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Favicon for openai

OpenAI: GPT-4.1 Mini

openai/gpt-4.1-mini

Compare

GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. It retains a 1 million token context window and scores 45.1% on hard instruction evals, 35.8% on MultiChallenge, and 84.1% on IFEval. Mini also shows strong coding ability (e.g., 31.6% on Aider’s polyglot diff benchmark) and vision understanding, making it suitable for interactive applications with tight performance constraints.

Modalities
In / Out Price
$0.40 / $1.60per 1M
Context
1.0M
Released
Apr 14, 2025
Knowledge Cutoff
Jun 2024
Compare
ProvidersPricingPerformanceUptimeBenchmarksAppsActivityFAQExplore

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), Floor (cheapest), or Exacto (highest tool-calling accuracy).

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Performance

Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Benchmarks

Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.

Benchmark score summary for OpenAI: GPT-4.1 Mini (Artificial Analysis and Design Arena)
SourceBenchmarkScore
Artificial AnalysisGPT-4.1 mini Coding Index20.2
Artificial AnalysisGPT-4.1 mini GPQA Diamond66.4%
Artificial AnalysisGPT-4.1 mini HLE5.0%
Artificial AnalysisGPT-4.1 mini IFBench38.3%
Artificial AnalysisGPT-4.1 mini τ²-Bench Telecom52.9%
Artificial AnalysisGPT-4.1 mini AA-LCR44.0%
Artificial AnalysisGPT-4.1 mini GDPval-AA0.0%
Artificial AnalysisGPT-4.1 mini CritPt0.0%
Artificial AnalysisGPT-4.1 mini Terminal-Bench Hard7.6%
Artificial AnalysisGPT-4.1 mini AA-Omniscience Accuracy20.3%
Artificial AnalysisGPT-4.1 mini AA-Omniscience Non-Hallucination Rate7.3%
Design ArenaGPT-4.1 mini Models Arena 3D Elo855
Design ArenaGPT-4.1 mini Models Arena Code Categories Elo1002
Design ArenaGPT-4.1 mini Models Arena Data Visualization Elo1042
Design ArenaGPT-4.1 mini Models Arena Game Development Elo1084
Design ArenaGPT-4.1 mini Models Arena UI Component Elo971
Design ArenaGPT-4.1 mini Models Arena Website Elo1005

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Activity

Token volume and request traffic to this model over time.

API

Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.

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$0.40$1.60$0.100.93s45 tps
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Not used in Standard routing:Why these endpoints are not used
$0.44$1.76$0.111.70s37 tps
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Throughput
45tok/s

P50, best across providers

Latency
0.93s

P50, best provider

AutoExacto benchmarks
GPQA DiamondTAU-Benchauto-routing64.0%45.3%OpenAI63.8%44.0%Azure63.8%43.3%Azure (EU)64.5%40.0%
Uptime (3d)The model was reachable. Request routed to a provider.
100.00%
Availability (3d)The model returned inference from any provider. Errors and empty responses count against it.
99.98%

Availability over the last 3 days

Last 72 hours
Availability 99.98%
3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability
99.98%
Without Routing
99.51%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and customization options.

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Frequently asked questions

GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. It retains a 1 million token context window and scores 45.1% on hard instruction evals, 35.8% on MultiChallenge, and 84.1% on IFEval.

GPT-4.1 Mini costs $0.40/M input tokens and $1.60/M output tokens, with separate rates for Cache Read at $0.10/M tokens.

GPT-4.1 Mini has a 1,047,576 token context window.

Yes. GPT-4.1 Mini accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.

GPT-4.1 Mini accepts images, text, and files such as PDFs as input and returns text.

GPT-4.1 Mini is served by 2 providers on OpenRouter: Azure and OpenAI. Requests are routed to the best available provider, with automatic failover to the others, and you can pin or exclude providers with provider routing.

GPT-4.1 Mini was released on April 14, 2025. Its knowledge cutoff is June 30, 2024.

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Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6.1 Sol and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6.1 Sol instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

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GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6.1 Sol and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6.1 Sol instead.

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