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GPT-4.1 Nano vs Mistral Large 3 2512

Mistral Large 3 2512 leads GPT-4.1 Nano on the Artificial Analysis Intelligence Index, 9.3 to 7.8, and GPT-4.1 Nano costs 3.8x less per output token ($0.40 vs $1.50 per million). Updated October 22, 2026; 21 benchmarks have a result for both.

Replace GPT-4.1 Nano Replace Mistral Large 3 2512
GPT-4.1 NanoOpenAI
Coding
No data
Overall
24.7
Output $/M
$0.40
1
Mistral Large 3 2512Mistral AI
Coding
No data
Overall
No data
Output $/M
$1.50
20
vs boards won of 21 shared

GPT-4.1 Nano or Mistral Large 3 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 output tokens 3.8x cheaper ($0.40 vs $1.50 per million) and a larger context window (1.05M against 262K tokens).

Model AGPT-4.1 NanoOpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BMistral Large 3 2512Mistral AINot benchmarked1 independent benchmark$0.77 per solved task
Shared results
21
GPT-4.1 Nano only
50
Mistral Large 3 2512 only
7
Like-for-like categories
7 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, Mistral Large 3 2512 not benchmarked.
Widest category
Web design, 22.6 points apart on average over 6 shared benchmarks.
Widest single gap
Design Arena: data viz: GPT-4.1 Nano 891 against Mistral Large 3 2512 1136, 30.6 points apart on a 0 to 100 scale.
Output price
GPT-4.1 Nano $0.40 against Mistral Large 3 2512 $1.50 per million output tokens, 3.8x apart.
Context window
GPT-4.1 Nano takes 1.05M tokens, Mistral Large 3 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 NanoBarsMistral Large 3 2512Ties
Coding02
Intelligence01
Reasoning02
Math02
Knowledge15
Web design06
General02
All shared boards120
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for GPT-4.1 Nano and Mistral Large 3 2512
BenchmarkGPT-4.1 NanoMistral Large 3 2512Leader
Aider polyglot8.9%Not measuredOnly GPT-4.1 Nano measured
AA Coding Index11.120.1Mistral Large 3 2512
LiveCodeBench (Vals AI)42.7%55.3%Mistral Large 3 2512
SWE-bench Verified (Vals AI)Not measured41.4%Only Mistral Large 3 2512 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
ALE-BenchNot measured264.7Only Mistral Large 3 2512 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 29 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4.1 Nano and Mistral Large 3 2512
BenchmarkGPT-4.1 NanoMistral Large 3 2512Leader
AA Intelligence Index7.89.3Mistral Large 3 2512
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 Mistral Large 3 2512
BenchmarkGPT-4.1 NanoMistral Large 3 2512Leader
AA Agentic IndexNot measured1.0Only Mistral Large 3 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 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for GPT-4.1 Nano and Mistral Large 3 2512
BenchmarkGPT-4.1 NanoMistral Large 3 2512Leader
GPQA Diamond50.8%68.4%Mistral Large 3 2512
MMMU Pro (Vals AI)55.0%66.2%Mistral Large 3 2512
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 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4.1 Nano and Mistral Large 3 2512
BenchmarkGPT-4.1 NanoMistral Large 3 2512Leader
AIME26.5%42.9%Mistral Large 3 2512
MATH 500 (Vals AI)80.2%Not measuredOnly GPT-4.1 Nano measured
MGSM (Vals AI)69.3%85.4%Mistral Large 3 2512
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 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index15
Knowledge benchmarks for GPT-4.1 Nano and Mistral Large 3 2512
BenchmarkGPT-4.1 NanoMistral Large 3 2512Leader
MMLU Pro63.5%79.8%Mistral Large 3 2512
LegalBench (Vals AI)61.1%79.1%Mistral Large 3 2512
CaseLaw (Vals AI)Not measured61.4%Only Mistral Large 3 2512 measured
CorpFin (Vals AI)42.1%61.0%Mistral Large 3 2512
TaxEval (Vals AI)60.8%73.1%Mistral Large 3 2512
MortgageTax (Vals AI)52.8%52.1%GPT-4.1 Nano
MedQA (Vals AI)68.2%82.2%Mistral Large 3 2512
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 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index06
Web design benchmarks for GPT-4.1 Nano and Mistral Large 3 2512
BenchmarkGPT-4.1 NanoMistral Large 3 2512Leader
Design Arena: websites9721162Mistral Large 3 2512
Design Arena: UI components9131099Mistral Large 3 2512
Design Arena: game dev9701080Mistral Large 3 2512
Design Arena: data viz8911136Mistral Large 3 2512
Design Arena: 3D9241099Mistral Large 3 2512
Design Arena: all code categories9621141Mistral Large 3 2512
Design Arena: SVGNot measured992Only Mistral Large 3 2512 measured
Design Arena: ASCII artNot measured1069Only Mistral Large 3 2512 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
General benchmarks for GPT-4.1 Nano and Mistral Large 3 2512
BenchmarkGPT-4.1 NanoMistral Large 3 2512Leader
SAGE (Vals AI)Not measured24.6%Only Mistral Large 3 2512 measured
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%65.1%Mistral Large 3 2512
LMCA5.5%16.7%Mistral Large 3 2512
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 25 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 Mistral Large 3 2512
BenchmarkGPT-4.1 NanoMistral Large 3 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
Mistral Large 3 2512$0.0013Fits in one request

GPT-4.1 Nano costs less

Repository review

50K input + 3K output tokens

GPT-4.1 Nano$0.0062Fits in one request
Mistral Large 3 2512$0.029Fits 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
Mistral Large 3 2512$0.115Fits 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 Mistral Large 3 2512
SpecificationOpenAI GPT-4.1 NanoMistral Large 3 2512
MakerOpenAIMistral AI
WeightsNo public weights listedNo public weights listed
ReleasedApril 14, 2025December 1, 2025
Context window1.05M262K
Input $/Mtok$0.10$0.50
Output $/Mtok$0.40$1.50
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computable$0.77
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4.1 Nano better than Mistral Large 3 2512 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4.1 Nano for output tokens 3.8x cheaper ($0.40 vs $1.50 per million) and a larger context window (1.05M against 262K tokens).

Which is cheaper, GPT-4.1 Nano or Mistral Large 3 2512?

GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Mistral Large 3 2512 costs $0.50 and $1.50. Cost per solved task is known for Mistral Large 3 2512 only.

Which has the bigger context window, GPT-4.1 Nano or Mistral Large 3 2512?

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

How many benchmarks is the GPT-4.1 Nano vs Mistral Large 3 2512 comparison based on?

21 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 Mistral Large 3 2512's on 1. Where only one model has a result, the page says so instead of naming a winner.

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