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Mistral Medium 3.5 vs Qwen3.5 397B A17B

Updated October 21, 2026. 57 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Mistral Medium 3.5 Replace Qwen3.5 397B A17B
Mistral Medium 3.5Mistral AI
Coding
9.2
Overall
14.2
Output $/M
$7.50
2
Qwen3.5 397B A17BQwen
Coding
28.6
Overall
21.4
Output $/M
$2.34
55
vs boards won of 57 shared

Decision reading

Pick Qwen3.5 397B A17B for coding: it scores 28.6 on the Vibe Coding Index against 9.2 for Mistral Medium 3.5.

Pick Mistral Medium 3.5 when what matters is ALE-Bench or LMArena Text: German, where it leads.

Model AMistral Medium 3.5Mistral AI9.2/1001 independent benchmark$6.02 per solved taskModel BQwen3.5 397B A17BQwen28.6/1001 independent benchmarkNo cost per solved task
Shared results
57
Mistral Medium 3.5 only
23
Qwen3.5 397B A17B only
8
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
Mistral Medium 3.5 9.2 from 1 benchmark, Qwen3.5 397B A17B 28.6 from 1 benchmark.
Widest category
Web design, 10.7 points apart on average over 7 shared benchmarks.
Widest single gap
Design Arena: SVG: Mistral Medium 3.5 949 against Qwen3.5 397B A17B 1139, 23.7 points apart on a 0 to 100 scale.
Output price
Mistral Medium 3.5 $7.50 against Qwen3.5 397B A17B $2.34 per million output tokens, 3.2x apart.
Context window
Both take 262K tokens.

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 3.5BarsQwen3.5 397B A17BTies
Coding17
Intelligence02
Agentic work01
Reasoning07
Math02
Knowledge04
Web design07
General125
All shared boards255
CodingLike-for-likeFour of these boards feed the Vibe Coding Index17
Coding benchmarks for Mistral Medium 3.5 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.5Qwen3.5 397B A17BLeader
LMArena WebDev12641400Qwen3.5 397B A17B
AA Coding Index46.9Not measuredOnly Mistral Medium 3.5 measured
SWE-bench Verified (Vals AI)66.4%Not measuredOnly Mistral Medium 3.5 measured
Vibe Code Bench (Vals AI)2.9%Not measuredOnly Mistral Medium 3.5 measured
Code migration (Vals AI)5.1%Not measuredOnly Mistral Medium 3.5 measured
Terminal-Bench 2.1 (Vals AI)39.0%Not measuredOnly Mistral Medium 3.5 measured
SciCode40.2%44.8%Qwen3.5 397B A17B
Terminal-Bench Hard (Artificial Analysis run)33.3%40.9%Qwen3.5 397B A17B
ALE-Bench764.0621.9Mistral Medium 3.5
LMArena Text: coding14601464Qwen3.5 397B A17B
LMArena Text: software and IT14581463Qwen3.5 397B A17B
LMArena WebDev: React12541388Qwen3.5 397B A17B
LMArena WebDev: HTML13081410Qwen3.5 397B A17B
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Mistral Medium 3.5 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.5Qwen3.5 397B A17BLeader
AA Intelligence Index14.221.4Qwen3.5 397B A17B
Epoch Capabilities Index141.4146.8Qwen3.5 397B A17B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Mistral Medium 3.5 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.5Qwen3.5 397B A17BLeader
AA Agentic Index7.0Not measuredOnly Mistral Medium 3.5 measured
Finance Agent (Vals AI)32.1%Not measuredOnly Mistral Medium 3.5 measured
Tax Agent Bench (Vals AI)4.5%Not measuredOnly Mistral Medium 3.5 measured
Legal Research Bench (Vals AI)9.1%Not measuredOnly Mistral Medium 3.5 measured
Excel Modeling Benchmark (Vals AI)11.2%Not measuredOnly Mistral Medium 3.5 measured
τ²-Bench Telecom94.2%95.6%Qwen3.5 397B A17B
APEX-AgentsNot measured24.9%Only Qwen3.5 397B A17B measured
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for Mistral Medium 3.5 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.5Qwen3.5 397B A17BLeader
GPQA Diamond34.9%Not measuredOnly Mistral Medium 3.5 measured
GPQA Diamond (Artificial Analysis run)74.8%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)13.8%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 prompts14361448Qwen3.5 397B A17B
LMArena Text: expert14321462Qwen3.5 397B A17B
LMArena Text: hard prompts in English14451450Qwen3.5 397B A17B
LMArena Vision: diagrams12541277Qwen3.5 397B A17B
LMArena Vision: homework12301292Qwen3.5 397B A17B
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Mistral Medium 3.5 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.5Qwen3.5 397B A17BLeader
OTIS Mock AIMENot measured88.9%Only Qwen3.5 397B A17B measured
FrontierMath, tiers 1 to 3Not measured31.2%Only Qwen3.5 397B A17B measured
ProofBench9.0%Not measuredOnly Mistral Medium 3.5 measured
LMArena Text: math14311453Qwen3.5 397B A17B
LMArena Text: mathematical work14401454Qwen3.5 397B A17B
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Mistral Medium 3.5 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.5Qwen3.5 397B A17BLeader
MMLU Pro75.3%Not measuredOnly Mistral Medium 3.5 measured
CaseLaw (Vals AI)44.2%Not measuredOnly Mistral Medium 3.5 measured
CorpFin (Vals AI)58.8%Not measuredOnly Mistral Medium 3.5 measured
TaxEval (Vals AI)68.0%Not measuredOnly Mistral Medium 3.5 measured
MortgageTax (Vals AI)28.9%Not measuredOnly Mistral Medium 3.5 measured
MedCode (Vals AI)33.8%Not measuredOnly Mistral Medium 3.5 measured
LMArena Text: science14281457Qwen3.5 397B A17B
LMArena Text: medicine and healthcare14091455Qwen3.5 397B A17B
LMArena Text: legal and government14371441Qwen3.5 397B A17B
LMArena Text: business and finance14261435Qwen3.5 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 3.5 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.5Qwen3.5 397B A17BLeader
Design Arena: websites11421191Qwen3.5 397B A17B
Design Arena: UI components10791163Qwen3.5 397B A17B
Design Arena: game dev10791141Qwen3.5 397B A17B
Design Arena: data viz11201175Qwen3.5 397B A17B
Design Arena: 3D10611161Qwen3.5 397B A17B
Design Arena: all code categories11221180Qwen3.5 397B A17B
Design Arena: SVG9491139Qwen3.5 397B A17B
Design Arena: ASCII art1062Not measuredOnly Mistral Medium 3.5 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index125
General benchmarks for Mistral Medium 3.5 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.5Qwen3.5 397B A17BLeader
SAGE (Vals AI)37.6%Not measuredOnly Mistral Medium 3.5 measured
MedScribe (Vals AI)67.7%Not measuredOnly Mistral Medium 3.5 measured
Vals Multimodal Index34.8%Not measuredOnly Mistral Medium 3.5 measured
IFBench68.8%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)69.3%77.3%Qwen3.5 397B A17B
DTBench75.5%87.5%Qwen3.5 397B A17B
LMCA26.1%37.9%Qwen3.5 397B A17B
Surface Evolver Bench26.9%Not measuredOnly Mistral Medium 3.5 measured
LMArena Text14211437Qwen3.5 397B A17B
LMArena Text: instruction following14151424Qwen3.5 397B A17B
LMArena Text: creative writing13741402Qwen3.5 397B A17B
LMArena Vision12221262Qwen3.5 397B A17B
LMArena Text: English14331443Qwen3.5 397B A17B
LMArena Text: non-English14041430Qwen3.5 397B A17B
LMArena Text: Chinese14441498Qwen3.5 397B A17B
LMArena Text: German14531445Mistral Medium 3.5
LMArena Text: JapaneseNot measured1429Only Qwen3.5 397B A17B measured
LMArena Text: Korean13841384Qwen3.5 397B A17B
LMArena Text: French14481462Qwen3.5 397B A17B
LMArena Text: Spanish14111441Qwen3.5 397B A17B
LMArena Text: Russian13951430Qwen3.5 397B A17B
LMArena Text: Polish14301447Qwen3.5 397B A17B
LMArena Text: multi-turn14231446Qwen3.5 397B A17B
LMArena Text: longer queries14151441Qwen3.5 397B A17B
LMArena Text: writing and language13841415Qwen3.5 397B A17B
LMArena Text: entertainment and media13821399Qwen3.5 397B A17B
LMArena Vision: English12401268Qwen3.5 397B A17B
LMArena Vision: Chinese12441314Qwen3.5 397B A17B
LMArena Vision: reading text12331271Qwen3.5 397B A17B
LMArena Vision: humor12161262Qwen3.5 397B A17B
LMArena Vision: captioningNot measured1230Only Qwen3.5 397B A17B measured
LMArena Vision: recognizing thingsNot measured1265Only Qwen3.5 397B A17B measured
LMArena Vision: creative writing11981260Qwen3.5 397B A17B
Neither model has a result on the other 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Mistral Medium 3.5 and Qwen3.5 397B A17B
BenchmarkMistral Medium 3.5Qwen3.5 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 3.5$0.0053Fits in one request
Qwen3.5 397B A17B$0.0016Fits in one request

