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R1 0528 vs Mistral Large 3 2512

R1 0528 leads Mistral Large 3 2512 on the Artificial Analysis Intelligence Index, 13.1 to 9.3, and Mistral Large 3 2512 costs 1.5x less per output token ($1.50 vs $2.18 per million). Updated October 22, 2026; 9 benchmarks have a result for both.

Replace R1 0528 Replace Mistral Large 3 2512
R1 0528DeepSeek
Coding
No data
Overall
46.8
Output $/M
$2.18
7
Mistral Large 3 2512Mistral AI
Coding
No data
Overall
No data
Output $/M
$1.50
2
vs boards won of 9 shared

R1 0528 or Mistral Large 3 2512: which to pick

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

Pick R1 0528 for work measured by Design Arena: websites or Design Arena: all code categories, where it leads.

Model AR1 0528DeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BMistral Large 3 2512Mistral AINot benchmarked1 independent benchmark$0.77 per solved task
Shared results
9
R1 0528 only
51
Mistral Large 3 2512 only
19
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
R1 0528 not benchmarked, Mistral Large 3 2512 not benchmarked.
Widest category
Coding, 18.0 points apart on average over 1 shared benchmark.
Widest single gap
ALE-Bench: R1 0528 804.1 against Mistral Large 3 2512 264.7, 18.0 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.18 against Mistral Large 3 2512 $1.50 per million output tokens, 1.5x apart.
Context window
R1 0528 takes 164K 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
CategoryR1 0528BarsMistral Large 3 2512Ties
Coding10
Intelligence10
Web design52
All shared boards72
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
Coding benchmarks for R1 0528 and Mistral Large 3 2512
BenchmarkR1 0528Mistral Large 3 2512Leader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
AA Coding IndexNot measured20.1Only Mistral Large 3 2512 measured
LiveCodeBench (Vals AI)Not measured55.3%Only Mistral Large 3 2512 measured
SWE-bench Verified (Vals AI)Not measured41.4%Only Mistral Large 3 2512 measured
LiveCodeBench (Artificial Analysis run)77.0%Not measuredOnly R1 0528 measured
Terminal-Bench Hard (Artificial Analysis run)15.9%Not measuredOnly R1 0528 measured
WeirdML41.6%Not measuredOnly R1 0528 measured
ALE-Bench804.1264.7R1 0528
LMArena Text: coding1427Not measuredOnly R1 0528 measured
LMArena Text: software and IT1435Not measuredOnly R1 0528 measured
Neither model has a result on the other 29 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for R1 0528 and Mistral Large 3 2512
BenchmarkR1 0528Mistral Large 3 2512Leader
AA Intelligence Index13.19.3R1 0528
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Agentic work benchmarks for R1 0528 and Mistral Large 3 2512
BenchmarkR1 0528Mistral Large 3 2512Leader
AA Agentic IndexNot measured1.0Only Mistral Large 3 2512 measured
τ²-Bench Telecom36.5%Not measuredOnly R1 0528 measured
METR time horizons53.8%Not measuredOnly R1 0528 measured
DeepResearch Bench35.1%Not measuredOnly R1 0528 measured
BrowseComp-Plus16.4%Not measuredOnly R1 0528 measured
Neither model has a result on the other 36 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
Reasoning benchmarks for R1 0528 and Mistral Large 3 2512
BenchmarkR1 0528Mistral Large 3 2512Leader
GPQA DiamondNot measured68.4%Only Mistral Large 3 2512 measured
MMMU Pro (Vals AI)Not measured66.2%Only Mistral Large 3 2512 measured
GPQA Diamond (Artificial Analysis run)81.3%Not measuredOnly R1 0528 measured
Humanity's Last Exam (Artificial Analysis run)15.8%Not measuredOnly R1 0528 measured
GPQA Diamond (Epoch AI run)76.3%Not measuredOnly R1 0528 measured
ARC-AGI21.2%Not measuredOnly R1 0528 measured
ARC-AGI-21.1%Not measuredOnly R1 0528 measured
SimpleBench40.8%Not measuredOnly R1 0528 measured
LMArena Text: hard prompts1415Not measuredOnly R1 0528 measured
LMArena Text: expert1394Not measuredOnly R1 0528 measured
LMArena Text: hard prompts in English1422Not measuredOnly R1 0528 measured
Neither model has a result on the other 11 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Math benchmarks for R1 0528 and Mistral Large 3 2512
BenchmarkR1 0528Mistral Large 3 2512Leader
AIMENot measured42.9%Only Mistral Large 3 2512 measured
MGSM (Vals AI)Not measured85.4%Only Mistral Large 3 2512 measured
AA Math Index76.0Not measuredOnly R1 0528 measured
AIME 2025 (Artificial Analysis run)76.0%Not measuredOnly R1 0528 measured
OTIS Mock AIME66.4%Not measuredOnly R1 0528 measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math1400Not measuredOnly R1 0528 measured
LMArena Text: mathematical work1415Not measuredOnly R1 0528 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 R1 0528 and Mistral Large 3 2512
BenchmarkR1 0528Mistral Large 3 2512Leader
MMLU ProNot measured79.8%Only Mistral Large 3 2512 measured
LegalBench (Vals AI)Not measured79.1%Only Mistral Large 3 2512 measured
CaseLaw (Vals AI)Not measured61.4%Only Mistral Large 3 2512 measured
CorpFin (Vals AI)Not measured61.0%Only Mistral Large 3 2512 measured
TaxEval (Vals AI)Not measured73.1%Only Mistral Large 3 2512 measured
MortgageTax (Vals AI)Not measured52.1%Only Mistral Large 3 2512 measured
MedQA (Vals AI)Not measured82.2%Only Mistral Large 3 2512 measured
MMLU Pro (Artificial Analysis run)84.9%Not measuredOnly R1 0528 measured
LMArena Text: science1430Not measuredOnly R1 0528 measured
LMArena Text: medicine and healthcare1445Not measuredOnly R1 0528 measured
LMArena Text: legal and government1419Not measuredOnly R1 0528 measured
LMArena Text: business and finance1402Not measuredOnly R1 0528 measured
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index52
Web design benchmarks for R1 0528 and Mistral Large 3 2512
BenchmarkR1 0528Mistral Large 3 2512Leader
Design Arena: websites11491162Mistral Large 3 2512
Design Arena: UI components11001099R1 0528
Design Arena: game dev10971080R1 0528
Design Arena: data viz11821136R1 0528
Design Arena: 3D11101099R1 0528
Design Arena: all code categories11381141Mistral Large 3 2512
Design Arena: SVG1038992R1 0528
Design Arena: ASCII artNot measured1069Only Mistral Large 3 2512 measured
Neither model has a result on the other 1 board here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index24 boards, none shared
General benchmarks for R1 0528 and Mistral Large 3 2512
BenchmarkR1 0528Mistral Large 3 2512Leader
SAGE (Vals AI)Not measured24.6%Only Mistral Large 3 2512 measured
IFBench39.6%Not measuredOnly R1 0528 measured
Long context reasoning (AA-LCR)55.7%Not measuredOnly R1 0528 measured
DTBenchNot measured65.1%Only Mistral Large 3 2512 measured
LMCANot measured16.7%Only Mistral Large 3 2512 measured
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
LMArena Text1428Not measuredOnly R1 0528 measured
LMArena Text: instruction following1382Not measuredOnly R1 0528 measured
LMArena Text: creative writing1405Not measuredOnly R1 0528 measured
LMArena Text: English1437Not measuredOnly R1 0528 measured
LMArena Text: non-English1412Not measuredOnly R1 0528 measured
LMArena Text: Chinese1442Not measuredOnly R1 0528 measured
LMArena Text: German1404Not measuredOnly R1 0528 measured
LMArena Text: Japanese1391Not measuredOnly R1 0528 measured
LMArena Text: Korean1360Not measuredOnly R1 0528 measured
LMArena Text: French1417Not measuredOnly R1 0528 measured
LMArena Text: Spanish1411Not measuredOnly R1 0528 measured
LMArena Text: Russian1423Not measuredOnly R1 0528 measured
LMArena Text: Polish1428Not measuredOnly R1 0528 measured
LMArena Text: multi-turn1405Not measuredOnly R1 0528 measured
LMArena Text: longer queries1391Not measuredOnly R1 0528 measured
LMArena Text: writing and language1402Not measuredOnly R1 0528 measured
LMArena Text: entertainment and media1400Not measuredOnly R1 0528 measured
Neither model has a result on the other 22 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for R1 0528 and Mistral Large 3 2512
BenchmarkR1 0528Mistral 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

