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Mistral Medium 3.5 vs Qwen3 Next 80B A3B Thinking

Mistral Medium 3.5 leads Qwen3 Next 80B A3B Thinking on the Artificial Analysis Intelligence Index, 14.2 to 11.2, and Qwen3 Next 80B A3B Thinking costs 6.3x less per output token ($1.20 vs $7.50 per million). Updated October 22, 2026; 29 benchmarks have a result for both.

Mistral Medium 3.5Mistral AI
Coding
25.5
Overall
37.3
Output $/M
$7.50
29
Qwen3 Next 80B A3B ThinkingQwen
Coding
No data
Overall
No data
Output $/M
$1.20
0
vs boards won of 29 shared

Mistral Medium 3.5 or Qwen3 Next 80B A3B Thinking: which to pick

Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; Qwen3 Next 80B A3B Thinking has none, so the two cannot be ranked on coding.

Pick Qwen3 Next 80B A3B Thinking for output tokens 6.3x cheaper ($1.20 vs $7.50 per million).

Shared results
29
Mistral Medium 3.5 only
52
Qwen3 Next 80B A3B Thinking only
1
Like-for-like categories
6 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 Next 80B A3B Thinking not benchmarked.
Widest category
Coding, 17.3 points apart on average over 3 shared benchmarks.
Widest single gap
AA Coding Index: Mistral Medium 3.5 46.9 against Qwen3 Next 80B A3B Thinking 17.4, 29.5 points apart on a 0 to 100 scale.
Output price
Mistral Medium 3.5 $7.50 against Qwen3 Next 80B A3B Thinking $1.20 per million output tokens, 6.3x 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 Next 80B A3B ThinkingTies
Coding30
Intelligence10
Reasoning30
Math20
Knowledge40
General160
All shared boards290
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for Mistral Medium 3.5 and Qwen3 Next 80B A3B Thinking
BenchmarkMistral Medium 3.5Qwen3 Next 80B A3B ThinkingLeader
LMArena WebDev1264Not measuredOnly Mistral Medium 3.5 measured
AA Coding Index46.917.4Mistral Medium 3.5
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%Not measuredOnly Mistral Medium 3.5 measured
Terminal-Bench Hard (Artificial Analysis run)33.3%Not measuredOnly Mistral Medium 3.5 measured
ALE-Bench764.0Not measuredOnly Mistral Medium 3.5 measured
LMArena Text: coding14601392Mistral Medium 3.5
LMArena Text: software and IT14581393Mistral Medium 3.5
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 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Mistral Medium 3.5 and Qwen3 Next 80B A3B Thinking
BenchmarkMistral Medium 3.5Qwen3 Next 80B A3B ThinkingLeader
AA Intelligence Index14.211.2Mistral Medium 3.5
Epoch Capabilities Index141.4Not measuredOnly Mistral Medium 3.5 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Agentic work benchmarks for Mistral Medium 3.5 and Qwen3 Next 80B A3B Thinking
BenchmarkMistral Medium 3.5Qwen3 Next 80B A3B ThinkingLeader
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%Not measuredOnly Mistral Medium 3.5 measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
Reasoning benchmarks for Mistral Medium 3.5 and Qwen3 Next 80B A3B Thinking
BenchmarkMistral Medium 3.5Qwen3 Next 80B A3B ThinkingLeader
GPQA Diamond34.9%Not measuredOnly Mistral Medium 3.5 measured
GPQA Diamond (Artificial Analysis run)74.8%Not measuredOnly Mistral Medium 3.5 measured
Humanity's Last Exam (Artificial Analysis run)13.8%Not measuredOnly Mistral Medium 3.5 measured
LMArena Text: hard prompts14361371Mistral Medium 3.5
LMArena Text: expert14321376Mistral Medium 3.5
LMArena Text: hard prompts in English14451384Mistral Medium 3.5
LMArena Vision: diagrams1254Not measuredOnly Mistral Medium 3.5 measured
LMArena Vision: homework1230Not measuredOnly Mistral Medium 3.5 measured
Kagi LLM Benchmark41.4%Not measuredOnly Mistral Medium 3.5 measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for Mistral Medium 3.5 and Qwen3 Next 80B A3B Thinking
BenchmarkMistral Medium 3.5Qwen3 Next 80B A3B ThinkingLeader
ProofBench9.0%Not measuredOnly Mistral Medium 3.5 measured
LMArena Text: math14311399Mistral Medium 3.5
LMArena Text: mathematical work14401390Mistral Medium 3.5
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for Mistral Medium 3.5 and Qwen3 Next 80B A3B Thinking
BenchmarkMistral Medium 3.5Qwen3 Next 80B A3B ThinkingLeader
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: science14281384Mistral Medium 3.5
LMArena Text: medicine and healthcare14091392Mistral Medium 3.5
LMArena Text: legal and government14371364Mistral Medium 3.5
LMArena Text: business and finance14261360Mistral Medium 3.5
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Mistral Medium 3.5 and Qwen3 Next 80B A3B Thinking
BenchmarkMistral Medium 3.5Qwen3 Next 80B A3B ThinkingLeader
Design Arena: websites1142Not measuredOnly Mistral Medium 3.5 measured
Design Arena: UI components1076Not measuredOnly Mistral Medium 3.5 measured
Design Arena: game dev1079Not measuredOnly Mistral Medium 3.5 measured
Design Arena: data viz1120Not measuredOnly Mistral Medium 3.5 measured
Design Arena: 3D1060Not measuredOnly Mistral Medium 3.5 measured
Design Arena: all code categories1121Not measuredOnly Mistral Medium 3.5 measured
Design Arena: SVG949Not measuredOnly Mistral Medium 3.5 measured
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 index160
General benchmarks for Mistral Medium 3.5 and Qwen3 Next 80B A3B Thinking
BenchmarkMistral Medium 3.5Qwen3 Next 80B A3B ThinkingLeader
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%Not measuredOnly Mistral Medium 3.5 measured
Long context reasoning (AA-LCR)69.3%Not measuredOnly Mistral Medium 3.5 measured
DTBench75.5%Not measuredOnly Mistral Medium 3.5 measured
LMCA26.1%Not measuredOnly Mistral Medium 3.5 measured
Surface Evolver Bench26.9%Not measuredOnly Mistral Medium 3.5 measured
LMArena Text14211368Mistral Medium 3.5
LMArena Text: instruction following14151344Mistral Medium 3.5
LMArena Text: creative writing13741314Mistral Medium 3.5
LMArena Vision1222Not measuredOnly Mistral Medium 3.5 measured
LMArena Text: English14331392Mistral Medium 3.5
LMArena Text: non-English14041342Mistral Medium 3.5
LMArena Text: Chinese14441407Mistral Medium 3.5
LMArena Text: German14531356Mistral Medium 3.5
LMArena Text: JapaneseNot measured1280Only Qwen3 Next 80B A3B Thinking measured
LMArena Text: Korean13841310Mistral Medium 3.5
LMArena Text: French14481351Mistral Medium 3.5
LMArena Text: Spanish14111358Mistral Medium 3.5
LMArena Text: Russian13951337Mistral Medium 3.5
LMArena Text: Polish14301345Mistral Medium 3.5
LMArena Text: multi-turn14231346Mistral Medium 3.5
LMArena Text: longer queries14151353Mistral Medium 3.5
LMArena Text: writing and language13841330Mistral Medium 3.5
LMArena Text: entertainment and media13821312Mistral Medium 3.5
LMArena Vision: English1240Not measuredOnly Mistral Medium 3.5 measured
LMArena Vision: Chinese1244Not measuredOnly Mistral Medium 3.5 measured
LMArena Vision: reading text1233Not measuredOnly Mistral Medium 3.5 measured
LMArena Vision: humor1216Not measuredOnly Mistral Medium 3.5 measured
LMArena Vision: creative writing1198Not measuredOnly Mistral Medium 3.5 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 Mistral Medium 3.5 and Qwen3 Next 80B A3B Thinking
BenchmarkMistral Medium 3.5Qwen3 Next 80B A3B ThinkingLeader
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 Next 80B A3B Thinking$0.0008Fits in one request

