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Mistral Medium 3.5 vs Qwen3.7 Plus

Qwen3.7 Plus leads Mistral Medium 3.5 on coding, 33.2 to 25.5 on the Vibe Coding Index, and Qwen3.7 Plus costs 5.9x less per output token ($1.28 vs $7.50 per million). Updated October 21, 2026; 66 benchmarks have a result for both.

Replace Mistral Medium 3.5 Replace Qwen3.7 Plus
Mistral Medium 3.5Mistral AI
Coding
25.5
Overall
14.2
Output $/M
$7.50
1
Qwen3.7 PlusQwen
Coding
33.2
Overall
25.2
Output $/M
$1.28
65
vs boards won of 66 shared

Mistral Medium 3.5 or Qwen3.7 Plus: which to pick

Pick Qwen3.7 Plus for coding: it scores 33.2 on the Vibe Coding Index against 25.5 for Mistral Medium 3.5, though on 2 benchmarks against 3.

Pick Mistral Medium 3.5 for work measured by τ²-Bench Telecom, where it leads.

Model AMistral Medium 3.5Mistral AI25.5/1003 independent benchmarks$6.02 per solved taskModel BQwen3.7 PlusQwen33.2/1002 independent benchmarksNo cost per solved task
Shared results
66
Mistral Medium 3.5 only
14
Qwen3.7 Plus 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 3.5 25.5 from 3 benchmarks, Qwen3.7 Plus 33.2 from 2 benchmarks.
Widest category
Web design, 20.5 points apart on average over 8 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): Mistral Medium 3.5 2.9% against Qwen3.7 Plus 46.4%, 43.5 points apart on a 0 to 100 scale.
Output price
Mistral Medium 3.5 $7.50 against Qwen3.7 Plus $1.28 per million output tokens, 5.9x apart.
Context window
Mistral Medium 3.5 takes 262K tokens, Qwen3.7 Plus 1M.

