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GPT-4.1 Nano vs Ministral 3 14B 2512

GPT-4.1 Nano leads Ministral 3 14B 2512 on the Artificial Analysis Intelligence Index, 7.8 to 6.0, and Ministral 3 14B 2512 costs 2.0x less per output token ($0.20 vs $0.40 per million). Updated October 22, 2026; 6 benchmarks have a result for both.

Replace GPT-4.1 Nano Replace Ministral 3 14B 2512
GPT-4.1 NanoOpenAI
Coding
No data
Overall
24.7
Output $/M
$0.40
1
Ministral 3 14B 2512Mistral AI
Coding
No data
Overall
No data
Output $/M
$0.20
5
vs boards won of 6 shared

GPT-4.1 Nano or Ministral 3 14B 2512: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick GPT-4.1 Nano for a larger context window (1.05M against 262K tokens) and work measured by AA Coding Index or Design Arena: websites, where it leads.

Model AGPT-4.1 NanoOpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BMinistral 3 14B 2512Mistral AINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
6
GPT-4.1 Nano only
65
Ministral 3 14B 2512 only
1
Like-for-like categories
3 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GPT-4.1 Nano not benchmarked, Ministral 3 14B 2512 not benchmarked.
Widest category
Web design, 10.7 points apart on average over 4 shared benchmarks.
Widest single gap
Design Arena: websites: GPT-4.1 Nano 973 against Ministral 3 14B 2512 1081, 13.5 points apart on a 0 to 100 scale.
Output price
GPT-4.1 Nano $0.40 against Ministral 3 14B 2512 $0.20 per million output tokens, 2.0x apart.
Context window
GPT-4.1 Nano takes 1.05M tokens, Ministral 3 14B 2512 262K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGPT-4.1 NanoBarsMinistral 3 14B 2512Ties
Coding01
Intelligence10
Web design04
All shared boards15
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
Coding benchmarks for GPT-4.1 Nano and Ministral 3 14B 2512
BenchmarkGPT-4.1 NanoMinistral 3 14B 2512Leader
Aider polyglot8.9%Not measuredOnly GPT-4.1 Nano measured
AA Coding Index11.114.4Ministral 3 14B 2512
LiveCodeBench (Vals AI)42.7%Not measuredOnly GPT-4.1 Nano measured
LiveCodeBench (Artificial Analysis run)32.6%Not measuredOnly GPT-4.1 Nano measured
Terminal-Bench Hard (Artificial Analysis run)3.8%Not measuredOnly GPT-4.1 Nano measured
WeirdML19.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: coding1306Not measuredOnly GPT-4.1 Nano measured
LMArena Text: software and IT1305Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 35 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GPT-4.1 Nano and Ministral 3 14B 2512
BenchmarkGPT-4.1 NanoMinistral 3 14B 2512Leader
AA Intelligence Index7.86.0GPT-4.1 Nano
Epoch Capabilities Index129.5Not measuredOnly GPT-4.1 Nano measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Agentic work benchmarks for GPT-4.1 Nano and Ministral 3 14B 2512
BenchmarkGPT-4.1 NanoMinistral 3 14B 2512Leader
AA Agentic IndexNot measured1.1Only Ministral 3 14B 2512 measured
τ²-Bench Telecom17.3%Not measuredOnly GPT-4.1 Nano measured
Berkeley Function Calling Leaderboard (BFCL)33.1%Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 39 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
Reasoning benchmarks for GPT-4.1 Nano and Ministral 3 14B 2512
BenchmarkGPT-4.1 NanoMinistral 3 14B 2512Leader
GPQA Diamond50.8%Not measuredOnly GPT-4.1 Nano measured
MMMU Pro (Vals AI)55.0%Not measuredOnly GPT-4.1 Nano measured
GPQA Diamond (Artificial Analysis run)51.2%Not measuredOnly GPT-4.1 Nano measured
Humanity's Last Exam (Artificial Analysis run)3.8%Not measuredOnly GPT-4.1 Nano measured
GPQA Diamond (Epoch AI run)48.9%Not measuredOnly GPT-4.1 Nano measured
ARC-AGI0.0%Not measuredOnly GPT-4.1 Nano measured
ARC-AGI-20.0%Not measuredOnly GPT-4.1 Nano measured
Chess puzzles0.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: hard prompts1286Not measuredOnly GPT-4.1 Nano measured
LMArena Text: expert1271Not measuredOnly GPT-4.1 Nano measured
LMArena Text: hard prompts in English1293Not measuredOnly GPT-4.1 Nano measured
Kagi LLM Benchmark33.3%Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 11 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Math benchmarks for GPT-4.1 Nano and Ministral 3 14B 2512
BenchmarkGPT-4.1 NanoMinistral 3 14B 2512Leader
AIME26.5%Not measuredOnly GPT-4.1 Nano measured
MATH 500 (Vals AI)80.2%Not measuredOnly GPT-4.1 Nano measured
MGSM (Vals AI)69.3%Not measuredOnly GPT-4.1 Nano measured
AA Math Index24.0Not measuredOnly GPT-4.1 Nano measured
AIME 2025 (Artificial Analysis run)24.0%Not measuredOnly GPT-4.1 Nano measured
OTIS Mock AIME28.9%Not measuredOnly GPT-4.1 Nano measured
MATH level 570.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: math1274Not measuredOnly GPT-4.1 Nano measured
LMArena Text: mathematical work1274Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 5 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
Knowledge benchmarks for GPT-4.1 Nano and Ministral 3 14B 2512
BenchmarkGPT-4.1 NanoMinistral 3 14B 2512Leader
MMLU Pro63.5%Not measuredOnly GPT-4.1 Nano measured
LegalBench (Vals AI)61.1%Not measuredOnly GPT-4.1 Nano measured
CorpFin (Vals AI)42.1%Not measuredOnly GPT-4.1 Nano measured
TaxEval (Vals AI)60.8%Not measuredOnly GPT-4.1 Nano measured
MortgageTax (Vals AI)52.8%Not measuredOnly GPT-4.1 Nano measured
MedQA (Vals AI)68.2%Not measuredOnly GPT-4.1 Nano measured
MMLU Pro (Artificial Analysis run)65.7%Not measuredOnly GPT-4.1 Nano measured
SimpleQA Verified6.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: science1297Not measuredOnly GPT-4.1 Nano measured
LMArena Text: medicine and healthcare1276Not measuredOnly GPT-4.1 Nano measured
LMArena Text: legal and government1300Not measuredOnly GPT-4.1 Nano measured
LMArena Text: business and finance1267Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index04
Web design benchmarks for GPT-4.1 Nano and Ministral 3 14B 2512
BenchmarkGPT-4.1 NanoMinistral 3 14B 2512Leader
Design Arena: websites9731081Ministral 3 14B 2512
Design Arena: UI components913Not measuredOnly GPT-4.1 Nano measured
Design Arena: game dev9701037Ministral 3 14B 2512
Design Arena: data viz891Not measuredOnly GPT-4.1 Nano measured
Design Arena: 3D924989Ministral 3 14B 2512
Design Arena: all code categories9621064Ministral 3 14B 2512
Neither model has a result on the other 3 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index20 boards, none shared
General benchmarks for GPT-4.1 Nano and Ministral 3 14B 2512
BenchmarkGPT-4.1 NanoMinistral 3 14B 2512Leader
IFBench32.0%Not measuredOnly GPT-4.1 Nano measured
Long context reasoning (AA-LCR)20.3%Not measuredOnly GPT-4.1 Nano measured
DTBench52.5%Not measuredOnly GPT-4.1 Nano measured
LMCA5.5%Not measuredOnly GPT-4.1 Nano measured
Fiction.LiveBench25.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text1285Not measuredOnly GPT-4.1 Nano measured
LMArena Text: instruction following1266Not measuredOnly GPT-4.1 Nano measured
LMArena Text: creative writing1260Not measuredOnly GPT-4.1 Nano measured
LMArena Vision1063Not measuredOnly GPT-4.1 Nano measured
LMArena Text: English1303Not measuredOnly GPT-4.1 Nano measured
LMArena Text: non-English1260Not measuredOnly GPT-4.1 Nano measured
LMArena Text: Chinese1270Not measuredOnly GPT-4.1 Nano measured
LMArena Text: German1288Not measuredOnly GPT-4.1 Nano measured
LMArena Text: Japanese1198Not measuredOnly GPT-4.1 Nano measured
LMArena Text: Russian1261Not measuredOnly GPT-4.1 Nano measured
LMArena Text: multi-turn1277Not measuredOnly GPT-4.1 Nano measured
LMArena Text: longer queries1283Not measuredOnly GPT-4.1 Nano measured
LMArena Text: writing and language1261Not measuredOnly GPT-4.1 Nano measured
LMArena Text: entertainment and media1243Not measuredOnly GPT-4.1 Nano measured
LMArena Vision: English1101Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 28 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4.1 Nano and Ministral 3 14B 2512
BenchmarkGPT-4.1 NanoMinistral 3 14B 2512Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GPT-4.1 Nano$0.0003Fits in one request
Ministral 3 14B 2512$0.0003Fits in one request

