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Mistral Medium 3.1 vs Nemotron 3.5 Super VL

Nemotron 3.5 Super VL leads Mistral Medium 3.1 on the Artificial Analysis Intelligence Index, 26.5 to 9.2, and Nemotron 3.5 Super VL costs 4.0x less per output token ($0.50 vs $2.00 per million). Updated October 23, 2026; 9 benchmarks have a result for both.

Mistral Medium 3.1Mistral AI
Coding
No data
Overall
No data
Output $/M
$2.00
0
Nemotron 3.5 Super VLNVIDIA
Coding
No data
Overall
35.6
Output $/M
$0.50
9
vs boards won of 9 shared

Mistral Medium 3.1 or Nemotron 3.5 Super VL: which to pick

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

Pick Mistral Medium 3.1 for work measured by SciCode or AA Intelligence Index, where it leads.

Shared results
9
Mistral Medium 3.1 only
65
Nemotron 3.5 Super VL only
7
Like-for-like categories
5 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
Mistral Medium 3.1 not benchmarked, Nemotron 3.5 Super VL not benchmarked.
Widest category
General, 26.3 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): Mistral Medium 3.1 42.7% against Nemotron 3.5 Super VL 69.0%, 26.3 points apart on a 0 to 100 scale.
Output price
Mistral Medium 3.1 $2.00 against Nemotron 3.5 Super VL $0.50 per million output tokens, 4.0x apart.
Context window
Mistral Medium 3.1 takes 131K tokens, Nemotron 3.5 Super VL 1.05M.

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
CategoryMistral Medium 3.1BarsNemotron 3.5 Super VLTies
Coding01
Intelligence01
Reasoning01
Web design05
General01
All shared boards09
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
Coding benchmarks for Mistral Medium 3.1 and Nemotron 3.5 Super VL
BenchmarkMistral Medium 3.1Nemotron 3.5 Super VLLeader
LiveBench codingNot measured65.5%Only Nemotron 3.5 Super VL measured
AA Coding Index20.5Not measuredOnly Mistral Medium 3.1 measured
LiveCodeBench (Vals AI)44.8%Not measuredOnly Mistral Medium 3.1 measured
LiveCodeBench (Artificial Analysis run)40.6%Not measuredOnly Mistral Medium 3.1 measured
SciCode32.4%47.8%Nemotron 3.5 Super VL
Terminal-Bench Hard (Artificial Analysis run)10.6%Not measuredOnly Mistral Medium 3.1 measured
ALE-Bench210.2Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: coding1433Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: software and IT1436Not measuredOnly Mistral Medium 3.1 measured
Neither model has a result on the other 34 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Mistral Medium 3.1 and Nemotron 3.5 Super VL
BenchmarkMistral Medium 3.1Nemotron 3.5 Super VLLeader
AA Intelligence Index9.226.5Nemotron 3.5 Super VL
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 Mistral Medium 3.1 and Nemotron 3.5 Super VL
BenchmarkMistral Medium 3.1Nemotron 3.5 Super VLLeader
AA Agentic Index1.9Not measuredOnly Mistral Medium 3.1 measured
LiveBench researchNot measured70.9%Only Nemotron 3.5 Super VL measured
LiveBench browserNot measured4.0%Only Nemotron 3.5 Super VL measured
LiveBench long agenticNot measured15.8%Only Nemotron 3.5 Super VL measured
τ²-Bench Telecom40.6%Not measuredOnly Mistral Medium 3.1 measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
Reasoning benchmarks for Mistral Medium 3.1 and Nemotron 3.5 Super VL
BenchmarkMistral Medium 3.1Nemotron 3.5 Super VLLeader
LiveBench reasoningNot measured78.8%Only Nemotron 3.5 Super VL measured
MMMU Pro (Vals AI)63.0%Not measuredOnly Mistral Medium 3.1 measured
GPQA Diamond (Artificial Analysis run)58.8%Not measuredOnly Mistral Medium 3.1 measured
Humanity's Last Exam (Artificial Analysis run)4.7%30.7%Nemotron 3.5 Super VL
LMArena Text: hard prompts1426Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: expert1408Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: hard prompts in English1435Not measuredOnly Mistral Medium 3.1 measured
LMArena Vision: diagrams1175Not measuredOnly Mistral Medium 3.1 measured
LMArena Vision: homework1165Not measuredOnly Mistral Medium 3.1 measured
Neither model has a result on the other 14 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Math benchmarks for Mistral Medium 3.1 and Nemotron 3.5 Super VL
BenchmarkMistral Medium 3.1Nemotron 3.5 Super VLLeader
AIME42.3%Not measuredOnly Mistral Medium 3.1 measured
MATH 500 (Vals AI)87.0%Not measuredOnly Mistral Medium 3.1 measured
MGSM (Vals AI)90.9%Not measuredOnly Mistral Medium 3.1 measured
AA Math Index38.3Not measuredOnly Mistral Medium 3.1 measured
AIME 2025 (Artificial Analysis run)38.3%Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: math1408Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: mathematical work1412Not measuredOnly Mistral Medium 3.1 measured
Neither model has a result on the other 7 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
Knowledge benchmarks for Mistral Medium 3.1 and Nemotron 3.5 Super VL
BenchmarkMistral Medium 3.1Nemotron 3.5 Super VLLeader
MMLU Pro75.3%Not measuredOnly Mistral Medium 3.1 measured
LegalBench (Vals AI)62.0%Not measuredOnly Mistral Medium 3.1 measured
CorpFin (Vals AI)50.7%Not measuredOnly Mistral Medium 3.1 measured
TaxEval (Vals AI)70.3%Not measuredOnly Mistral Medium 3.1 measured
MortgageTax (Vals AI)36.5%Not measuredOnly Mistral Medium 3.1 measured
MedQA (Vals AI)78.2%Not measuredOnly Mistral Medium 3.1 measured
MMLU Pro (Artificial Analysis run)68.3%Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: science1444Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: medicine and healthcare1449Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: legal and government1439Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: business and finance1418Not measuredOnly Mistral Medium 3.1 measured
Neither model has a result on the other 9 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index05
Web design benchmarks for Mistral Medium 3.1 and Nemotron 3.5 Super VL
BenchmarkMistral Medium 3.1Nemotron 3.5 Super VLLeader
Design Arena: websites11321172Nemotron 3.5 Super VL
Design Arena: UI components10971148Nemotron 3.5 Super VL
Design Arena: game dev10731110Nemotron 3.5 Super VL
Design Arena: data viz1144Not measuredOnly Mistral Medium 3.1 measured
Design Arena: 3D10821127Nemotron 3.5 Super VL
Design Arena: all code categories11201154Nemotron 3.5 Super VL
Design Arena: SVG992Not measuredOnly Mistral Medium 3.1 measured
Design Arena: ASCII art997Not measuredOnly Mistral Medium 3.1 measured
LiveBench designNot measured60.7%Only Nemotron 3.5 Super VL measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
General benchmarks for Mistral Medium 3.1 and Nemotron 3.5 Super VL
BenchmarkMistral Medium 3.1Nemotron 3.5 Super VLLeader
LiveBench data analysisNot measured64.6%Only Nemotron 3.5 Super VL measured
IFBench39.8%Not measuredOnly Mistral Medium 3.1 measured
Long context reasoning (AA-LCR)42.7%69.0%Nemotron 3.5 Super VL
DTBench67.5%Not measuredOnly Mistral Medium 3.1 measured
LMCA19.1%Not measuredOnly Mistral Medium 3.1 measured
LMArena Text1424Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: instruction following1398Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: creative writing1391Not measuredOnly Mistral Medium 3.1 measured
LMArena Vision1172Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: English1438Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: non-English1408Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: Chinese1447Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: German1432Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: Japanese1379Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: Korean1380Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: French1460Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: Spanish1433Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: Russian1411Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: Polish1430Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: multi-turn1418Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: longer queries1406Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: writing and language1395Not measuredOnly Mistral Medium 3.1 measured
LMArena Text: entertainment and media1390Not measuredOnly Mistral Medium 3.1 measured
LMArena Vision: English1179Not measuredOnly Mistral Medium 3.1 measured
LMArena Vision: Chinese1190Not measuredOnly Mistral Medium 3.1 measured
LMArena Vision: reading text1175Not measuredOnly Mistral Medium 3.1 measured
LMArena Vision: humor1168Not measuredOnly Mistral Medium 3.1 measured
LMArena Vision: captioning1145Not measuredOnly Mistral Medium 3.1 measured
LMArena Vision: recognizing things1147Not measuredOnly Mistral Medium 3.1 measured
LMArena Vision: creative writing1200Not measuredOnly Mistral Medium 3.1 measured
Neither model has a result on the other 18 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Mistral Medium 3.1 and Nemotron 3.5 Super VL
BenchmarkMistral Medium 3.1Nemotron 3.5 Super VLLeader
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