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 3.5BarsQwen3.7 PlusTies
Coding08
Intelligence02
Agentic work15
Reasoning07
Math02
Knowledge05
Web design08
General028
All shared boards165
CodingLike-for-likeSix of these boards feed the Vibe Coding Index08
Coding benchmarks for Mistral Medium 3.5 and Qwen3.7 Plus
BenchmarkMistral Medium 3.5Qwen3.7 PlusLeader
LMArena WebDev1264Not measuredOnly Mistral Medium 3.5 measured
AA Coding Index46.955.9Qwen3.7 Plus
SWE-bench Verified (Vals AI)66.4%Not measuredOnly Mistral Medium 3.5 measured
Vibe Code Bench (Vals AI)2.9%46.4%Qwen3.7 Plus
Code migration (Vals AI)5.1%12.9%Qwen3.7 Plus
Terminal-Bench 2.1 (Vals AI)39.0%52.8%Qwen3.7 Plus
SkillsBench (Vals AI)Not measured54.3%Only Qwen3.7 Plus measured
SciCode40.2%46.1%Qwen3.7 Plus
Terminal-Bench Hard (Artificial Analysis run)33.3%47.0%Qwen3.7 Plus
FrontierCodeNot measured10.2%Only Qwen3.7 Plus measured
ALE-Bench764.0Not measuredOnly Mistral Medium 3.5 measured
LMArena Text: coding14601473Qwen3.7 Plus
LMArena Text: software and IT14581473Qwen3.7 Plus
LMArena WebDev: React1254Not measuredOnly Mistral Medium 3.5 measured
LMArena WebDev: HTML1308Not measuredOnly Mistral Medium 3.5 measured
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Mistral Medium 3.5 and Qwen3.7 Plus
BenchmarkMistral Medium 3.5Qwen3.7 PlusLeader
AA Intelligence Index14.225.2Qwen3.7 Plus
Epoch Capabilities Index141.4147.0Qwen3.7 Plus
Agentic workLike-for-likePublic boards, shown as published, kept out of the index15
Agentic work benchmarks for Mistral Medium 3.5 and Qwen3.7 Plus
BenchmarkMistral Medium 3.5Qwen3.7 PlusLeader
AA Agentic Index7.017.5Qwen3.7 Plus
Finance Agent (Vals AI)32.1%38.2%Qwen3.7 Plus
Tax Agent Bench (Vals AI)4.5%10.5%Qwen3.7 Plus
Legal Research Bench (Vals AI)9.1%16.4%Qwen3.7 Plus
Excel Modeling Benchmark (Vals AI)11.2%49.3%Qwen3.7 Plus
τ²-Bench Telecom94.2%93.0%Mistral Medium 3.5
OSWorld 2.0Not measured2.8%Only Qwen3.7 Plus 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 Qwen3.7 Plus
BenchmarkMistral Medium 3.5Qwen3.7 PlusLeader
GPQA Diamond34.9%Not measuredOnly Mistral Medium 3.5 measured
GPQA Diamond (Artificial Analysis run)74.8%90.0%Qwen3.7 Plus
Humanity's Last Exam (Artificial Analysis run)13.8%35.6%Qwen3.7 Plus
GPQA Diamond (Epoch AI run)Not measured87.9%Only Qwen3.7 Plus measured
Mystery game puzzlesNot measured17.0%Only Qwen3.7 Plus measured
LMArena Text: hard prompts14361460Qwen3.7 Plus
LMArena Text: expert14321466Qwen3.7 Plus
LMArena Text: hard prompts in English14451460Qwen3.7 Plus
LMArena Vision: diagrams12541292Qwen3.7 Plus
LMArena Vision: homework12301295Qwen3.7 Plus
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 Qwen3.7 Plus
BenchmarkMistral Medium 3.5Qwen3.7 PlusLeader
OTIS Mock AIMENot measured93.3%Only Qwen3.7 Plus measured
FrontierMath, tiers 1 to 3Not measured34.4%Only Qwen3.7 Plus measured
ProofBench9.0%Not measuredOnly Mistral Medium 3.5 measured
LMArena Text: math14311467Qwen3.7 Plus
LMArena Text: mathematical work14401470Qwen3.7 Plus
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for Mistral Medium 3.5 and Qwen3.7 Plus
BenchmarkMistral Medium 3.5Qwen3.7 PlusLeader
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%66.2%Qwen3.7 Plus
MedCode (Vals AI)33.8%Not measuredOnly Mistral Medium 3.5 measured
LMArena Text: science14281464Qwen3.7 Plus
LMArena Text: medicine and healthcare14091464Qwen3.7 Plus
LMArena Text: legal and government14371451Qwen3.7 Plus
LMArena Text: business and finance14261435Qwen3.7 Plus
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for Mistral Medium 3.5 and Qwen3.7 Plus
BenchmarkMistral Medium 3.5Qwen3.7 PlusLeader
Design Arena: websites11421269Qwen3.7 Plus
Design Arena: UI components10791261Qwen3.7 Plus
Design Arena: game dev10791262Qwen3.7 Plus
Design Arena: data viz11201276Qwen3.7 Plus
Design Arena: 3D10611239Qwen3.7 Plus
Design Arena: all code categories11211269Qwen3.7 Plus
Design Arena: SVG9501208Qwen3.7 Plus
Design Arena: ASCII art10631143Qwen3.7 Plus
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index028
General benchmarks for Mistral Medium 3.5 and Qwen3.7 Plus
BenchmarkMistral Medium 3.5Qwen3.7 PlusLeader
SAGE (Vals AI)37.6%39.3%Qwen3.7 Plus
MedScribe (Vals AI)67.7%Not measuredOnly Mistral Medium 3.5 measured
Vals Multimodal Index34.8%53.9%Qwen3.7 Plus
IFBench68.8%78.0%Qwen3.7 Plus
Long context reasoning (AA-LCR)69.3%73.0%Qwen3.7 Plus
DTBench75.5%84.0%Qwen3.7 Plus
LMCA26.1%37.6%Qwen3.7 Plus
Surface Evolver Bench26.9%Not measuredOnly Mistral Medium 3.5 measured
LMArena Text14211454Qwen3.7 Plus
LMArena Text: instruction following14151439Qwen3.7 Plus
LMArena Text: creative writing13741438Qwen3.7 Plus
LMArena Vision12221278Qwen3.7 Plus
LMArena Text: English14331458Qwen3.7 Plus
LMArena Text: non-English14041445Qwen3.7 Plus
LMArena Text: Chinese14441509Qwen3.7 Plus
LMArena Text: German14531471Qwen3.7 Plus
LMArena Text: JapaneseNot measured1412Only Qwen3.7 Plus measured
LMArena Text: Korean13841413Qwen3.7 Plus
LMArena Text: French14481472Qwen3.7 Plus
LMArena Text: Spanish14111457Qwen3.7 Plus
LMArena Text: Russian13951457Qwen3.7 Plus
LMArena Text: Polish14301449Qwen3.7 Plus
LMArena Text: multi-turn14231459Qwen3.7 Plus
LMArena Text: longer queries14151454Qwen3.7 Plus
LMArena Text: writing and language13841437Qwen3.7 Plus
LMArena Text: entertainment and media13821422Qwen3.7 Plus
LMArena Vision: English12401280Qwen3.7 Plus
LMArena Vision: Chinese12441332Qwen3.7 Plus
LMArena Vision: reading text12331288Qwen3.7 Plus
LMArena Vision: humor12161261Qwen3.7 Plus
LMArena Vision: creative writing11981258Qwen3.7 Plus
LMArena DocumentNot measured1444Only Qwen3.7 Plus 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 Mistral Medium 3.5 and Qwen3.7 Plus
BenchmarkMistral Medium 3.5Qwen3.7 PlusLeader
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
Qwen3.7 Plus$0.0010Fits in one request

