Gpt-5-Mini vs Gpt-5-Nano

Who wins: Gpt-5-Mini or Gpt-5-Nano?

Choose GPT-5 Nano first if extreme cost optimization on text-only tasks is the primary constraint; otherwise, choose GPT-5 Mini first if higher reasoning capabilities, multimodal inputs, or complex problem-solving are required.

Based on our analysis across 5 dimensions with 8 sources, Gpt-5-Mini scores 8.0/10 overall while Gpt-5-Nano scores 7.5/10 overall.

Should I choose Gpt-5-Mini or Gpt-5-Nano?

Verdict: Choose GPT-5 Nano first if extreme cost optimization on text-only tasks is the primary constraint; otherwise, choose GPT-5 Mini first if higher reasoning capabilities, multimodal inputs, or complex problem-solving are required.

Choose GPT-5 Mini for multimodal tasks and superior reasoning performance, or select GPT-5 Nano for five times cheaper text-only processing across high-throughput workloads.

GPT-5 Mini delivers superior benchmark performance and broader capabilities, ranking #59 overall with an evaluation score of 0.825 on StackUnseen, 0.60 on SWE-bench Verified, 0.823 on GPQA, and 0.982 on ProLLM Summarization, alongside native multimodal support and adjustable thinking effort at $0.25 per million input tokens and $2.00 per million output tokens. In contrast, GPT-5 Nano is five times cheaper at $0.05 per million input tokens and $0.40 per million output tokens, but trades off performance with an overall rank of #120, a StackUnseen score of 0.604, a LiveBench Agentic Coding score of 0.28, and a restriction to text-only modality. While both models support 128,000 maximum output tokens, function calling, prompt caching, and structured outputs, exact latency figures remain unverified and context window sizes remain inconsistent across sources (ranging from 272K to 400K tokens for GPT-5 Mini and 131K to 400K tokens for GPT-5 Nano).

Best for Gpt-5-Mini

• Complex reasoning and academic benchmarks requiring higher accuracy
• Multimodal input and processing tasks
• High-accuracy code generation requiring SWE-bench performance
• Adjustable thinking effort reasoning workflows
• High-accuracy document summarization

Best for Gpt-5-Nano

• High-throughput, budget-sensitive API deployments
• Lightweight agentic and text-only tasks
• Workloads prioritizing the lowest per-token operational costs
• Cost-effective text-only structured output and tool integration

When not to compare directly

Do not compare GPT-5 Mini and GPT-5 Nano directly on precise context window limits or raw latency and inference speed, as exact speed metrics are undocumented and context window specifications conflict across sources (272K–400K for GPT-5 Mini vs. 131K–400K for GPT-5 Nano).

What are the key differences between Gpt-5-Mini and Gpt-5-Nano?

• API Pricing & Cost Efficiency

  GPT-5 Nano costs $0.05 per million input tokens and $0.40 per million output tokens, making it five times cheaper than GPT-5 Mini across both input and output pricing.

  Gpt-5-Mini: GPT-5 Mini is priced at $0.25 per million input tokens and $2.00 per million output tokens for API access.

  Gpt-5-Nano: GPT-5 Nano offers highly budget-friendly rates of $0.05 per million input tokens and $0.40 per million output tokens.

  Scores — Gpt-5-Mini: 6.5/10, Gpt-5-Nano: 9.5/10

  Determines the financial feasibility of deploying the models across high-throughput production environments and large-scale automated pipelines.

  Sources: GPT-5 Mini - Specs, Benchmarks & Details, GPT 5 Nano by OpenAI — Pricing, Specs & API Access

• Reasoning & Benchmark Performance

  GPT-5 Mini achieves a higher ProLLM StackUnseen benchmark score of 0.825 and an overall rank of #59, whereas GPT-5 Nano scores 0.604 on StackUnseen with an overall rank of #120.

  Gpt-5-Mini: GPT-5 Mini is a reasoning model featuring adjustable thinking effort, ranking #59 overall with an evaluation benchmark score of 0.825 on StackUnseen, 0.60 on SWE-bench Verified, 0.823 on GPQA, and a #1 rank on ProLLM Summarization with 0.982.

