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DeepSeek V3.2 Exp vs Mistral Medium 3.1

DeepSeek V3.2 Exp leads Mistral Medium 3.1 on the Artificial Analysis Intelligence Index, 16.6 to 9.2, and DeepSeek V3.2 Exp costs 4.9x less per output token ($0.41 vs $2.00 per million). Updated October 21, 2026; 48 benchmarks have a result for both.

Replace DeepSeek V3.2 Exp Replace Mistral Medium 3.1
DeepSeek V3.2 ExpDeepSeek
Coding
No data
Overall
No data
Output $/M
$0.41
40
Mistral Medium 3.1Mistral AI
Coding
No data
Overall
No data
Output $/M
$2.00
8
vs boards won of 48 shared

DeepSeek V3.2 Exp or Mistral Medium 3.1: which to pick

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

Pick DeepSeek V3.2 Exp for output tokens 4.9x cheaper ($0.41 vs $2.00 per million) and a larger context window (164K against 131K tokens).

Model ADeepSeek V3.2 ExpDeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BMistral Medium 3.1Mistral AINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
48
DeepSeek V3.2 Exp only
11
Mistral Medium 3.1 only
26
Like-for-like categories
8 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
DeepSeek V3.2 Exp not benchmarked, Mistral Medium 3.1 not benchmarked.
Widest category
Math, 26.1 points apart on average over 4 shared benchmarks.
Widest single gap
AA Math Index: DeepSeek V3.2 Exp 87.7 against Mistral Medium 3.1 38.3, 49.4 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against Mistral Medium 3.1 $2.00 per million output tokens, 4.9x apart.
Context window
DeepSeek V3.2 Exp takes 164K tokens, Mistral Medium 3.1 131K.

