un rapide o-series modèle combining compact raisonnement avec codage et utilisation d'outils.
Technical Specifications
- Provider
- OpenAI
- Category
- reasoning
- Release Date
- 2025-04
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- $20 / month
- Reasoning Effort Control
- Configurable (Low / Med / High)
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- o4-mini
- Supported SDK & API Protocol
- OpenAI Responses API & Chat Completions v1
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2027+)
- Production Rate Limits (RPM & TPM)
- 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($1.1/1M in) across full 200K window
- Prompt Cache Write & TTL Policy
- Automatic prefix caching at $0.275/1M (no extra cache-write fee)
- Computer Use & Browser GUI Agent
- 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
- Web Search Grounding & Code Sandbox
- Native Web Search tool + Hosted Code Interpreter & File Search RAG
- Fine-Tuning & Distillation Support
- Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
- Weights Precision & Quantization
- Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster)
- Minimum GPU VRAM Requirement
- Zero Local VRAM Required (100% Serverless Cloud API)
- Supported Inference Engines
- Managed OpenAI Cloud API
- System Card & Safety Framework
- OpenAI Preparedness Framework Audited & System Card Published
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
o4-mini Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~93 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to o4-mini:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 57.5%
- GPQA Diamond
- 73%
- MMLU-Pro
- 83%
- HumanEval
- 91.5%
- Humanity's Last Exam
- 11.2%
- Terminal-Bench
- 36%
- SciCode
- 38.5%
- LiveCodeBench
- 65%
- AIME
- 89.5%
- Long-Context Reasoning
- 74.5%
- Chatbot Arena Elo
- 1325pts
Strengths
- Cheap raisonnement avec vision et appel d'outils
- rapide enough pour interactif agent boucles
- solide maths performances pour son prix band
- Excellent maths précision par dollar among o-series modèles
Limitations & Trade-offs
- Clearly weaker que o3 sur difficult multi-étapes problems
- pensée tokens add latence sur hard prompts
Editorial Assessment
Excellent valeur pour raisonnement-heavy tâches qui cannot justify o3 tarification.
Best for: économique raisonnement inside agent pipelines et codage assistants
Compare o4-mini
Frequently Asked Questions about o4-mini
How much does the o4-mini API cost per 1M tokens?+
o4-mini by OpenAI is priced at $1.1 per 1M input tokens and $4.4 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.275 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the o4-mini pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in o4-mini's context window?+
o4-mini features a 200K tokens context window, which equals approximately 150,000 English words or ~400 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 100,000 tokens.
Can I run o4-mini locally on my own GPU (Open Weights)?+
No, o4-mini is a proprietary closed-weights model hosted by OpenAI. It cannot be downloaded for local GPU execution and must be accessed via OpenAI's official API or cloud platform.
Does OpenAI train on data sent to the o4-mini API, and is commercial use allowed?+
API payloads are excluded from model training by default. License tier: Proprietary Commercial API (OpenAI Terms). Full commercial ownership of generated outputs. Supported deployment channels include: OpenAI Platform API, Microsoft Azure OpenAI Foundry.
How does o4-mini perform on coding and reasoning benchmarks?+
o4-mini achieves a composite Intelligence Index of 50/100, scoring 57.5% on SWE-bench Verified, 73% on GPQA Diamond, 83% on MMLU-Pro, and 1325 pts on Chatbot Arena Elo.
What is o4-mini best used for?+
o4-mini is best suited for: économique raisonnement inside agent pipelines et codage assistants. Assessment: Excellent valeur pour raisonnement-heavy tâches qui cannot justify o3 tarification.
OpenAI Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to OpenAI's flagship reasoning tier:
GPT-6 Astra
GPT-6.1 Sol
GPT-6 Sol
GPT-6 Luna
GPT-5.6 Sol
GPT-5.6 Terra
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to o4-mini:
L'unité textuelle de base traitée par un modèle linguistique, représentant généralement 3 à 4 caractères ou environ 0,75 mot. Par exemple, une phrase courante est découpée en plusieurs jetons.
Une suite de tests standardisés permettant d'évaluer et de comparer objectivement les performances des modèles d'IA sur des tâches ciblées (code avec SWE-bench, mathématiques avec AIME, etc.).
Le nombre maximal de jetons qu'un modèle peut traiter simultanément (entrée et sortie comprises). Une fenêtre de 1 000 000 jetons permet d'analyser environ 750 000 mots en une seule requête.
Un modèle d'IA entraîné par apprentissage par renforcement pour allouer du temps de calcul à la réflexion logique avant d'émettre une solution, idéal pour les problèmes scientifiques et le code complexe.
Une méthode de raisonnement pas-à-pas où le modèle décompose le problème en étapes logiques intermédiaires avant de conclure, augmentant drastiquement la précision en mathématiques et programmation.
La capacité d'un LLM à déterminer quand une action externe est requise et à générer des arguments structurés pour invoquer des API, calculatrices ou bases de données avant de poursuivre.
Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: OpenAI official pricing & model documentation (verified October 2026)↗