Enter opens the highlighted result. Esc closes.

Mistral Large vs Qwen3.8 27B

Qwen3.8 27B leads Mistral Large on the Artificial Analysis Intelligence Index, 33.7 to 5.8, and Qwen3.8 27B costs 2.4x less per output token ($2.55 vs $6.00 per million). Updated October 22, 2026; 37 benchmarks have a result for both.

Replace Mistral Large Replace Qwen3.8 27B
Mistral LargeMistral AI
Coding
No data
Overall
No data
Output $/M
$6.00
0
Qwen3.8 27BQwen
Coding
74.2
Overall
59.6
Output $/M
$2.55
37
vs boards won of 37 shared

Mistral Large or Qwen3.8 27B: which to pick

Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; Mistral Large has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Model AMistral LargeMistral AINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.8 27BQwen74.2/1004 independent benchmarks$1.26 per solved task
Shared results
37
Mistral Large only
7
Qwen3.8 27B only
48
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 Large not benchmarked, Qwen3.8 27B 74.2 from 4 benchmarks.
Widest category
Reasoning, 39.7 points apart on average over 6 shared benchmarks.
Widest single gap
GPQA Diamond (Artificial Analysis run): Mistral Large 35.1% against Qwen3.8 27B 90.5%, 55.4 points apart on a 0 to 100 scale.
Output price
Mistral Large $6.00 against Qwen3.8 27B $2.55 per million output tokens, 2.4x apart.
Context window
Mistral Large takes 128K tokens, Qwen3.8 27B 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 LargeBarsQwen3.8 27BTies
Coding03
Intelligence02
Reasoning06
Math02
Knowledge06
General018
All shared boards037
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for Mistral Large and Qwen3.8 27B
BenchmarkMistral LargeQwen3.8 27BLeader
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDevNot measured1593Only Qwen3.8 27B measured
AA Coding IndexNot measured68.1Only Qwen3.8 27B measured
LiveCodeBench (Vals AI)37.1%84.0%Qwen3.8 27B
SWE-bench Verified (Vals AI)Not measured86.0%Only Qwen3.8 27B measured
Vibe Code Bench (Vals AI)Not measured64.8%Only Qwen3.8 27B measured
Code migration (Vals AI)Not measured14.2%Only Qwen3.8 27B measured
ProgramBench (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
IOI (Vals AI)Not measured39.1%Only Qwen3.8 27B measured
Terminal-Bench 2.1 (Vals AI)Not measured58.4%Only Qwen3.8 27B measured
SkillsBench (Vals AI)Not measured38.1%Only Qwen3.8 27B measured
CyberBench (Vals AI)Not measured67.8%Only Qwen3.8 27B measured
LiveCodeBench (Artificial Analysis run)17.8%Not measuredOnly Mistral Large measured
SciCodeNot measured46.6%Only Qwen3.8 27B measured
LMArena Text: coding12751483Qwen3.8 27B
LMArena Text: software and IT12741478Qwen3.8 27B
LMArena WebDev: ReactNot measured1602Only Qwen3.8 27B measured
LMArena WebDev: HTMLNot measured1570Only Qwen3.8 27B measured
LMArena WebDev: from an imageNot measured1577Only Qwen3.8 27B measured
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Mistral Large and Qwen3.8 27B
BenchmarkMistral LargeQwen3.8 27BLeader
AA Intelligence Index5.833.7Qwen3.8 27B
Epoch Capabilities Index122.1151.7Qwen3.8 27B
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Agentic work benchmarks for Mistral Large and Qwen3.8 27B
BenchmarkMistral LargeQwen3.8 27BLeader
AA Agentic IndexNot measured45.8Only Qwen3.8 27B measured
LiveBench researchNot measured59.4%Only Qwen3.8 27B measured
LiveBench browserNot measured32.0%Only Qwen3.8 27B measured
LiveBench long agenticNot measured27.9%Only Qwen3.8 27B measured
Finance Agent (Vals AI)Not measured48.5%Only Qwen3.8 27B measured
Tax Agent Bench (Vals AI)Not measured30.9%Only Qwen3.8 27B measured
Legal Research Bench (Vals AI)Not measured36.1%Only Qwen3.8 27B measured
Excel Modeling Benchmark (Vals AI)Not measured59.7%Only Qwen3.8 27B measured
Terminal-Bench-Science (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
Neither model has a result on the other 31 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for Mistral Large and Qwen3.8 27B
BenchmarkMistral LargeQwen3.8 27BLeader
GPQA Diamond47.7%88.9%Qwen3.8 27B
LiveBench reasoningNot measured80.0%Only Qwen3.8 27B measured
MMMU Pro (Vals AI)Not measured83.9%Only Qwen3.8 27B measured
GPQA Diamond (Artificial Analysis run)35.1%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)3.5%33.9%Qwen3.8 27B
ARC-AGINot measured87.5%Only Qwen3.8 27B measured
ARC-AGI-2Not measured42.4%Only Qwen3.8 27B measured
LMArena Text: hard prompts12571459Qwen3.8 27B
LMArena Text: expert12081479Qwen3.8 27B
LMArena Text: hard prompts in English12681461Qwen3.8 27B
LMArena Vision: diagramsNot measured1285Only Qwen3.8 27B measured
LMArena Vision: homeworkNot measured1295Only Qwen3.8 27B measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Mistral Large and Qwen3.8 27B
BenchmarkMistral LargeQwen3.8 27BLeader
AIME9.2%Not measuredOnly Mistral Large measured
MATH 500 (Vals AI)74.4%Not measuredOnly Mistral Large measured
MGSM (Vals AI)87.2%Not measuredOnly Mistral Large measured
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
LMArena Text: math12611455Qwen3.8 27B
LMArena Text: mathematical work12571458Qwen3.8 27B
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index06
Knowledge benchmarks for Mistral Large and Qwen3.8 27B
BenchmarkMistral LargeQwen3.8 27BLeader
MMLU Pro69.7%84.3%Qwen3.8 27B
LegalBench (Vals AI)Not measured82.4%Only Qwen3.8 27B measured
TaxEval (Vals AI)63.8%70.8%Qwen3.8 27B
MortgageTax (Vals AI)Not measured64.9%Only Qwen3.8 27B measured
MedQA (Vals AI)76.2%Not measuredOnly Mistral Large measured
MedCode (Vals AI)Not measured28.7%Only Qwen3.8 27B measured
MMLU Pro (Artificial Analysis run)51.5%Not measuredOnly Mistral Large measured
MMLU68.8%Not measuredOnly Mistral Large measured
LMArena Text: science12601467Qwen3.8 27B
LMArena Text: medicine and healthcare12551437Qwen3.8 27B
LMArena Text: legal and government12751447Qwen3.8 27B
LMArena Text: business and finance12381438Qwen3.8 27B
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Web design benchmarks for Mistral Large and Qwen3.8 27B
BenchmarkMistral LargeQwen3.8 27BLeader
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
Neither model has a result on the other 8 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
General benchmarks for Mistral Large and Qwen3.8 27B
BenchmarkMistral LargeQwen3.8 27BLeader
LiveBench data analysisNot measured76.6%Only Qwen3.8 27B measured
SAGE (Vals AI)Not measured52.4%Only Qwen3.8 27B measured
MedScribe (Vals AI)Not measured83.8%Only Qwen3.8 27B measured
Long context reasoning (AA-LCR)Not measured82.0%Only Qwen3.8 27B measured
DTBench60.8%88.0%Qwen3.8 27B
LMCANot measured41.4%Only Qwen3.8 27B measured
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
LMArena Text12651440Qwen3.8 27B
LMArena Text: instruction following12491436Qwen3.8 27B
LMArena Text: creative writing12421383Qwen3.8 27B
LMArena VisionNot measured1269Only Qwen3.8 27B measured
LMArena Text: English12841448Qwen3.8 27B
LMArena Text: non-English12351428Qwen3.8 27B
LMArena Text: Chinese12391501Qwen3.8 27B
LMArena Text: German12421443Qwen3.8 27B
LMArena Text: Japanese11701387Qwen3.8 27B
LMArena Text: Korean12021384Qwen3.8 27B
LMArena Text: French13251465Qwen3.8 27B
LMArena Text: Spanish12681449Qwen3.8 27B
LMArena Text: Russian12521415Qwen3.8 27B
LMArena Text: Polish11571439Qwen3.8 27B
LMArena Text: multi-turn12601440Qwen3.8 27B
LMArena Text: longer queries12611448Qwen3.8 27B
LMArena Text: writing and language12611405Qwen3.8 27B
LMArena Text: entertainment and media12301393Qwen3.8 27B
LMArena Vision: EnglishNot measured1267Only Qwen3.8 27B measured
LMArena Vision: ChineseNot measured1326Only Qwen3.8 27B measured
LMArena Vision: reading textNot measured1281Only Qwen3.8 27B measured
LMArena Vision: humorNot measured1235Only Qwen3.8 27B measured
LMArena Vision: creative writingNot measured1237Only Qwen3.8 27B measured
Neither model has a result on the other 16 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Mistral Large and Qwen3.8 27B
BenchmarkMistral LargeQwen3.8 27BLeader
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 Large$0.0050Fits in one request
Qwen3.8 27B$0.0017Fits in one request