Same cost

Repository review

50K input + 3K output tokens

GPT-4.1 Nano$0.0062Fits in one request
Ministral 3 14B 2512$0.011Fits in one request

GPT-4.1 Nano costs less

Agent loop

200K input + 10K output tokens

GPT-4.1 Nano$0.024Fits in one request
Ministral 3 14B 2512$0.042Fits in one request

GPT-4.1 Nano costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GPT-4.1 Nano and Ministral 3 14B 2512
SpecificationOpenAI GPT-4.1 NanoMistral Ministral 3 14B 2512
MakerOpenAIMistral AI
WeightsNo public weights listedOpen weights
ReleasedApril 14, 2025December 2, 2025
Context window1.05M262K
Input $/Mtok$0.10$0.20
Output $/Mtok$0.40$0.20
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4.1 Nano better than Ministral 3 14B 2512 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4.1 Nano for a larger context window (1.05M against 262K tokens) and work measured by AA Coding Index or Design Arena: websites, where it leads.

Which is cheaper, GPT-4.1 Nano or Ministral 3 14B 2512?

GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Ministral 3 14B 2512 costs $0.20 and $0.20. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-4.1 Nano or Ministral 3 14B 2512?

GPT-4.1 Nano, with 1.05M tokens against 262K.

How many benchmarks is the GPT-4.1 Nano vs Ministral 3 14B 2512 comparison based on?

6 benchmarks have a result for both models as of October 22, 2026. GPT-4.1 Nano's coding index rests on 0 independent benchmarks and Ministral 3 14B 2512's on 0. Where only one model has a result, the page says so instead of naming a winner.

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