Qwen3.5 397B A17B costs less

Repository review

50K input + 3K output tokens

Mistral Medium 3.5$0.098Fits in one request
Qwen3.5 397B A17B$0.027Fits in one request

Qwen3.5 397B A17B costs less

Agent loop

200K input + 10K output tokens

Mistral Medium 3.5$0.375Fits in one request
Qwen3.5 397B A17B$0.101Fits in one request

Qwen3.5 397B A17B 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 3.5 and Qwen3.5 397B A17B
SpecificationMistral Medium 3.5Qwen3.5 397B A17B
MakerMistral AIQwen
WeightsNo public weights listedOpen weights
ReleasedApril 30, 2026February 16, 2026
Context window262K262K
Input $/Mtok$1.50$0.39
Output $/Mtok$7.50$2.34
Coding index9.228.6
Independent measurements behind it11
Cost per solved task$6.02Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Mistral Medium 3.5 better than Qwen3.5 397B A17B for coding?

Pick Qwen3.5 397B A17B for coding: it scores 28.6 on the Vibe Coding Index against 9.2 for Mistral Medium 3.5. Pick Mistral Medium 3.5 when what matters is ALE-Bench or LMArena Text: German, where it leads.

Which is cheaper, Mistral Medium 3.5 or Qwen3.5 397B A17B?

Mistral Medium 3.5 costs $1.50 per million input tokens and $7.50 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for Mistral Medium 3.5 only.

Which has the bigger context window, Mistral Medium 3.5 or Qwen3.5 397B A17B?

Neither: both take 262K tokens.

How many benchmarks is the Mistral Medium 3.5 vs Qwen3.5 397B A17B comparison based on?

57 benchmarks have a result for both models as of October 21, 2026. Mistral Medium 3.5's coding index rests on 1 independent benchmark 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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