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Mistral Medium 3.1 vs Qwen3.5 397B A17B

Qwen3.5 397B A17B leads Mistral Medium 3.1 on the Artificial Analysis Intelligence Index, 21.4 to 9.2, at about the same output price ($2.34 vs $2.00 per million). Updated October 22, 2026; 56 benchmarks have a result for both.

Replace Mistral Medium 3.1 Replace Qwen3.5 397B A17B
Mistral Medium 3.1Mistral AI
Coding
No data
Overall
No data
Output $/M
$2.00
0
Qwen3.5 397B A17BQwen
Coding
No data
Overall
57.1
Output $/M
$2.34
56
vs boards won of 56 shared

Mistral Medium 3.1 or Qwen3.5 397B A17B: which to pick

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

Pick Mistral Medium 3.1 for output tokens 1.2x cheaper ($2.00 vs $2.34 per million) and work measured by SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model AMistral Medium 3.1Mistral AINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5 397B A17BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
56
Mistral Medium 3.1 only
18
Qwen3.5 397B A17B only
9
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
Mistral Medium 3.1 not benchmarked, Qwen3.5 397B A17B not benchmarked.
Widest category
Agentic work, 55.0 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: Mistral Medium 3.1 40.6% against Qwen3.5 397B A17B 95.6%, 55.0 points apart on a 0 to 100 scale.
Output price
Mistral Medium 3.1 $2.00 against Qwen3.5 397B A17B $2.34 per million output tokens, 1.2x apart.
Context window
Mistral Medium 3.1 takes 131K tokens, Qwen3.5 397B A17B 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
CategoryMistral Medium 3.1BarsQwen3.5 397B A17BTies
Coding05
Intelligence01
Agentic work01
Reasoning07
Math02
Knowledge04
Web design07
General029
All shared boards056
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
Coding benchmarks for Mistral Medium 3.1 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.1Qwen3.5 397B A17BLeader
LMArena WebDevNot measured1400Only Qwen3.5 397B A17B 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%44.8%Qwen3.5 397B A17B
Terminal-Bench Hard (Artificial Analysis run)10.6%40.9%Qwen3.5 397B A17B
ALE-Bench210.2621.9Qwen3.5 397B A17B
LMArena Text: coding14331464Qwen3.5 397B A17B
LMArena Text: software and IT14361463Qwen3.5 397B A17B
LMArena WebDev: ReactNot measured1388Only Qwen3.5 397B A17B measured
LMArena WebDev: HTMLNot measured1410Only Qwen3.5 397B A17B measured
Neither model has a result on the other 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Mistral Medium 3.1 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.1Qwen3.5 397B A17BLeader
AA Intelligence Index9.221.4Qwen3.5 397B A17B
Epoch Capabilities IndexNot measured146.8Only Qwen3.5 397B A17B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Mistral Medium 3.1 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.1Qwen3.5 397B A17BLeader
AA Agentic Index1.9Not measuredOnly Mistral Medium 3.1 measured
τ²-Bench Telecom40.6%95.6%Qwen3.5 397B A17B
APEX-AgentsNot measured24.9%Only Qwen3.5 397B A17B measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for Mistral Medium 3.1 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.1Qwen3.5 397B A17BLeader
MMMU Pro (Vals AI)63.0%Not measuredOnly Mistral Medium 3.1 measured
GPQA Diamond (Artificial Analysis run)58.8%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)4.7%29.0%Qwen3.5 397B A17B
GPQA Diamond (Epoch AI run)Not measured86.4%Only Qwen3.5 397B A17B measured
Mystery game puzzlesNot measured18.0%Only Qwen3.5 397B A17B measured
LMArena Text: hard prompts14261448Qwen3.5 397B A17B
LMArena Text: expert14081462Qwen3.5 397B A17B
LMArena Text: hard prompts in English14351450Qwen3.5 397B A17B
LMArena Vision: diagrams11751277Qwen3.5 397B A17B
LMArena Vision: homework11651292Qwen3.5 397B A17B
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Mistral Medium 3.1 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.1Qwen3.5 397B A17BLeader
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
OTIS Mock AIMENot measured88.9%Only Qwen3.5 397B A17B measured
FrontierMath, tiers 1 to 3Not measured31.2%Only Qwen3.5 397B A17B measured
LMArena Text: math14081453Qwen3.5 397B A17B
LMArena Text: mathematical work14121454Qwen3.5 397B A17B
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Mistral Medium 3.1 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.1Qwen3.5 397B A17BLeader
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: science14441457Qwen3.5 397B A17B
LMArena Text: medicine and healthcare14491455Qwen3.5 397B A17B
LMArena Text: legal and government14391441Qwen3.5 397B A17B
LMArena Text: business and finance14181435Qwen3.5 397B A17B
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for Mistral Medium 3.1 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.1Qwen3.5 397B A17BLeader
Design Arena: websites11321190Qwen3.5 397B A17B
Design Arena: UI components10971160Qwen3.5 397B A17B
Design Arena: game dev10731140Qwen3.5 397B A17B
Design Arena: data viz11441175Qwen3.5 397B A17B
Design Arena: 3D10811160Qwen3.5 397B A17B
Design Arena: all code categories11201179Qwen3.5 397B A17B
Design Arena: SVG9921139Qwen3.5 397B A17B
Design Arena: ASCII art997Not measuredOnly 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 index029
General benchmarks for Mistral Medium 3.1 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.1Qwen3.5 397B A17BLeader
IFBench39.8%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)42.7%77.3%Qwen3.5 397B A17B
DTBench67.5%87.5%Qwen3.5 397B A17B
LMCA19.1%37.9%Qwen3.5 397B A17B
LMArena Text14241437Qwen3.5 397B A17B
LMArena Text: instruction following13981424Qwen3.5 397B A17B
LMArena Text: creative writing13911402Qwen3.5 397B A17B
LMArena Vision11721262Qwen3.5 397B A17B
LMArena Text: English14381443Qwen3.5 397B A17B
LMArena Text: non-English14081430Qwen3.5 397B A17B
LMArena Text: Chinese14471498Qwen3.5 397B A17B
LMArena Text: German14321445Qwen3.5 397B A17B
LMArena Text: Japanese13791429Qwen3.5 397B A17B
LMArena Text: Korean13801384Qwen3.5 397B A17B
LMArena Text: French14601462Qwen3.5 397B A17B
LMArena Text: Spanish14331441Qwen3.5 397B A17B
LMArena Text: Russian14111430Qwen3.5 397B A17B
LMArena Text: Polish14301447Qwen3.5 397B A17B
LMArena Text: multi-turn14181446Qwen3.5 397B A17B
LMArena Text: longer queries14061441Qwen3.5 397B A17B
LMArena Text: writing and language13951415Qwen3.5 397B A17B
LMArena Text: entertainment and media13901399Qwen3.5 397B A17B
LMArena Vision: English11791268Qwen3.5 397B A17B
LMArena Vision: Chinese11901314Qwen3.5 397B A17B
LMArena Vision: reading text11751271Qwen3.5 397B A17B
LMArena Vision: humor11681262Qwen3.5 397B A17B
LMArena Vision: captioning11451230Qwen3.5 397B A17B
LMArena Vision: recognizing things11471265Qwen3.5 397B A17B
LMArena Vision: creative writing12001260Qwen3.5 397B A17B
Neither model has a result on the other 17 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 Qwen3.5 397B A17B
BenchmarkMistral Medium 3.1Qwen3.5 397B A17BLeader
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
Qwen3.5 397B A17B$0.0016Fits in one request

Mistral Medium 3.1 costs less

Repository review

50K input + 3K output tokens

Mistral Medium 3.1$0.026Fits in one request
Qwen3.5 397B A17B$0.027Fits in one request

Mistral Medium 3.1 costs less

Agent loop

200K input + 10K output tokens

Mistral Medium 3.1$0.100Exceeds the context window
Qwen3.5 397B A17B$0.101Fits in one request

Mistral Medium 3.1 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 Qwen3.5 397B A17B
SpecificationMistral Medium 3.1Qwen3.5 397B A17B
MakerMistral AIQwen
WeightsNo public weights listedOpen weights
ReleasedAugust 13, 2025February 16, 2026
Context window131K262K
Input $/Mtok$0.40$0.39
Output $/Mtok$2.00$2.34
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 Qwen3.5 397B A17B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Medium 3.1 for output tokens 1.2x cheaper ($2.00 vs $2.34 per million) and work measured by SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, Mistral Medium 3.1 or Qwen3.5 397B A17B?

Mistral Medium 3.1 costs $0.40 per million input tokens and $2.00 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for neither of them.

Which has the bigger context window, Mistral Medium 3.1 or Qwen3.5 397B A17B?

Qwen3.5 397B A17B, with 262K tokens against 131K.

How many benchmarks is the Mistral Medium 3.1 vs Qwen3.5 397B A17B comparison based on?

56 benchmarks have a result for both models as of October 22, 2026. Mistral Medium 3.1's coding index rests on 0 independent benchmarks and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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