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
CategoryDeepSeek V3.2 ExpBarsMistral Medium 3.1Ties
Coding40
Intelligence10
Agentic work01
Reasoning50
Math40
Knowledge23
Web design70
General174
All shared boards408
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for DeepSeek V3.2 Exp and Mistral Medium 3.1
BenchmarkDeepSeek V3.2 ExpMistral Medium 3.1Leader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena WebDev1272Not measuredOnly DeepSeek V3.2 Exp measured
AA Coding IndexNot measured20.5Only Mistral Medium 3.1 measured
LiveCodeBench (Vals AI)Not measured44.8%Only Mistral Medium 3.1 measured
LiveCodeBench (Artificial Analysis run)78.9%40.6%DeepSeek V3.2 Exp
SciCodeNot measured32.4%Only Mistral Medium 3.1 measured
Terminal-Bench Hard (Artificial Analysis run)31.1%10.6%DeepSeek V3.2 Exp
WeirdML39.5%Not measuredOnly DeepSeek V3.2 Exp measured
ALE-BenchNot measured210.2Only Mistral Medium 3.1 measured
LMArena Text: coding14381433DeepSeek V3.2 Exp
LMArena Text: software and IT14381436DeepSeek V3.2 Exp
LMArena WebDev: HTML1281Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V3.2 Exp and Mistral Medium 3.1
BenchmarkDeepSeek V3.2 ExpMistral Medium 3.1Leader
AA Intelligence Index16.69.2DeepSeek V3.2 Exp
Epoch Capabilities Index144.9Not measuredOnly DeepSeek V3.2 Exp measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V3.2 Exp and Mistral Medium 3.1
BenchmarkDeepSeek V3.2 ExpMistral Medium 3.1Leader
AA Agentic IndexNot measured1.9Only Mistral Medium 3.1 measured
τ²-Bench Telecom33.9%40.6%Mistral Medium 3.1
TheAgentCompany42.9%Not measuredOnly DeepSeek V3.2 Exp measured
Berkeley Function Calling Leaderboard (BFCL)56.7%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for DeepSeek V3.2 Exp and Mistral Medium 3.1
BenchmarkDeepSeek V3.2 ExpMistral Medium 3.1Leader
MMMU Pro (Vals AI)Not measured63.0%Only Mistral Medium 3.1 measured
GPQA Diamond (Artificial Analysis run)79.7%58.8%DeepSeek V3.2 Exp
Humanity's Last Exam (Artificial Analysis run)14.9%4.7%DeepSeek V3.2 Exp
LMArena Text: hard prompts14291426DeepSeek V3.2 Exp
LMArena Text: expert14221408DeepSeek V3.2 Exp
LMArena Text: hard prompts in English14441435DeepSeek V3.2 Exp
LMArena Vision: diagramsNot measured1175Only Mistral Medium 3.1 measured
LMArena Vision: homeworkNot measured1165Only Mistral Medium 3.1 measured
Kagi LLM Benchmark52.2%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for DeepSeek V3.2 Exp and Mistral Medium 3.1
BenchmarkDeepSeek V3.2 ExpMistral Medium 3.1Leader
AIMENot measured42.3%Only Mistral Medium 3.1 measured
MATH 500 (Vals AI)Not measured87.0%Only Mistral Medium 3.1 measured
MGSM (Vals AI)Not measured90.9%Only Mistral Medium 3.1 measured
AA Math Index87.738.3DeepSeek V3.2 Exp
AIME 2025 (Artificial Analysis run)87.7%38.3%DeepSeek V3.2 Exp
LMArena Text: math14221408DeepSeek V3.2 Exp
LMArena Text: mathematical work14331412DeepSeek V3.2 Exp
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index23
Knowledge benchmarks for DeepSeek V3.2 Exp and Mistral Medium 3.1
BenchmarkDeepSeek V3.2 ExpMistral Medium 3.1Leader
MMLU ProNot measured75.3%Only Mistral Medium 3.1 measured
LegalBench (Vals AI)Not measured62.0%Only Mistral Medium 3.1 measured
CorpFin (Vals AI)Not measured50.7%Only Mistral Medium 3.1 measured
TaxEval (Vals AI)Not measured70.3%Only Mistral Medium 3.1 measured
MortgageTax (Vals AI)Not measured36.5%Only Mistral Medium 3.1 measured
MedQA (Vals AI)Not measured78.2%Only Mistral Medium 3.1 measured
MMLU Pro (Artificial Analysis run)85.0%68.3%DeepSeek V3.2 Exp
LMArena Text: science14431444Mistral Medium 3.1
LMArena Text: medicine and healthcare14681449DeepSeek V3.2 Exp
LMArena Text: legal and government14191439Mistral Medium 3.1
LMArena Text: business and finance14121418Mistral Medium 3.1
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index70
Web design benchmarks for DeepSeek V3.2 Exp and Mistral Medium 3.1
BenchmarkDeepSeek V3.2 ExpMistral Medium 3.1Leader
Design Arena: websites11791133DeepSeek V3.2 Exp
Design Arena: UI components11671100DeepSeek V3.2 Exp
Design Arena: game dev11441074DeepSeek V3.2 Exp
Design Arena: data viz11521144DeepSeek V3.2 Exp
Design Arena: 3D11481081DeepSeek V3.2 Exp
Design Arena: all code categories11711120DeepSeek V3.2 Exp
Design Arena: SVG1032993DeepSeek V3.2 Exp
Design Arena: ASCII artNot measured997Only Mistral Medium 3.1 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index174
General benchmarks for DeepSeek V3.2 Exp and Mistral Medium 3.1
BenchmarkDeepSeek V3.2 ExpMistral Medium 3.1Leader
IFBench54.1%39.8%DeepSeek V3.2 Exp
Long context reasoning (AA-LCR)72.3%42.7%DeepSeek V3.2 Exp
DTBench87.7%67.5%DeepSeek V3.2 Exp
LMCA29.1%19.1%DeepSeek V3.2 Exp
Fiction.LiveBench83.3%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench13.2%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench Life9.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text14251424DeepSeek V3.2 Exp
LMArena Text: instruction following14011398DeepSeek V3.2 Exp
LMArena Text: creative writing14041391DeepSeek V3.2 Exp
LMArena VisionNot measured1172Only Mistral Medium 3.1 measured
LMArena Text: English14411438DeepSeek V3.2 Exp
LMArena Text: non-English14041408Mistral Medium 3.1
LMArena Text: Chinese14611447DeepSeek V3.2 Exp
LMArena Text: German14401432DeepSeek V3.2 Exp
LMArena Text: Japanese13311379Mistral Medium 3.1
LMArena Text: Korean13721380Mistral Medium 3.1
LMArena Text: French14341460Mistral Medium 3.1
LMArena Text: Spanish14411433DeepSeek V3.2 Exp
LMArena Text: Russian14121411DeepSeek V3.2 Exp
LMArena Text: Polish14351430DeepSeek V3.2 Exp
LMArena Text: multi-turn14221418DeepSeek V3.2 Exp
LMArena Text: longer queries14181406DeepSeek V3.2 Exp
LMArena Text: writing and language14071395DeepSeek V3.2 Exp
LMArena Text: entertainment and media14071390DeepSeek V3.2 Exp
LMArena Vision: EnglishNot measured1179Only Mistral Medium 3.1 measured
LMArena Vision: ChineseNot measured1190Only Mistral Medium 3.1 measured
LMArena Vision: reading textNot measured1175Only Mistral Medium 3.1 measured
LMArena Vision: humorNot measured1168Only Mistral Medium 3.1 measured
LMArena Vision: captioningNot measured1145Only Mistral Medium 3.1 measured
LMArena Vision: recognizing thingsNot measured1147Only Mistral Medium 3.1 measured
LMArena Vision: creative writingNot measured1200Only Mistral Medium 3.1 measured
Neither model has a result on the other 14 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.2 Exp and Mistral Medium 3.1
BenchmarkDeepSeek V3.2 ExpMistral Medium 3.1Leader
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

DeepSeek V3.2 Exp$0.0005Fits in one request
Mistral Medium 3.1$0.0014Fits in one request

DeepSeek V3.2 Exp costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2 Exp$0.015Fits in one request
Mistral Medium 3.1$0.026Fits in one request

DeepSeek V3.2 Exp costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2 Exp$0.058Exceeds the context window
Mistral Medium 3.1$0.100Exceeds the context window

DeepSeek V3.2 Exp 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 DeepSeek V3.2 Exp and Mistral Medium 3.1
SpecificationDeepSeek V3.2 ExpMistral Medium 3.1
MakerDeepSeekMistral AI
WeightsOpen weightsNo public weights listed
ReleasedSeptember 29, 2025August 13, 2025
Context window164K131K
Input $/Mtok$0.27$0.40
Output $/Mtok$0.41$2.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 Exp better than Mistral Medium 3.1 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 4.9x cheaper ($0.41 vs $2.00 per million) and a larger context window (164K against 131K tokens).

Which is cheaper, DeepSeek V3.2 Exp or Mistral Medium 3.1?

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Mistral Medium 3.1 costs $0.40 and $2.00. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V3.2 Exp or Mistral Medium 3.1?

DeepSeek V3.2 Exp, with 164K tokens against 131K.

How many benchmarks is the DeepSeek V3.2 Exp vs Mistral Medium 3.1 comparison based on?

48 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and Mistral Medium 3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.

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