GPT-4.1 nano vs GPT-4o mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-4.1 nano by OpenAI and GPT-4o mini by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, GPT-4.1 nano holds a notable pricing advantage for token ingestion. On output generation, GPT-4.1 nano is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-4.1 nano provides an expanded memory horizon with 1,048K tokens. In latency-sensitive production environments, GPT-4.1 nano leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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GPT-4.1 nano
高性能 AI モデルOpenAI が提供する GPT-4.1 nano は、最大 1.0M トークンのコンテキストウィンドウを備え、入力 $0.1/100万トークン・出力 $0.4/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.10 / 1M
$0.40 / 1M
1.05M tokens
24%
GPT-4o mini
高性能 AI モデルOpenAI が提供する GPT-4o mini は、最大 128K トークンのコンテキストウィンドウを備え、入力 $0.15/100万トークン・出力 $0.6/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.15 / 1M
$0.60 / 1M
128K tokens
24.5%
ひと目でわかる主な違い
- GPT-4.1 nano is 1.6x cheaper for API usage (input + output tokens combined).
- GPT-4.1 nano has a larger context window (1M vs 128K tokens).
- GPT-4.1 nano is faster at 220 tok/s vs 140 tok/s.
- GPT-4o mini leads on SWE-bench coding (24.5% vs 24.0%).
| Feature | GPT-4.1 nano | GPT-4o mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 26 | 30 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $0.10 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $0.40 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.08 / 1M | $0.14 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,571 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 33K | 16K |
Throughput Speed Tokens generated per second | 220 tok/s | 140 tok/s |
SWE-bench Verified Real-world software engineering | 24% | 24.5% |
GPQA Diamond Graduate-level reasoning | 38% | 40.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.07 / 1M |
分野別勝者スコアカード
At-a-glance decision framework検証済みベンチマークスコア比較
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-4.1 nano · Strengths & Key Advantages
- OpenAI の高度な Dense multimodal transformer (nano) アーキテクチャと 1.0M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.1/1M、出力 $0.4/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
GPT-4o mini · Strengths & Key Advantages
- OpenAI の高度な Small Multimodal Dense アーキテクチャと 128K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.15/1M、出力 $0.6/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-4.1 nano | GPT-4o mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.04 | $0.16 |
Output Tokens / Task Tokens generated per task | 42K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-4.1 nano | GPT-4o mini |
|---|---|---|
| Official API Model ID (SDK) | gpt-4-1-nano | gpt-4o-mini |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-04 | 2024-07 |
| Knowledge Cutoff | June 2024 | October 2023 |
| Architecture | Dense multimodal transformer (nano) | Small Multimodal Dense |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.1/1M in) across full 1048K window | Flat rate ($0.15/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.025/1M (no extra cache-write fee) | Automatic prefix caching at $0.075/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
GPT-4.1 nano vs GPT-4o mini: よくある質問 (FAQ)
Which is more intelligent, GPT-4.1 nano or GPT-4o mini?
GPT-4o mini is more intelligent. It scores 30 compared with 26 on the composite Intelligence Index.
Which model is faster?
GPT-4.1 nano is faster, generating 220 tokens per second compared with 140 tokens per second.
Which model is cheaper to run?
GPT-4.1 nano is cheaper at $0.08 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4.1 nano has lower latency, with a time to first token of 0.22s compared with 0.31s.
Which model has a larger context window?
GPT-4.1 nano has a larger context window, supporting 1.05M tokens compared with 128K tokens.
総合評価
Verdict & Model Selection Recommendation: Choose GPT-4.1 nano if your priority is Bulk classification, tagging, guardrails and cheap agent routing. Its performance in demanding tasks justifies its operational token cost. Choose GPT-4o mini if your workflow prioritizes Data extraction, customer support routing, and lightweight web scraping. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does GPT-4.1 nano compare to GPT-4o mini in API token pricing and cost per token?+
GPT-4.1 nano costs $0.10 per 1M input tokens and $0.40 per 1M output tokens, whereas GPT-4o mini costs $0.15 input and $0.60 output. Overall, GPT-4.1 nano is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: GPT-4.1 nano or GPT-4o mini?+
GPT-4.1 nano supports a context window of 1,048K tokens (~786k words), while GPT-4o mini supports 128K tokens (~96k words). Therefore, GPT-4.1 nano can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-4.1 nano or GPT-4o mini?+
GPT-4.1 nano scores 24% on SWE-bench Verified while GPT-4o mini scores 24.5%. For autonomous software engineering and multi-file debugging, GPT-4o mini holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-4.1 nano and GPT-4o mini?+
GPT-4.1 nano is optimized for low-latency direct generation. In contrast, GPT-4o mini focuses on standard fast inference.
How does inference speed (tokens per second) compare between GPT-4.1 nano and GPT-4o mini?+
GPT-4.1 nano generates approximately 220 tokens per second, while GPT-4o mini delivers 140 tokens per second. In latency-sensitive production environments, GPT-4.1 nano provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-4.1 nano and GPT-4o mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-4.1 nano to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-4o mini. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs