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GPT-4.1 mini benchmarks, pricing and specs

GPT-4.1 mini is an OpenAI model released on April 14, 2025 that takes text and image input and produces text. It costs $0.40 per 1M input tokens and $1.60 per 1M output tokens and accepts up to 1,047,576 tokens of context. It scores 1383 Elo on Arena Text, #38 of 39 current models (Arena, measured October 8, 2026).

Per 1M input tokens
$0.40
Per 1M output tokens
$1.60
Context window
1.05M
Arena Text
1383 Elo · #38/39

Data verified October 12, 2026 · Dataset 2026-10-12

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Key facts

Provider
OpenAI
Model family
GPT-4.1
Release date
April 14, 2025
Status
Current
Access
API only
API model id
Not reported
Context window
1,047,576 tokens
Max output
32,768 tokens
Input
text, image
Output
text
Task families
text, multimodal
Declared capabilities
reasoning

GPT-4.1 mini pricing

It is the #8 cheapest of 37 current text models priced per 1M input tokens.

UnitPrice (USD)TierAppliesSource
1M input tokens$0.40standardCurrent
1M output tokens$1.60standardCurrent
1M input tokens$0.10cachedCurrent

Independent benchmark results

Scores come from independent evaluators that ran the model themselves, pinned to a dated snapshot. Rank is among current models in the same snapshot. OpenAI's own launch figures are listed under strengths with the publisher named, because they are not measured the same way.

BenchmarkScoreRankMeasuredSource
Arena Text1383 Elo#38 of 39October 8, 2026Arena · 2026-10-12
GPQA Diamond65.8%#36 of 37October 12, 2026Epoch AI · 2026-10-12
FrontierMath Tiers 1-3 (v2)6.7%#36 of 37October 12, 2026Epoch AI · 2026-10-12
SimpleQA Verified12.7%#33 of 34October 12, 2026Epoch AI · 2026-10-12

Strengths, cautions and best uses

Strengths

  • GPT-4.1 mini records API access for text and image inputs and a published 1,047,576-token context window

Cautions

  • Not in the pinned LiveBench snapshot; its independent scores here come from Epoch AI's own GPQA Diamond, FrontierMath and SimpleQA Verified runs

Best for

  • Cost-sensitive long-context API evaluation

Alternatives to GPT-4.1 mini

The closest current textmodels by price. The cheapest model that scores as well or better is listed first; failing that, the cheapest one within 3% of the benchmark's scale.

Compare GPT-4.1 mini with

Other GPT-4.1 models

GPT-4.1 mini FAQ

How much does GPT-4.1 mini cost?

GPT-4.1 mini costs $0.40 per 1M input tokens and $1.60 per 1M output tokens at the standard tier. It is the #8 cheapest of 37 current text models priced per 1M input tokens. Source: OpenAI, verified August 6, 2026.

What is the context window of GPT-4.1 mini?

GPT-4.1 mini accepts up to 1,047,576 tokens of context. It can return up to 32,768 tokens in one response. That ranks #21 of 44 current text models by context length.

How does GPT-4.1 mini score on independent benchmarks?

GPT-4.1 mini scores 1383 Elo on Arena Text, #38 of 39 current models (Arena, measured October 8, 2026), 65.8% on GPQA Diamond, #36 of 37 current models (Epoch AI, measured October 12, 2026) and 6.7% on FrontierMath Tiers 1-3 (v2), #36 of 37 current models (Epoch AI, measured October 12, 2026). These are independent evaluations, not OpenAI's own launch figures.

Is GPT-4.1 mini open source or API only?

GPT-4.1 mini is available through OpenAI's API only; its weights are not published.

What are the best alternatives to GPT-4.1 mini?

The closest current alternatives by price and measured performance are GPT-6 Luna (75% cheaper per 1M input tokens, $0.10 vs $0.40), Gemini 3 Flash Preview (25% more per 1M input tokens, $0.50 vs $0.40), DeepSeek-V4.1-Flash (25% cheaper per 1M input tokens, $0.30 vs $0.40) and Gemini 3.5 Flash-Lite (25% cheaper per 1M input tokens, $0.30 vs $0.40).

Sources

Cite this page

Van Data Team. "GPT-4.1 mini Benchmarks, Pricing and Specs." AI Model Benchmark Dataset 2026-10-12. https://vandatateam.com/ai-model-benchmarks/gpt-4-1-mini

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Production evaluation

Deciding whether GPT-4.1 mini fits your workload?

Public benchmarks narrow the shortlist. We test the finalists on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.

  • Evaluation cases and acceptance thresholds tied to real work
  • Model routing, fallbacks and cost controls