Qwen3.8 27B costs less

Repository review

50K input + 3K output tokens

Mistral Large$0.118Fits in one request
Qwen3.8 27B$0.029Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

Mistral Large$0.460Exceeds the context window
Qwen3.8 27B$0.111Fits in one request

Qwen3.8 27B 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 Large and Qwen3.8 27B
SpecificationMistral LargeQwen3.8 27B
MakerMistral AIQwen
WeightsNo public weights listedOpen weights
ReleasedFebruary 26, 2024August 14, 2026
Context window128K1M
Input $/Mtok$2.00$0.425
Output $/Mtok$6.00$2.55
Coding indexNot benchmarked74.2
Independent measurements behind it04
Cost per solved taskNot computable$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is Mistral Large better than Qwen3.8 27B for coding?

Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; Mistral Large has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, Mistral Large or Qwen3.8 27B?

Mistral Large costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Cost per solved task is known for Qwen3.8 27B only.

Which has the bigger context window, Mistral Large or Qwen3.8 27B?

Qwen3.8 27B, with 1M tokens against 128K.

How many benchmarks is the Mistral Large vs Qwen3.8 27B comparison based on?

37 benchmarks have a result for both models as of October 22, 2026. Mistral Large's coding index rests on 0 independent benchmarks and Qwen3.8 27B's on 4. Where only one model has a result, the page says so instead of naming a winner.

Compare next