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GPT-4.1 vs Mistral Large

Updated October 21, 2026. 43 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GPT-4.1 Replace Mistral Large
GPT-4.1OpenAI
Coding
No data
Overall
12.7
Output $/M
$8.00
43
Mistral LargeMistral AI
Coding
No data
Overall
5.8
Output $/M
$6.00
0
vs boards won of 43 shared

Decision reading

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

GPT-4.1 has the edge where the context window is larger, 1.05M against 128K tokens.

Model AGPT-4.1OpenAINot benchmarkedNo coding benchmark$0.37 per solved taskModel BMistral LargeMistral AINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
43
GPT-4.1 only
44
Mistral Large only
1
Like-for-like categories
6 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 not benchmarked, Mistral Large not benchmarked.
Widest category
Coding, 21.0 points apart on average over 4 shared benchmarks.
Widest single gap
LMArena Text: Polish: GPT-4.1 1389 against Mistral Large 1157, 38.6 points apart on a 0 to 100 scale.
Output price
GPT-4.1 $8.00 against Mistral Large $6.00 per million output tokens, 1.3x apart.
Context window
GPT-4.1 takes 1.05M tokens, Mistral Large 128K.

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-4.1BarsMistral LargeTies
Coding40
Intelligence20
Reasoning60
Math50
Knowledge80
General180
All shared boards430
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for GPT-4.1 and Mistral Large
BenchmarkGPT-4.1Mistral LargeLeader
Aider polyglot52.4%Not measuredOnly GPT-4.1 measured
SWE-bench Verified39.6%Not measuredOnly GPT-4.1 measured
SWE-bench Multimodal31.1%Not measuredOnly GPT-4.1 measured
LiveCodeBench (Vals AI)54.7%37.1%GPT-4.1
LiveCodeBench (Artificial Analysis run)45.7%17.8%GPT-4.1
Terminal-Bench Hard (Artificial Analysis run)13.6%Not measuredOnly GPT-4.1 measured
WeirdML39.0%Not measuredOnly GPT-4.1 measured
ALE-Bench558.1Not measuredOnly GPT-4.1 measured
LMArena Text: coding13901275GPT-4.1
LMArena Text: software and IT13911274GPT-4.1
Neither model has a result on the other 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GPT-4.1 and Mistral Large
BenchmarkGPT-4.1Mistral LargeLeader
AA Intelligence Index12.75.8GPT-4.1
Epoch Capabilities Index136.7122.1GPT-4.1
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Agentic work benchmarks for GPT-4.1 and Mistral Large
BenchmarkGPT-4.1Mistral LargeLeader
τ²-Bench Telecom47.1%Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GPT-4.1 and Mistral Large
BenchmarkGPT-4.1Mistral LargeLeader
GPQA Diamond65.4%47.7%GPT-4.1
MMMU Pro (Vals AI)72.4%Not measuredOnly GPT-4.1 measured
GPQA Diamond (Artificial Analysis run)66.6%35.1%GPT-4.1
Humanity's Last Exam (Artificial Analysis run)4.2%3.5%GPT-4.1
GPQA Diamond (Epoch AI run)66.9%Not measuredOnly GPT-4.1 measured
ARC-AGI5.5%Not measuredOnly GPT-4.1 measured
ARC-AGI-20.4%Not measuredOnly GPT-4.1 measured
SimpleBench27.0%Not measuredOnly GPT-4.1 measured
Chess puzzles3.0%Not measuredOnly GPT-4.1 measured
LMArena Text: hard prompts13841257GPT-4.1
LMArena Text: expert13641208GPT-4.1
LMArena Text: hard prompts in English13931268GPT-4.1
LMArena Vision: diagrams1215Not measuredOnly GPT-4.1 measured
LMArena Vision: homework1248Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index50
Math benchmarks for GPT-4.1 and Mistral Large
BenchmarkGPT-4.1Mistral LargeLeader
AIME39.6%9.2%GPT-4.1
MATH 500 (Vals AI)87.2%74.4%GPT-4.1
MGSM (Vals AI)87.7%87.2%GPT-4.1
AA Math Index34.7Not measuredOnly GPT-4.1 measured
AIME 2025 (Artificial Analysis run)34.7%Not measuredOnly GPT-4.1 measured
OTIS Mock AIME38.3%Not measuredOnly GPT-4.1 measured
FrontierMath, tiers 1 to 36.0%Not measuredOnly GPT-4.1 measured
MATH level 583.0%Not measuredOnly GPT-4.1 measured
LMArena Text: math13691261GPT-4.1
LMArena Text: mathematical work13601257GPT-4.1
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
Knowledge benchmarks for GPT-4.1 and Mistral Large
BenchmarkGPT-4.1Mistral LargeLeader
MMLU Pro80.5%69.7%GPT-4.1
LegalBench (Vals AI)83.1%Not measuredOnly GPT-4.1 measured
CaseLaw (Vals AI)69.9%Not measuredOnly GPT-4.1 measured
CorpFin (Vals AI)63.0%Not measuredOnly GPT-4.1 measured
TaxEval (Vals AI)75.1%63.8%GPT-4.1
MortgageTax (Vals AI)65.9%Not measuredOnly GPT-4.1 measured
MedQA (Vals AI)91.2%76.2%GPT-4.1
