o4-mini vs o1-mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
o4-mini by OpenAI and o1-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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, o4-mini provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, o4-mini 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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o4-mini
Raisonneur compact rapideun rapide o-series modèle combining compact raisonnement avec codage et utilisation d'outils.
$1.10 / 1M
$4.40 / 1M
200K tokens
57.5%
o1-mini
Raisonneur compactun cheap, rapide modèle de raisonnement focused sur codage, mathematics et STEM problem solving.
$1.10 / 1M
$4.40 / 1M
128K tokens
44%
Différences Clés en un Coup d’Œil
- o4-mini is 1.1x cheaper for API usage (input + output tokens combined).
- o4-mini has a larger context window (200K vs 128K tokens).
- o4-mini is faster at 105 tok/s vs 90 tok/s.
- o4-mini leads on SWE-bench coding (57.5% vs 44.0%).
| Caractéristique | o4-mini | o1-mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 50 | 44 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $1.10 / 1M | $1.10 / 1M |
Output Price Cost per million output tokens | $4.40 / 1M | $4.40 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.85 / 1M | $1.05 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 100K | 66K |
Throughput Speed Tokens generated per second | 105 tok/s | 90 tok/s |
SWE-bench Verified Real-world software engineering | 57.5% | 44% |
GPQA Diamond Graduate-level reasoning | 73% | 68.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.28 / 1M | $0.55 / 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.
o4-mini · Strengths & Key Advantages
- 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
Considerations & Trade-offs
- Clearly weaker que o3 sur difficult multi-étapes problems
- pensée tokens add latence sur hard prompts
o1-mini · Strengths & Key Advantages
- raisonnement capacité à une fraction d'o1 tarification
- rapide sur compétitif codage et maths problems
- long 65K sortie économique pour étendu dérivations
- Cheapest way à get genuine chaîne de pensée raisonnement
Considerations & Trade-offs
- No vision support — texte seulement
- Connaissances générales sur le monde limitées
- Clearly weaker que o1 et o3 sur hard multi-étapes tâches
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o4-mini | o1-mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.72 | $0.85 |
Output Tokens / Task Tokens generated per task | 45K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 25K | 26K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | o4-mini | o1-mini |
|---|---|---|
| Official API Model ID (SDK) | o4-mini | o1-mini |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-04 | 2024-09 |
| Knowledge Cutoff | June 2024 | October 2023 |
| Architecture | Compact reasoning transformer | Reasoning transformer with hidden chain-of-thought |
| 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 ($1.1/1M in) across full 200K window | Flat rate ($1.1/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.275/1M (no extra cache-write fee) | Automatic prefix caching at $0.55/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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) |
| 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 |
| Output Modalities | text | text |
o4-mini vs o1-mini: Réponses Rapides (FAQ)
Which is more intelligent, o4-mini or o1-mini?
o4-mini is more intelligent. It scores 50 compared with 44 on the composite Intelligence Index.
Which model is faster?
o4-mini is faster, generating 105 tokens per second compared with 90 tokens per second.
Which model is cheaper to run?
o4-mini is cheaper at $0.85 per 1M tokens blended, compared with $1.05 (7:2:1 cache:input:output ratio).
Which model has lower latency?
o4-mini has lower latency, with a time to first token of 1.80s compared with 2.20s.
Which model has a larger context window?
o4-mini has a larger context window, supporting 200K tokens compared with 128K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose o4-mini if your priority is Budget reasoning inside agent pipelines and coding assistants. Its performance in demanding tasks justifies its operational token cost. Choose o1-mini if your workflow prioritizes Budget STEM and coding reasoning where cost matters. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does o4-mini compare to o1-mini in API token pricing and cost per token?+
Both o4-mini and o1-mini share matched API token pricing at $1.10 per 1M input tokens and $4.40 per 1M output tokens. Because their token economics are identical, your selection should be guided by benchmark latency, memory horizon, and reasoning capabilities rather than operational token fees. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: o4-mini or o1-mini?+
o4-mini supports a context window of 200K tokens (~150k words), while o1-mini supports 128K tokens (~96k words). Therefore, o4-mini can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: o4-mini or o1-mini?+
o4-mini scores 57.5% on SWE-bench Verified while o1-mini scores 44%. For autonomous software engineering and multi-file debugging, o4-mini holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between o4-mini and o1-mini?+
o4-mini features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, o1-mini provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between o4-mini and o1-mini?+
o4-mini generates approximately 105 tokens per second, while o1-mini delivers 90 tokens per second. In latency-sensitive production environments, o4-mini provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining o4-mini and o1-mini to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to o4-mini, and route deep architectural or complex reasoning queries to o4-mini. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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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