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.
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GPT-4.1
Long-Context WorkhorseProduction workhorse with a 1M-token context window and markedly improved instruction following.
$2.00 / 1M
$8.00 / 1M
1.05M tokens
54.6%
GPT-4.1 mini
Budget Long-ContextThe budget cut of GPT-4.1 — fast coding and structured extraction with the same 1M-token window.
$0.40 / 1M
$1.60 / 1M
1.05M tokens
36.5%
Key Differences at a Glance
- 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%).
| Feature | 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 |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-4.1 · Strengths & Key Advantages
- Massive 1M-token context window at a low price
- Excellent instruction following and coding accuracy
- Strong structured extraction for agent pipelines
- Reliable long-context recall up to roughly 300K tokens
Considerations & Trade-offs
- No reasoning mode, so it trails o3 and GPT-5 on multi-step problems
- Context beyond ~300K tokens degrades in accuracy
GPT-4.1 mini · Strengths & Key Advantages
- 1M-token context for a fraction of GPT-4.1 pricing
- Fast enough for interactive routing and extraction
- Reliable structured JSON output
- Strong vision performance for its price class
Considerations & Trade-offs
- Weak on complex multi-step reasoning
- Noticeably less accurate than full GPT-4.1 on 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 |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | 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: Quick Answers
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.
Frequently asked questions
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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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.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs