Enter opens the highlighted result. Esc closes.

DeepSeek V3.2 vs Mistral Medium 3.1

Updated October 21, 2026. 58 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V3.2 Replace Mistral Medium 3.1
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
21.5
Output $/M
$0.88
47
Mistral Medium 3.1Mistral AI
Coding
No data
Overall
9.2
Output $/M
$2.00
11
vs boards won of 58 shared

Decision reading

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Mistral Medium 3.1 has none, so the two cannot be ranked on coding.

Mistral Medium 3.1 has the edge where what matters is MGSM (Vals AI) or TaxEval (Vals AI), where it leads.

Model ADeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved taskModel BMistral Medium 3.1Mistral AINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
58
DeepSeek V3.2 only
17
Mistral Medium 3.1 only
16
Like-for-like categories
8 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
DeepSeek V3.2 30.0 from 3 benchmarks, Mistral Medium 3.1 not benchmarked.
Widest category
Agentic work, 50.0 points apart on average over 1 shared benchmark.
Widest single gap
AA Math Index: DeepSeek V3.2 92.0 against Mistral Medium 3.1 38.3, 53.7 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.88 against Mistral Medium 3.1 $2.00 per million output tokens, 2.3x apart.
Context window
DeepSeek V3.2 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 V3.2BarsMistral Medium 3.1Ties
Coding60
Intelligence10
Agentic work10
Reasoning50
Math51
Knowledge64
Web design80
General156
All shared boards4711
CodingLike-for-likeSix of these boards feed the Vibe Coding Index60
Coding benchmarks for DeepSeek V3.2 and Mistral Medium 3.1
BenchmarkDeepSeek V3.2Mistral Medium 3.1Leader
SWE-bench Verified70.0%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev1362Not measuredOnly DeepSeek V3.2 measured
AA Coding Index44.220.5DeepSeek V3.2
LiveCodeBench (Vals AI)80.7%44.8%DeepSeek V3.2
SWE-bench Verified (Vals AI)67.6%Not measuredOnly DeepSeek V3.2 measured
Vibe Code Bench (Vals AI)5.1%Not measuredOnly DeepSeek V3.2 measured
LiveCodeBench (Artificial Analysis run)86.2%40.6%DeepSeek V3.2
SciCodeNot measured32.4%Only Mistral Medium 3.1 measured
Terminal-Bench Hard (Artificial Analysis run)35.6%10.6%DeepSeek V3.2
Terminal-Bench39.6%Not measuredOnly DeepSeek V3.2 measured
ALE-BenchNot measured210.2Only Mistral Medium 3.1 measured
LMArena Text: coding14531433DeepSeek V3.2
LMArena Text: software and IT14491436DeepSeek V3.2
LMArena WebDev: React1350Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev: HTML1371Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V3.2 and Mistral Medium 3.1
BenchmarkDeepSeek V3.2Mistral Medium 3.1Leader
AA Intelligence Index21.59.2DeepSeek V3.2
Epoch Capabilities Index146.1Not measuredOnly DeepSeek V3.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V3.2 and Mistral Medium 3.1
BenchmarkDeepSeek V3.2Mistral Medium 3.1Leader
AA Agentic IndexNot measured1.9Only Mistral Medium 3.1 measured
τ²-Bench Telecom90.6%40.6%DeepSeek V3.2
APEX-Agents21.3%Not measuredOnly DeepSeek V3.2 measured
Vending-Bench 2$1,034Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for DeepSeek V3.2 and Mistral Medium 3.1
BenchmarkDeepSeek V3.2Mistral Medium 3.1Leader
GPQA Diamond80.3%Not measuredOnly DeepSeek V3.2 measured
MMMU Pro (Vals AI)Not measured63.0%Only Mistral Medium 3.1 measured
GPQA Diamond (Artificial Analysis run)84.0%58.8%DeepSeek V3.2
Humanity's Last Exam (Artificial Analysis run)24.6%4.7%DeepSeek V3.2
ARC-AGI57.0%Not measuredOnly DeepSeek V3.2 measured
ARC-AGI-24.0%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: hard prompts14341426DeepSeek V3.2
LMArena Text: expert14361408DeepSeek V3.2
LMArena Text: hard prompts in English14461435DeepSeek V3.2
LMArena Vision: diagramsNot measured1175Only Mistral Medium 3.1 measured
LMArena Vision: homeworkNot measured1165Only Mistral Medium 3.1 measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index51
Math benchmarks for DeepSeek V3.2 and Mistral Medium 3.1
BenchmarkDeepSeek V3.2Mistral Medium 3.1Leader
AIME84.6%42.3%DeepSeek V3.2
MATH 500 (Vals AI)Not measured87.0%Only Mistral Medium 3.1 measured
MGSM (Vals AI)86.0%90.9%Mistral Medium 3.1
AA Math Index92.038.3DeepSeek V3.2
AIME 2025 (Artificial Analysis run)92.0%38.3%DeepSeek V3.2
LMArena Text: math14351408DeepSeek V3.2
LMArena Text: mathematical work14401412DeepSeek V3.2