  Gpt-5-Nano: GPT-5 Nano is designed for lightweight agentic tasks and low-latency workloads, achieving an overall rank of #120 alongside benchmark scores of 0.604 on StackUnseen, 0.954 on ProLLM Summarization, 0.76 on MMLU Pro, and 0.28 on LiveBench Agentic Coding.

  Scores — Gpt-5-Mini: 8.5/10, Gpt-5-Nano: 6/10

  Assesses model accuracy and capability when handling complex multi-step reasoning, coding, and specialized academic benchmarks.

  Sources: GPT-5 Mini - Specs, Benchmarks & Details, GPT-5 Nano - Specs, Benchmarks & Details

• Latency & Inference Speed

  While GPT-5 Nano is explicitly architected as a fast-response engine for low-latency tasks and GPT-5 Mini is positioned for high-volume conversational workloads, neither model provides concrete latency or inference speed benchmark numbers in the evidence.

  Gpt-5-Mini: GPT-5 Mini is a lightweight reasoning model released on 13 Nov 2025 that supports up to 128,000 max output tokens with an estimated 100B parameters (or 3B–10B in conflicting reports), but specific latency or inference speed metrics (such as tokens per second or time-to-first-token) are unavailable in the evidence.

  Gpt-5-Nano: GPT-5 Nano is engineered for environments where low latency and high throughput are primary constraints, featuring a dense multi-head attention architecture and context lengths reported between 131K and 400K tokens, though direct measured speed and latency benchmarks are unavailable.

  Inference speed is critical for interactive conversational applications, real-time agent responses, and latency-sensitive deployments.

  Sources: GPT-5 Mini - Specs, Benchmarks & Details, GPT-5 Nano - Specs, Benchmarks & Details

• Context Window & Output Capacity

  While both GPT-5 Mini and GPT-5 Nano support up to 128,000 output tokens, their full context window capacities cannot be definitively contrasted due to conflicting source figures, ranging from 272K to 400K tokens for Mini and 131K to 400K tokens for Nano.

  Gpt-5-Mini: OpenAI's GPT-5 Mini features a maximum output capacity of 128,000 (128K) tokens, while its reported context window presents conflicting specifications across sources, listed as 400K tokens on APXML and 272K tokens on Inworld.

  Gpt-5-Nano: OpenAI's GPT-5 Nano supports up to 128,000 (128K) output tokens, but its context window is subject to conflicting data, listed at 400K tokens (with another table entry showing 272K) by Inworld versus 131K tokens on APXML.

  Defines how much input history, document context, and generated output can be processed in a single interaction.

  Sources: GPT-5 Mini - Specs, Benchmarks & Details, GPT 5 Nano by OpenAI — Pricing, Specs & API Access

• Modality & Advanced Capabilities

  While both models support function calling, prompt caching, and structured output schemas, GPT-5 Mini provides native multimodal capabilities whereas GPT-5 Nano is documented with text-only modality [1, 2, 4].

  Gpt-5-Mini: GPT-5 Mini supports function calling, prompt caching, structured outputs (response schema), and native multimodal tasks, alongside reasoning capabilities and an estimated 100B parameters [1, 3, 7].

  Gpt-5-Nano: GPT-5 Nano supports text modality, function calling, prompt caching, and structured outputs (response schemas), but lacks confirmed multimodal input support while operating at $0.05 per million input tokens [2, 4].

  Scores — Gpt-5-Mini: 9/10, Gpt-5-Nano: 7/10

  Ensures the selected model can support advanced operational features such as multimodal inputs, structured outputs, prompt caching, and tool integration.

  Sources: GPT-5 Mini - Specs, Benchmarks & Details, GPT 5 Nano by OpenAI — Pricing, Specs & API Access

What are the pros and cons of Gpt-5-Mini vs Gpt-5-Nano?

Where does this data come from?

1. GPT-5 Mini - Specs, Benchmarks & Details
2. GPT 5 Nano by OpenAI — Pricing, Specs & API Access
3. GPT 5 Mini by OpenAI — Pricing, Specs & API Access
4. GPT-5 Nano - Specs, Benchmarks & Details
5. GPT-5 Mini Model | OpenAI API
6. Everyone's Sleeping on GPT-5 Mini (And It's the Only ...
7. GPT-5 Mini: Compact Multimodal LLM
8. GPT-5 Mini - API Pricing & Benchmarks

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