Mistral Medium 3.1$0.0014Fits in one request
Nemotron 3.5 Super VL$0.0004Fits in one request

Nemotron 3.5 Super VL costs less

Repository review

50K input + 3K output tokens

Mistral Medium 3.1$0.026Fits in one request
Nemotron 3.5 Super VL$0.0090Fits in one request

Nemotron 3.5 Super VL costs less

Agent loop

200K input + 10K output tokens

Mistral Medium 3.1$0.100Exceeds the context window
Nemotron 3.5 Super VL$0.035Fits in one request

Nemotron 3.5 Super VL 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 Mistral Medium 3.1 and Nemotron 3.5 Super VL
SpecificationMistral Medium 3.1NVIDIA Nemotron 3.5 Super VL
MakerMistral AINVIDIA
WeightsNo public weights listedOpen weights
ReleasedAugust 13, 2025October 15, 2026
Context window131K1.05M
Input $/Mtok$0.40$0.15
Output $/Mtok$2.00$0.50
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Mistral Medium 3.1 better than Nemotron 3.5 Super VL for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Medium 3.1 for work measured by SciCode or AA Intelligence Index, where it leads.

Which is cheaper, Mistral Medium 3.1 or Nemotron 3.5 Super VL?

Mistral Medium 3.1 costs $0.40 per million input tokens and $2.00 per million output tokens; Nemotron 3.5 Super VL costs $0.15 and $0.50. Cost per solved task is known for neither of them.

Which has the bigger context window, Mistral Medium 3.1 or Nemotron 3.5 Super VL?

Nemotron 3.5 Super VL, with 1.05M tokens against 131K.

How many benchmarks is the Mistral Medium 3.1 vs Nemotron 3.5 Super VL comparison based on?

9 benchmarks have a result for both models as of October 23, 2026. Mistral Medium 3.1's coding index rests on 0 independent benchmarks and Nemotron 3.5 Super VL's on 1. Where only one model has a result, the page says so instead of naming a winner.

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