GPT-5 nano (minimal)

GPT-5 nano (minimal)

OpenAItext

API Available

Specifications

Context Window

Input Price/1M

$0.05

Output Price/1M

$0.40

Parameters

Speed

163 tok/s

Latency (TTFT)

614ms

Benchmarks

GPT-5 nano (minimal) results on the main AI model evaluation benchmarks. Higher scores indicate better performance.

Agentic

BenchmarkScoreMaximumMethodology
Terminal-Bench Hard7.0100.0

Coding

BenchmarkScoreMaximumMethodology
LiveCodeBench47.0100.0Artificial Analysis official API
SciCode29.0100.0
AA Coding Index14.2100.0Artificial Analysis official API

Knowledge

BenchmarkScoreMaximumMethodology
MMLU-Pro56.0100.0

Long Context

BenchmarkScoreMaximumMethodology
AA-LCR20.0100.0

Math

BenchmarkScoreMaximumMethodology
AA Math Index27.3100.0Artificial Analysis official API
AIME 202527.0100.0Artificial Analysis official API

overall

BenchmarkScoreMaximumMethodology
AA Intelligence Index13.8100.0Artificial Analysis official API

Reasoning

BenchmarkScoreMaximumMethodology
MMLU Pro55.6100.0Artificial Analysis official API
GPQA Diamond43.0100.0Artificial Analysis official API
IFBench33.0100.0
HLE4.0100.0

Tool Use

BenchmarkScoreMaximumMethodology
Tau²-Bench26.0100.0

Information

Release date
August 07, 2025
Tool Calling
❌ Not supported
Vision
❌ Not supported
Audio
❌ Not supported

Full Analysis: GPT-5 nano (minimal)

What is GPT-5 nano (minimal)?

GPT-5 nano (minimal) is an AI model developed by OpenAI, classified as a text model. It focuses on text processing and natural language generation. As a proprietary model, it is available via OpenAI's cloud API.

Pricing & Costs in 2026

GPT-5 nano (minimal) is usage-based, priced at $0.05/1M input tokens and $0.4/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.

Benchmarks & Performance

GPT-5 nano (minimal) was evaluated on 14 different benchmarks, covering categories like Agentic, Coding, Knowledge, Long Context, Math, overall, Reasoning, Tool Use. Results show moderate performance across available evaluations.

It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.

Recommended Use Cases

GPT-5 nano (minimal) specializes in text, offering advanced capabilities for creating and processing text content.

Comparison with Alternatives

In the 2026 AI model ecosystem, GPT-5 nano (minimal) competes directly with similarly capable models. Key competitors include Claude (Anthropic), Gemini (Google), and open source models like Llama (Meta) and Qwen (Alibaba). The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.

For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.

Frequently Asked Questions

What is GPT-5 nano (minimal)?

GPT-5 nano (minimal) is an AI model developed by OpenAI. It is a text model.

How much does GPT-5 nano (minimal) cost?

GPT-5 nano (minimal) costs $0.05/1M input tokens and $0.4/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.

How does GPT-5 nano (minimal) compare with other models?

In available benchmarks, GPT-5 nano (minimal) scored: Terminal-Bench Hard: 7/100, LiveCodeBench: 47/100, SciCode: 29/100. See the full table above for a detailed comparison.

Is GPT-5 nano (minimal) open source?

No, GPT-5 nano (minimal) is a proprietary model from OpenAI. It is available via cloud API. For open source alternatives, check our open source model ranking.

What is GPT-5 nano (minimal) best for?

GPT-5 nano (minimal) excels at general-purpose language tasks.

Last updated: June 01, 2026 View methodology →