Noometry

Model comparison

GPT-4.1 nano vs Pixtral Large

Pixtral Large is the stronger model overall, scoring 32.2 to 27.9 on the Noometry Index. GPT-4.1 nano costs 17× less per token, which makes it the better buy when Pixtral Large's lead doesn't matter for your workload.

Last verified . 2 shared benchmarks.

GPT-4.1 nano OpenAI

27.9

Rank #327 Confirmed

Pixtral Large Mistral AI

32.2

Rank #259 Reported

Summary

  • They share 2 benchmarks with published results for both. GPT-4.1 nano scores higher in 1 category and Pixtral Large in 2 categories; 3 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where Pixtral Large leads 21.7 to 8.5.
  • GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $2 / $6 for Pixtral Large.
  • GPT-4.1 nano accepts more context: 1.05M tokens versus 128K.
  • Pixtral Large has downloadable open weights; the other is API-only.

Side by side

GPT-4.1 nanoPixtral Large
ProviderOpenAIMistral AI
Noometry Index27.932.2
Released2025-04-142024-11-01
WeightsProprietaryOpen
Context window1.05M128K
Max output33K128K
Input $ / M tokens$0.10$2
Output $ / M tokens$0.40$6
Results tracked383

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Category by category

Coding Not comparable

GPT-4.1 nano: 24.1 (#330), Pixtral Large: —

BenchmarkGPT-4.1 nanoPixtral Large
Aider Polyglot8.9%—
SciCode25.9%—
WeirdML19%—
LMArena Coding1306—

Agentic & Tool Use Not comparable

GPT-4.1 nano: 26.5 (#104), Pixtral Large: —

BenchmarkGPT-4.1 nanoPixtral Large
Berkeley Function Calling Leaderboard33%—

Reasoning Pixtral Large leads

GPT-4.1 nano: 8.5 (#349), Pixtral Large: 21.7 (#218)

BenchmarkGPT-4.1 nanoPixtral Large
ARC-AGI-20%—
Kagi LLM Benchmark33.3%—
ARC-AGI-10%—
CritPt0%—
EnigmaEval—0.8%
LMArena Hard Prompts1286—
DTBench52.5%—
LMCA5.5%—
Epoch Capabilities Index129.62—

Math Not comparable

GPT-4.1 nano: 26.9 (#252), Pixtral Large: —

BenchmarkGPT-4.1 nanoPixtral Large
OTIS Mock AIME 2024-202528.9%—
Omni-MATH36.7%—
LMArena Math1274—
MATH Level 570%—
FrontierMath (Feb 2025 set)1%—

Knowledge Not comparable

GPT-4.1 nano: 21.8 (#273), Pixtral Large: —

BenchmarkGPT-4.1 nanoPixtral Large
GPQA Diamond48.9%—
SimpleQA Verified6%—
MMLU-Pro55%—
GPQA (HELM)50.7%—
LMArena Expert1272—

Multimodal Pixtral Large leads

GPT-4.1 nano: 29.2 (#113), Pixtral Large: 30.6 (#111)

BenchmarkGPT-4.1 nanoPixtral Large
LMArena Vision10631089

Multilingual Not comparable

GPT-4.1 nano: 41.6 (#205), Pixtral Large: —

BenchmarkGPT-4.1 nanoPixtral Large
LMArena Non-English1260—
LMArena Chinese1270—
LMArena German1288—
LMArena Japanese1198—
LMArena Russian1261—

Instruction Following Not comparable

GPT-4.1 nano: 67.8 (#193), Pixtral Large: —

BenchmarkGPT-4.1 nanoPixtral Large
IFEval84.3%—
LMArena Instruction Following1267—

Long Context Not comparable

GPT-4.1 nano: 23.7 (#296), Pixtral Large: —

BenchmarkGPT-4.1 nanoPixtral Large
Fiction.LiveBench25%—
LMArena Longer Query1283—

Writing & Preference GPT-4.1 nano leads

GPT-4.1 nano: 40.5 (#243), Pixtral Large: 32.9 (#278)

BenchmarkGPT-4.1 nanoPixtral Large
EQ-Bench Creative Writing946988
LMArena Text1285—
LMArena Creative Writing1260—
WildBench81.2%—
LMArena Multi-Turn1277—

Frequently asked questions

Is GPT-4.1 nano better than Pixtral Large?

Pixtral Large is the stronger model overall, scoring 32.2 to 27.9 on the Noometry Index. GPT-4.1 nano costs 17× less per token, which makes it the better buy when Pixtral Large's lead doesn't matter for your workload.

Which is cheaper, GPT-4.1 nano or Pixtral Large?

GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Pixtral Large lists at $2 and $6.

Which has the bigger context window?

GPT-4.1 nano does, with 1.05M tokens against 128K.

How many benchmarks do GPT-4.1 nano and Pixtral Large share?

2 benchmarks have published results for both models. GPT-4.1 nano has 38 scored results on Noometry and Pixtral Large has 3.

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