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index64
Knowledge benchmarks for DeepSeek V3.2 and Mistral Medium 3.1
BenchmarkDeepSeek V3.2Mistral Medium 3.1Leader
MMLU Pro84.9%75.3%DeepSeek V3.2
LegalBench (Vals AI)76.1%62.0%DeepSeek V3.2
CaseLaw (Vals AI)55.4%Not measuredOnly DeepSeek V3.2 measured
CorpFin (Vals AI)51.0%50.7%DeepSeek V3.2
TaxEval (Vals AI)68.2%70.3%Mistral Medium 3.1
MortgageTax (Vals AI)Not measured36.5%Only Mistral Medium 3.1 measured
MedQA (Vals AI)93.9%78.2%DeepSeek V3.2
MMLU Pro (Artificial Analysis run)86.2%68.3%DeepSeek V3.2
LMArena Text: science14461444DeepSeek V3.2
LMArena Text: medicine and healthcare14401449Mistral Medium 3.1
LMArena Text: legal and government14251439Mistral Medium 3.1
LMArena Text: business and finance14101418Mistral Medium 3.1
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for DeepSeek V3.2 and Mistral Medium 3.1
BenchmarkDeepSeek V3.2Mistral Medium 3.1Leader
Design Arena: websites11751133DeepSeek V3.2
Design Arena: UI components11491099DeepSeek V3.2
Design Arena: game dev11311074DeepSeek V3.2
Design Arena: data viz11581144DeepSeek V3.2
Design Arena: 3D11331081DeepSeek V3.2
Design Arena: all code categories11641120DeepSeek V3.2
Design Arena: SVG1032993DeepSeek V3.2
Design Arena: ASCII art1082997DeepSeek V3.2
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index156
General benchmarks for DeepSeek V3.2 and Mistral Medium 3.1
BenchmarkDeepSeek V3.2Mistral Medium 3.1Leader
IFBench60.7%39.8%DeepSeek V3.2
Long context reasoning (AA-LCR)73.3%42.7%DeepSeek V3.2
DTBench85.6%67.5%DeepSeek V3.2
LMCA28.8%19.1%DeepSeek V3.2
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text14251424DeepSeek V3.2
LMArena Text: instruction following14131398DeepSeek V3.2
LMArena Text: creative writing14001391DeepSeek V3.2
LMArena VisionNot measured1172Only Mistral Medium 3.1 measured
LMArena Text: English14391438DeepSeek V3.2
LMArena Text: non-English14091408DeepSeek V3.2
LMArena Text: Chinese14551447DeepSeek V3.2
LMArena Text: German14131432Mistral Medium 3.1
LMArena Text: Japanese13741379Mistral Medium 3.1
LMArena Text: Korean13701380Mistral Medium 3.1
LMArena Text: French14291460Mistral Medium 3.1
LMArena Text: Spanish14241433Mistral Medium 3.1
LMArena Text: Russian14241411DeepSeek V3.2
LMArena Text: Polish14191430Mistral Medium 3.1
LMArena Text: multi-turn14271418DeepSeek V3.2
LMArena Text: longer queries14281406DeepSeek V3.2
LMArena Text: writing and language14071395DeepSeek V3.2
LMArena Text: entertainment and media13951390DeepSeek V3.2
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 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.2 and Mistral Medium 3.1
BenchmarkDeepSeek V3.2Mistral 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$0.0007Fits in one request
Mistral Medium 3.1$0.0014Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.017Fits in one request
Mistral Medium 3.1$0.026Fits in one request

DeepSeek V3.2 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.067Exceeds the context window
Mistral Medium 3.1$0.100Exceeds the context window

DeepSeek V3.2 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 and Mistral Medium 3.1
SpecificationDeepSeek V3.2Mistral Medium 3.1
MakerDeepSeekMistral AI
WeightsOpen weightsNo public weights listed
ReleasedDecember 1, 2025August 13, 2025
Context window164K131K
Input $/Mtok$0.29$0.40
Output $/Mtok$0.88$2.00
Coding index30.0Not benchmarked
Independent measurements behind it30
Cost per solved task$0.99Not computable
SourceOpenRouter listingOpenRouter listing

Questions

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

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Mistral Medium 3.1 has none, so the two cannot be ranked on coding. Mistral Medium 3.1 has the edge where what matters is MGSM (Vals AI) or TaxEval (Vals AI), where it leads.

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

DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; Mistral Medium 3.1 costs $0.40 and $2.00. Cost per solved task is known for DeepSeek V3.2 only.

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

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

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

58 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks and Mistral Medium 3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.

Compare next