POST /v1/chat/completionsGPT-4o Mini
gpt-4o-miniGPT-4o Mini is the fast and affordable small model in the GPT-4o family. OpenAI docs position it for focused tasks with text and image input, structured outputs, fine-tuning, and distillation workflows. It is best introduced as a lightweight multimodal production model rather than a reduced copy of GPT-4o.
Total Context
128Ktokens
Max Output
16.4Ktokens
Released
Jul 18, 2024
Modalities
GPT-4o Mini Price
| Input Price | Output Price | Cache Read |
|---|---|---|
| $0.15/M | $0.6/M | $0.075/M |
GPT-4o Mini API
GPT-4o Mini Benchmark
GPT-4o mini
6.9
/100
Artificial Analysis Intelligence Index
Artificial Analysis broad capability aggregate
Index score
14.7
/100
Artificial Analysis Math Index
Artificial Analysis math reasoning aggregate
Index score
Knowledge & Reasoning
MMLU-Pro
Advanced multi-task knowledge
64.8%
GPQA
Advanced science problem solving
42.6%
HLE
Broad expert-level exam set
4%
Coding & Engineering
LiveCodeBench
Live coding problems
23.4%
SciCode
Scientific coding challenges
22.9%
Math
MATH-500
Advanced math problem solving
78.9%
AIME
Competition math problems
11.7%
AIME 2025
Competition math problems
14.7%
Instruction Following & Agent Tasks
IFBench
Prompt constraint adherence
31.0%
Metrics sourced from Artificial Analysis
Frequently asked questions about GPT-4o Mini
Understand what GPT-4o Mini is, its best uses, distinguishing strengths, practical tradeoffs, and safe TokenHub integration guidance.
Where does GPT-4o Mini sit within its provider’s model family?+
GPT-4o Mini is a compact omni model designed for fast, economical text and image workloads. It has been retired from ChatGPT, while API availability may remain; check TokenHub’s current listing.
Which production scenarios suit GPT-4o Mini?+
Best-fit scenarios include customer-support automation, large-scale classification and routing, and analysis of text and visual inputs. Test representative inputs and define measurable acceptance criteria before production.
What makes GPT-4o Mini stand out for large-scale classification and routing?+
Key strengths include fast response times, cost-efficient scaling, and combined text and image understanding. This combination is especially useful for large-scale classification and routing.
What tradeoffs should developers consider with GPT-4o Mini?+
Consider another model when the task requires the provider’s strongest reasoning capability, quality matters more than speed or cost, or the workflow cannot include human review for important decisions. Verify important factual, legal, financial, medical, or operational outputs with qualified human review.
How can a team safely start using GPT-4o Mini on TokenHub?+
In TokenHub, select the exact model identifier displayed for GPT-4o Mini, use the endpoint documented for your account, and authenticate with your TokenHub credentials. Check the current TokenHub documentation for supported text and image inputs, because platform exposure can differ from the provider’s full model capabilities.
Media and Discussions
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