MMLU Pro (Artificial Analysis run)80.6%51.5%GPT-4.1
SimpleQA Verified31.1%Not measuredOnly GPT-4.1 measured
MMLUNot measured68.8%Only Mistral Large measured
GeoBench72.0%Not measuredOnly GPT-4.1 measured
LMArena Text: science13821260GPT-4.1
LMArena Text: medicine and healthcare13791255GPT-4.1
LMArena Text: legal and government13991275GPT-4.1
LMArena Text: business and finance13691238GPT-4.1
Neither model has a result on the other 2 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Web design benchmarks for GPT-4.1 and Mistral Large
BenchmarkGPT-4.1Mistral LargeLeader
Design Arena: websites1039Not measuredOnly GPT-4.1 measured
Design Arena: UI components1000Not measuredOnly GPT-4.1 measured
Design Arena: game dev1079Not measuredOnly GPT-4.1 measured
Design Arena: data viz1104Not measuredOnly GPT-4.1 measured
Design Arena: 3D850Not measuredOnly GPT-4.1 measured
Design Arena: all code categories1026Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
General benchmarks for GPT-4.1 and Mistral Large
BenchmarkGPT-4.1Mistral LargeLeader
IFBench43.0%Not measuredOnly GPT-4.1 measured
Long context reasoning (AA-LCR)68.3%Not measuredOnly GPT-4.1 measured
DTBench68.3%60.8%GPT-4.1
LMCA25.6%Not measuredOnly GPT-4.1 measured
Fiction.LiveBench63.9%Not measuredOnly GPT-4.1 measured
LMArena Text13831265GPT-4.1
LMArena Text: instruction following13671249GPT-4.1
LMArena Text: creative writing13631242GPT-4.1
LMArena Vision1210Not measuredOnly GPT-4.1 measured
LMArena Text: English13921284GPT-4.1
LMArena Text: non-English13701235GPT-4.1
LMArena Text: Chinese13821239GPT-4.1
LMArena Text: German13811242GPT-4.1
LMArena Text: Japanese13191170GPT-4.1
LMArena Text: Korean13381202GPT-4.1
LMArena Text: French13821325GPT-4.1
LMArena Text: Spanish13761268GPT-4.1
LMArena Text: Russian13771252GPT-4.1
LMArena Text: Polish13891157GPT-4.1
LMArena Text: multi-turn13971260GPT-4.1
LMArena Text: longer queries13851261GPT-4.1
LMArena Text: writing and language13711261GPT-4.1
LMArena Text: entertainment and media13651230GPT-4.1
LMArena Vision: English1209Not measuredOnly GPT-4.1 measured
LMArena Vision: Chinese1226Not measuredOnly GPT-4.1 measured
LMArena Vision: reading text1218Not measuredOnly GPT-4.1 measured
LMArena Vision: humor1196Not measuredOnly GPT-4.1 measured
LMArena Vision: captioning1207Not measuredOnly GPT-4.1 measured
LMArena Vision: recognizing things1208Not measuredOnly GPT-4.1 measured
LMArena Vision: creative writing1210Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 16 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4.1 and Mistral Large
BenchmarkGPT-4.1Mistral LargeLeader
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$0.0060Fits in one request
Mistral Large$0.0050Fits in one request

Mistral Large costs less

Repository review

50K input + 3K output tokens

GPT-4.1$0.124Fits in one request
Mistral Large$0.118Fits in one request

Mistral Large costs less

Agent loop

200K input + 10K output tokens

GPT-4.1$0.480Fits in one request
Mistral Large$0.460Exceeds the context window

Mistral Large 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 and Mistral Large
SpecificationOpenAI GPT-4.1Mistral Large
MakerOpenAIMistral AI
WeightsNo public weights listedNo public weights listed
ReleasedApril 14, 2025February 26, 2024
Context window1.05M128K
Input $/Mtok$2.00$2.00
Output $/Mtok$8.00$6.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved task$0.37Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4.1 better than Mistral Large for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. GPT-4.1 has the edge where the context window is larger, 1.05M against 128K tokens.

Which is cheaper, GPT-4.1 or Mistral Large?

GPT-4.1 costs $2.00 per million input tokens and $8.00 per million output tokens; Mistral Large costs $2.00 and $6.00. Cost per solved task is known for GPT-4.1 only.

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

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

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

43 benchmarks have a result for both models as of October 21, 2026. GPT-4.1's coding index rests on 0 independent benchmarks and Mistral Large's on 0. Where only one model has a result, the page says so instead of naming a winner.

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