT-5 , GPT-4.1 , GPT-4o mini Transcribe , GPT-4o mini TTS , GPT-4o Transcribe , GPT-4o
Type
multimodal
Context
128K tokens
Max Output
16K tokens
Status
current
Input
$0.15/1M tok
Output
$0.6/1M tok
API Access
Yes
License
proprietary
multimodal cost-efficient assistant automation production
Released July 2024 · Updated March 6, 2026

Overview

Freshness note: Model capabilities, limits, and pricing can change quickly. This profile is a point-in-time snapshot last verified on February 15, 2026.

GPT-4o mini is OpenAI’s cost-efficient GPT-4o tier for production workloads where volume and latency matter. OpenAI still documents it as an API-available multimodal model, even as public product defaults have moved forward to newer GPT-5 family models.

Capabilities

The model performs well on concise reasoning, extraction, summarization, and common workflow automation tasks. It supports multimodal patterns at materially lower operating cost than higher-end tiers, which keeps it relevant for cost-sensitive API systems.

Technical Details

GPT-4o mini uses large-context handling similar to broader GPT-4o family behavior but targets cost-performance optimization. It is effective when prompts are structured and outputs are validated.

Pricing & Access

OpenAI’s current pricing docs still list GPT-4o mini at 0.15per1Minputtokensand0.15 per 1M input tokens and 0.60 per 1M output tokens. It remains available through OpenAI API model endpoints, though teams should verify exact feature availability by account tier and surface.

Best Use Cases

Strong fit for high-throughput support assistants, operations automations, content normalization, and product features requiring reliable but cost-sensitive inference.

Comparisons

Compared with GPT-4o, GPT-4o mini trades some quality headroom for significantly lower cost. Compared with GPT-5 nano, selection depends on required multimodal depth and ecosystem strategy. Compared with Gemini 2.5 Flash-Lite, both target efficient scale with different platform tradeoffs.