R1 0528$0.0016Fits in one request
Mistral Large 3 2512$0.0013Fits in one request

Mistral Large 3 2512 costs less

Repository review

50K input + 3K output tokens

R1 0528$0.032Fits in one request
Mistral Large 3 2512$0.029Fits in one request

Mistral Large 3 2512 costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.122Exceeds the context window
Mistral Large 3 2512$0.115Fits in one request

Mistral Large 3 2512 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 R1 0528 and Mistral Large 3 2512
SpecificationDeepSeek R1 0528Mistral Large 3 2512
MakerDeepSeekMistral AI
WeightsOpen weightsNo public weights listed
ReleasedMay 28, 2025December 1, 2025
Context window164K262K
Input $/Mtok$0.50$0.50
Output $/Mtok$2.18$1.50
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computable$0.77
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than Mistral Large 3 2512 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick R1 0528 for work measured by Design Arena: websites or Design Arena: all code categories, where it leads.

Which is cheaper, R1 0528 or Mistral Large 3 2512?

R1 0528 costs $0.50 per million input tokens and $2.18 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, R1 0528 or Mistral Large 3 2512?

Mistral Large 3 2512, with 262K tokens against 164K.

How many benchmarks is the R1 0528 vs Mistral Large 3 2512 comparison based on?

9 benchmarks have a result for both models as of October 22, 2026. R1 0528'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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