Qwen3.7 Plus costs less

Repository review

50K input + 3K output tokens

Mistral Medium 3.5$0.098Fits in one request
Qwen3.7 Plus$0.020Fits in one request

Qwen3.7 Plus costs less

Agent loop

200K input + 10K output tokens

Mistral Medium 3.5$0.375Fits in one request
Qwen3.7 Plus$0.077Fits in one request

Qwen3.7 Plus 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 Qwen3.7 Plus
SpecificationMistral Medium 3.5Qwen3.7 Plus
MakerMistral AIQwen
WeightsNo public weights listedNo public weights listed
ReleasedApril 30, 2026June 3, 2026
Context window262K1M
Input $/Mtok$1.50$0.32
Output $/Mtok$7.50$1.28
Coding index25.533.2
Independent measurements behind it32
Cost per solved task$6.02Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Mistral Medium 3.5 better than Qwen3.7 Plus for coding?

Pick Qwen3.7 Plus for coding: it scores 33.2 on the Vibe Coding Index against 25.5 for Mistral Medium 3.5, though on 2 benchmarks against 3. Pick Mistral Medium 3.5 for work measured by τ²-Bench Telecom, where it leads.

Which is cheaper, Mistral Medium 3.5 or Qwen3.7 Plus?

Mistral Medium 3.5 costs $1.50 per million input tokens and $7.50 per million output tokens; Qwen3.7 Plus costs $0.32 and $1.28. Cost per solved task is known for Mistral Medium 3.5 only.

Which has the bigger context window, Mistral Medium 3.5 or Qwen3.7 Plus?

Qwen3.7 Plus, with 1M tokens against 262K.

How many benchmarks is the Mistral Medium 3.5 vs Qwen3.7 Plus comparison based on?

66 benchmarks have a result for both models as of October 21, 2026. Mistral Medium 3.5's coding index rests on 3 independent benchmarks and Qwen3.7 Plus's on 2. Where only one model has a result, the page says so instead of naming a winner.

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