GPT-4.1 vs GPT-4.1 mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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GPT-4.1 by OpenAI and GPT-4.1 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 mini holds a notable pricing advantage for token ingestion. On output generation, GPT-4.1 mini 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 1,048K tokens. In latency-sensitive production environments, GPT-4.1 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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GPT-4.1
Bourreau de travail à contexte longproduction bourreau de travail avec un 1M-token fenêtre de contexte et markedly improved instruction following.
$2.00 / 1M
$8.00 / 1M
1.05M tokens
54.6%
GPT-4.1 mini
Contexte long économiquele économique cut de GPT-4.1 — rapide codage et structuré extraction avec le same 1M-token window.
$0.40 / 1M
$1.60 / 1M
1.05M tokens
36.5%
Différences Clés en un Coup d’Œil
- GPT-4.1 mini is 5.0x cheaper for API usage (input + output tokens combined).
- GPT-4.1 mini is faster at 165 tok/s vs 110 tok/s.
- GPT-4.1 leads on SWE-bench coding (54.6% vs 36.5%).
| Caractéristique | GPT-4.1 | GPT-4.1 mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 36 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $2.00 / 1M | $0.40 / 1M |
Output Price Cost per million output tokens | $8.00 / 1M | $1.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.55 / 1M | $0.31 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,571 A4 pages of text | 1.05M tokens~1,571 A4 pages of text |
Max Output Tokens Per single request | 33K | 33K |
Throughput Speed Tokens generated per second | 110 tok/s | 165 tok/s |
SWE-bench Verified Real-world software engineering | 54.6% | 36.5% |
GPQA Diamond Graduate-level reasoning | 65% | 49.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.10 / 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-4.1 · Strengths & Key Advantages
- massif 1M-token fenêtre de contexte à un faible prix
- Excellent instruction following et codage précision
- solide structuré extraction pour agent pipelines
- fiable à long contexte recall up à roughly 300K tokens
Considerations & Trade-offs
- No raisonnement mode, so il trails o3 et GPT-5 sur multi-étapes problems
- contexte beyond ~300K tokens degrades dans précision
GPT-4.1 mini · Strengths & Key Advantages
- 1M-token contexte pour un fraction de GPT-4.1 tarification
- rapide enough pour interactif routage et extraction
- fiable structuré JSON sortie
- solide vision performances pour son prix classe
Considerations & Trade-offs
- Weak sur complexe multi-étapes raisonnement
- Noticeably moins accurate que complet GPT-4.1 sur long documents
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-4.1 | GPT-4.1 mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.72 | $0.14 |
Output Tokens / Task Tokens generated per task | 52K | 48K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | GPT-4.1 | GPT-4.1 mini |
|---|---|---|
| Official API Model ID (SDK) | gpt-4-1 | gpt-4-1-mini |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-04 | 2025-04 |
| Knowledge Cutoff | June 2024 | June 2024 |
| Architecture | Dense multimodal transformer | Dense multimodal transformer (small) |
| 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) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 1048K window | Flat rate ($0.4/1M in) across full 1048K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.5/1M (no extra cache-write fee) | Automatic prefix caching at $0.1/1M (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 vs GPT-4.1 mini: Réponses Rapides (FAQ)
Which is more intelligent, GPT-4.1 or GPT-4.1 mini?
GPT-4.1 is more intelligent. It scores 48 compared with 36 on the composite Intelligence Index.
Which model is faster?
GPT-4.1 mini is faster, generating 165 tokens per second compared with 110 tokens per second.
Which model is cheaper to run?
GPT-4.1 mini is cheaper at $0.31 per 1M tokens blended, compared with $1.55 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4.1 mini has lower latency, with a time to first token of 0.30s compared with 0.48s.
Verdict
Verdict & Model Selection Recommendation: Choose GPT-4.1 if your priority is Long-document analysis, large-codebase review and structured extraction. Its performance in demanding tasks justifies its operational token cost. Choose GPT-4.1 mini if your workflow prioritizes High-volume extraction, classification and 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-4.1 compare to GPT-4.1 mini in API token pricing and cost per token?+
GPT-4.1 costs $2.00 per 1M input tokens and $8.00 per 1M output tokens, whereas GPT-4.1 mini costs $0.40 input and $1.60 output. Overall, GPT-4.1 mini 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 or GPT-4.1 mini?+
Both GPT-4.1 and GPT-4.1 mini offer an identical context window of 1,048K tokens (~786k 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-4.1 or GPT-4.1 mini?+
GPT-4.1 scores 54.6% on SWE-bench Verified while GPT-4.1 mini scores 36.5%. For autonomous software engineering and multi-file debugging, GPT-4.1 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-4.1 and GPT-4.1 mini?+
GPT-4.1 is optimized for low-latency direct generation. In contrast, GPT-4.1 mini focuses on standard fast inference.
How does inference speed (tokens per second) compare between GPT-4.1 and GPT-4.1 mini?+
GPT-4.1 generates approximately 110 tokens per second, while GPT-4.1 mini delivers 165 tokens per second. In latency-sensitive production environments, GPT-4.1 mini provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-4.1 and GPT-4.1 mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-4.1 mini to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-4.1. 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