Qwen3 Next 80B A3B Thinking costs less

Repository review

50K input + 3K output tokens

Mistral Medium 3.5$0.098Fits in one request
Qwen3 Next 80B A3B Thinking$0.011Fits in one request

Qwen3 Next 80B A3B Thinking costs less

Agent loop

200K input + 10K output tokens

Mistral Medium 3.5$0.375Fits in one request
Qwen3 Next 80B A3B Thinking$0.042Fits in one request

Qwen3 Next 80B A3B Thinking 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 Next 80B A3B Thinking
SpecificationMistral Medium 3.5Qwen3 Next 80B A3B Thinking
MakerMistral AIQwen
WeightsNo public weights listedOpen weights
ReleasedApril 30, 2026September 11, 2025
Context window262K262K
Input $/Mtok$1.50$0.15
Output $/Mtok$7.50$1.20
Coding index25.5Not benchmarked
Independent measurements behind it30
Cost per solved task$6.02Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Mistral Medium 3.5 better than Qwen3 Next 80B A3B Thinking for coding?

Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; Qwen3 Next 80B A3B Thinking has none, so the two cannot be ranked on coding. Pick Qwen3 Next 80B A3B Thinking for output tokens 6.3x cheaper ($1.20 vs $7.50 per million).

Which is cheaper, Mistral Medium 3.5 or Qwen3 Next 80B A3B Thinking?

Mistral Medium 3.5 costs $1.50 per million input tokens and $7.50 per million output tokens; Qwen3 Next 80B A3B Thinking costs $0.15 and $1.20. Cost per solved task is known for Mistral Medium 3.5 only.

Which has the bigger context window, Mistral Medium 3.5 or Qwen3 Next 80B A3B Thinking?

Neither: both take 262K tokens.

How many benchmarks is the Mistral Medium 3.5 vs Qwen3 Next 80B A3B Thinking comparison based on?

29 benchmarks have a result for both models as of October 22, 2026. Mistral Medium 3.5's coding index rests on 3 independent benchmarks and Qwen3 Next 80B A3B Thinking's on 0. Where only one model has a result, the page says so instead of naming a winner.

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