GPT-5-mini vs GPT-4.1 Comparison | Generated by AI
Overview
GPT-5-mini and GPT-4.1 are models in the OpenAI lineup, with GPT-5-mini being a scaled-down variant of the advanced reasoning-oriented GPT-5, and GPT-4.1 a faster, non-reasoning model optimized for responsiveness. GPT-5-mini excels in complex tasks requiring deep analysis but at the cost of higher latency and potential cost, while GPT-4.1 prioritizes speed and efficiency for simpler interactions. Below is a detailed comparison based on available benchmarks, pricing, and capabilities. All comparisons are drawn from sources discussing these models. [1][2][3][4][5]
Intelligence and Performance
- Reasoning Depth: GPT-5-mini employs advanced reasoning modes (e.g., high mode for complex tasks), enabling multi-hop logic, step-by-step analysis, and autonomous task execution. It outperforms GPT-4.1 in benchmarks like SWE-bench Verified (74.9% success rate vs. 54.6%) and Aider’s polyglot code edits (88% passthrough vs. ~52%). In agentic tasks, GPT-5-mini stays on track without losing context, unlike GPT-4.1, which may require more user prompts. GPT-5’s reasoning stability makes it proactive in planning and execution. [3][4][6]
- Coding and Math: GPT-5-mini handles real-world codebases, debugging, and multilingual edits better. It scores higher in mathematical reasoning (e.g., surpassing GPT-4.1 in AIME benchmarks). GPT-4.1 was strong for basic coding but lacks GPT-5-mini’s depth in independent solution generation. GPT-5-mini generates working code patches more reliably. [3][4]
- Other Abilities (e.g., Hallucination, Language Tasks): GPT-5-mini reduces confusion mid-task and halts less often compared to GPT-4.1. However, both are proficient in general language tasks; GPT-5-mini’s strengths shine in analytical, enterprise-level applications. Hallucination rates are lower in GPT-5-mini for complex prompts. [3][4]
Price and Cost Efficiency
- Input Tokens: GPT-5-mini is cheaper at $0.25 per 1M tokens, vs. GPT-4.1’s $2 per 1M (making GPT-5-mini roughly 8x cheaper for input). GPT-4.1 mini is ~1.6x more expensive than GPT-5-mini. For cost-effective writing, GPT-5-mini offers better value despite higher token usage in reasoning. [5][7][8]
- Output Tokens: GPT-5-mini costs $2 per 1M, while GPT-4.1 is $8 per 1M (GPT-5-mini ~4x cheaper). GPT-4.1 mini is ~0.8x cheaper than GPT-5-mini for output, but overall, GPT-5-mini is more economical for balanced use. Token consumption can be 100x higher in GPT-5-mini due to reasoning, offsetting some savings. [3][5][7][8]
- Total Cost Trade-offs: For high-volume, simple tasks, GPT-4.1’s speed yields lower per-query costs; GPT-5-mini suits environments where accuracy trumps volume, with Azure pricing linked to usage. Variants like -nano exist for further cost optimization. [3][5]
Speed and Latency
- Response Time: GPT-4.1 offers lower latency (~720ms first-token time) for snappy, responsive interactions. GPT-5-mini has higher latency (~1000ms) due to reasoning depth, making it less ideal for real-time apps like voice agents. In minimal reasoning mode, GPT-5-mini still lags slightly. [3][4]
- Throughput and Optimization: GPT-4.1 excels in high-throughput workloads (e.g., chatbots), delivering fast, concise responses. GPT-5-mini may introduce lags during complex tasks but provides deeper, longer outputs. GPT-4.1 is optimized for speed; GPT-5-mini prioritizes accuracy over immediacy. [1][3]
Context Window and Capabilities
- Context Window: GPT-5-mini supports up to 400K input tokens (272K in, 128K out); GPT-4.1 handles 128K short context or up to 1M in long-context mode. GPT-4.1 allows longer total context for sprawling conversations. [3][6]
- Output Length and Perspective: GPT-5-mini enables structured, analytical outputs; GPT-4.1 focuses on concise, conversational replies. Variants include turbo modes for custom needs. [3][1]
Use Cases and Best Fits
- Best for GPT-5-Mini: Complex reasoning, code generation/review, agentic tool-calling, business research, multi-step tasks. Ideal for developers needing advanced coding or math solutions. Suited for enterprise applications where depth outweighs speed. [3][4]
- Best for GPT-4.1: Real-time chat, customer support, lightweight summarization, short queries, high-volume deployments. Better for low-latency needs like live interactions. GPT-4.1 variants (e.g., mini) cater to cost-conscious, simple workloads. [3][4][5]
- Trade-offs Example: For cost-effective writing, GPT-5-mini is recommended as “smarter and cheaper,” but GPT-4.1 wins in instant feedback scenarios. Azure offers variants (GPT-5-nano, GPT-4.1-mini) for tailored deployments. [3][7]
Summary Table
| Feature | GPT-5-Mini | GPT-4.1 |
|---|---|---|
| Model Type | Reasoning | Non-reasoning, fast response |
| Intelligence | High (e.g., 74.9% SWE-bench) | Moderate (e.g., 54.6% SWE-bench) |
| Latency | Higher (~1000ms) | Lower (~720ms) |
| Context Window | 400K total (272K in, 128K out) | 128K to 1M (long-context) |
| Pricing (Input) | $0.25/1M tokens | $2/1M tokens |
| Pricing (Output) | $2/1M tokens | $8/1M tokens |
| Best For | Complex, analytical tasks | Quick, conversational tasks |
This comparison is based on current data up to September 2025; model details may evolve. For specific implementations, refer to OpenAI API or Azure AI Foundry docs. [2][3]
Citations: [1] https://artificialanalysis.ai/models/comparisons/gpt-5-mini-vs-gpt-4-1 [2] https://platform.openai.com/docs/models/compare?model=gpt-4.1-mini [3] https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/how-to/model-choice-guide [4] https://medium.com/@leucopsis/how-gpt-5-compares-to-gpt-4-1-21fad92c2a3a [5] https://blog.galaxy.ai/compare/gpt-4-1-mini-vs-gpt-5-mini [6] https://llm-stats.com/models/compare/gpt-4.1-2025-04-14-vs-gpt-5-mini-2025-08-07 [7] https://www.reddit.com/r/ChatGPTPro/comments/1mq5qu0/gpt_41_mini_vs_5_mini_for_cost_effective_writing/ [8] https://community.openai.com/t/gpt-5-100x-token-usage-compared-to-gpt-4-1/1345419