GPT-5 mini vs GPT-5 nano
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5 mini by OpenAI and GPT-5 nano 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-5 nano holds a notable pricing advantage for token ingestion. On output generation, GPT-5 nano is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 400K tokens. In latency-sensitive production environments, GPT-5 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.
Changez et Comparez les Modèles
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GPT-5 mini
GPT-5 efficaceun smaller GPT-5 tuned pour responsive agents et quotidien automatisation.
$0.25 / 1M
$2.00 / 1M
400K tokens
52%
GPT-5 nano
Le GPT-5 le plus économiquele smallest GPT-5 lane, conçu pour routage, extraction et classification.
$0.05 / 1M
$0.40 / 1M
400K tokens
34.5%
Différences Clés en un Coup d’Œil
- GPT-5 nano is 5.0x cheaper for API usage (input + output tokens combined).
- GPT-5 nano is faster at 250 tok/s vs 185 tok/s.
- GPT-5 mini leads on SWE-bench coding (52.0% vs 34.5%).
| Caractéristique | GPT-5 mini | GPT-5 nano |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 46 | 35 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $0.25 / 1M | $0.05 / 1M |
Output Price Cost per million output tokens | $2.00 / 1M | $0.40 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.27 / 1M | $0.05 / 1M |
Context Window Maximum input + output tokens | 400K tokens~600 A4 pages of text | 400K tokens~600 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 185 tok/s | 250 tok/s |
SWE-bench Verified Real-world software engineering | 52% | 34.5% |
GPQA Diamond Graduate-level reasoning | 66% | 48% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.01 / 1M |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5 mini · Strengths & Key Advantages
- Cheap, rapide et still capable de raisonnement
- Retains le complet 400K fenêtre de contexte
- fiable appel d'outils pour agent flux de travail
- Handles multimodal entrée well pour son size
Considerations & Trade-offs
- Substantially weaker que complet GPT-5 sur hard tâches
- Struggles avec long, ambiguous multi-étapes plans
GPT-5 nano · Strengths & Key Advantages
- Extremely cheap à $0.05 / $0.40 par 1M tokens
- très faible latence pour élevé-débit pipelines
- Still supports raisonnement et outil calls
- Ideal as un premier-pass router dans multi-modèle pipelines
Considerations & Trade-offs
- Weak sur complexe raisonnement et au format long génération
- pas suitable as un primary assistant modèle
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5 mini | GPT-5 nano |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.32 | $0.06 |
Output Tokens / Task Tokens generated per task | 50K | 42K |
Reasoning Tokens / Task Thinking tokens burned per task | 24K | 18K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | GPT-5 mini | GPT-5 nano |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-mini | gpt-5-nano |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-08 | 2025-08 |
| Knowledge Cutoff | September 2024 | September 2024 |
| Architecture | Compact unified reasoning model | Nano unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.25/1M in) across full 400K window | Flat rate ($0.05/1M in) across full 400K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.025/1M (no extra cache-write fee) | Automatic prefix caching at $0.005/1M (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-5 mini vs GPT-5 nano: Réponses Rapides (FAQ)
Which is more intelligent, GPT-5 mini or GPT-5 nano?
GPT-5 mini is more intelligent. It scores 46 compared with 35 on the composite Intelligence Index.
Which model is faster?
GPT-5 nano is faster, generating 250 tokens per second compared with 185 tokens per second.
Which model is cheaper to run?
GPT-5 nano is cheaper at $0.05 per 1M tokens blended, compared with $0.27 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-5 nano has lower latency, with a time to first token of 0.38s compared with 0.70s.
Verdict
Verdict & Model Selection Recommendation: Choose GPT-5 mini if your priority is Cost-sensitive agents, assistants and coding help. Its performance in demanding tasks justifies its operational token cost. Choose GPT-5 nano if your workflow prioritizes Bulk classification, tagging and cheap agent routing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does GPT-5 mini compare to GPT-5 nano in API token pricing and cost per token?+
GPT-5 mini costs $0.25 per 1M input tokens and $2.00 per 1M output tokens, whereas GPT-5 nano costs $0.05 input and $0.40 output. Overall, GPT-5 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-5 mini or GPT-5 nano?+
Both GPT-5 mini and GPT-5 nano offer an identical context window of 400K tokens (~300k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: GPT-5 mini or GPT-5 nano?+
GPT-5 mini scores 52% on SWE-bench Verified while GPT-5 nano scores 34.5%. For autonomous software engineering and multi-file debugging, GPT-5 mini holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5 mini and GPT-5 nano?+
GPT-5 mini features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-5 nano provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5 mini and GPT-5 nano?+
GPT-5 mini generates approximately 185 tokens per second, while GPT-5 nano delivers 250 tokens per second. In latency-sensitive production environments, GPT-5 nano provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5 mini and GPT-5 nano to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5 nano to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5 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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs