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Mistral Small 3.1 24B vs Qwen3.8 27B

Mistral Small 3.1 24B costs 4.6x less per output token than Qwen3.8 27B ($0.555 vs $2.55 per million). Updated October 22, 2026; 36 benchmarks have a result for both.

Replace Mistral Small 3.1 24B Replace Qwen3.8 27B
Mistral Small 3.1 24BMistral AI
Coding
No data
Overall
No data
Output $/M
$0.555
0
Qwen3.8 27BQwen
Coding
74.2
Overall
59.6
Output $/M
$2.55
36
vs boards won of 36 shared

Mistral Small 3.1 24B or Qwen3.8 27B: which to pick

Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; Mistral Small 3.1 24B has none, so the two cannot be ranked on coding.

Pick Mistral Small 3.1 24B for output tokens 4.6x cheaper ($0.555 vs $2.55 per million).

Model AMistral Small 3.1 24BMistral AINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.8 27BQwen74.2/1004 independent benchmarks$1.26 per solved task
Shared results
36
Mistral Small 3.1 24B only
2
Qwen3.8 27B only
49
Like-for-like categories
5 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 Small 3.1 24B not benchmarked, Qwen3.8 27B 74.2 from 4 benchmarks.
Widest category
Math, 31.7 points apart on average over 2 shared benchmarks.
Widest single gap
LMArena Text: Chinese: Mistral Small 3.1 24B 1253 against Qwen3.8 27B 1501, 41.4 points apart on a 0 to 100 scale.
Output price
Mistral Small 3.1 24B $0.555 against Qwen3.8 27B $2.55 per million output tokens, 4.6x apart.
Context window
Mistral Small 3.1 24B 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 Small 3.1 24BBarsQwen3.8 27BTies
Coding02
Reasoning05
Math02
Knowledge04
General023
All shared boards036
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for Mistral Small 3.1 24B and Qwen3.8 27B
BenchmarkMistral Small 3.1 24BQwen3.8 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)Not measured84.0%Only Qwen3.8 27B measured
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
SciCodeNot measured46.6%Only Qwen3.8 27B measured
LMArena Text: coding13091483Qwen3.8 27B
LMArena Text: software and IT13011478Qwen3.8 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 25 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for Mistral Small 3.1 24B and Qwen3.8 27B
BenchmarkMistral Small 3.1 24BQwen3.8 27BLeader
AA Intelligence IndexNot measured33.7Only Qwen3.8 27B measured
Epoch Capabilities IndexNot measured151.7Only Qwen3.8 27B measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Agentic work benchmarks for Mistral Small 3.1 24B and Qwen3.8 27B
BenchmarkMistral Small 3.1 24BQwen3.8 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 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Mistral Small 3.1 24B and Qwen3.8 27B
BenchmarkMistral Small 3.1 24BQwen3.8 27BLeader
GPQA DiamondNot measured88.9%Only Qwen3.8 27B measured
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)Not measured90.5%Only Qwen3.8 27B measured
Humanity's Last Exam (Artificial Analysis run)Not measured33.9%Only Qwen3.8 27B measured
ARC-AGINot measured87.5%Only Qwen3.8 27B measured
ARC-AGI-2Not measured42.4%Only Qwen3.8 27B measured
LMArena Text: hard prompts12781459Qwen3.8 27B
LMArena Text: expert12571479Qwen3.8 27B
LMArena Text: hard prompts in English12901461Qwen3.8 27B
LMArena Vision: diagrams11191285Qwen3.8 27B
LMArena Vision: homework11581295Qwen3.8 27B
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 Small 3.1 24B and Qwen3.8 27B
BenchmarkMistral Small 3.1 24BQwen3.8 27BLeader
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
LMArena Text: math12611455Qwen3.8 27B
LMArena Text: mathematical work12711458Qwen3.8 27B
Neither model has a result on the other 11 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Mistral Small 3.1 24B and Qwen3.8 27B
BenchmarkMistral Small 3.1 24BQwen3.8 27BLeader
MMLU ProNot measured84.3%Only Qwen3.8 27B measured
LegalBench (Vals AI)Not measured82.4%Only Qwen3.8 27B measured
TaxEval (Vals AI)Not measured70.8%Only Qwen3.8 27B measured
MortgageTax (Vals AI)Not measured64.9%Only Qwen3.8 27B measured
MedCode (Vals AI)Not measured28.7%Only Qwen3.8 27B measured
LMArena Text: science12651467Qwen3.8 27B
LMArena Text: medicine and healthcare12671437Qwen3.8 27B
LMArena Text: legal and government12711447Qwen3.8 27B
LMArena Text: business and finance12571438Qwen3.8 27B
Neither model has a result on the other 11 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Web design benchmarks for Mistral Small 3.1 24B and Qwen3.8 27B
BenchmarkMistral Small 3.1 24BQwen3.8 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 index023
General benchmarks for Mistral Small 3.1 24B and Qwen3.8 27B
BenchmarkMistral Small 3.1 24BQwen3.8 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
DTBenchNot measured88.0%Only Qwen3.8 27B measured
LMCANot measured41.4%Only Qwen3.8 27B measured
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
LMArena Text12771440Qwen3.8 27B
LMArena Text: instruction following12631436Qwen3.8 27B
LMArena Text: creative writing12521383Qwen3.8 27B
LMArena Vision11361269Qwen3.8 27B
LMArena Text: English12951448Qwen3.8 27B
LMArena Text: non-English12551428Qwen3.8 27B
LMArena Text: Chinese12531501Qwen3.8 27B
LMArena Text: German12661443Qwen3.8 27B
LMArena Text: Japanese12081387Qwen3.8 27B
LMArena Text: Korean12071384Qwen3.8 27B
LMArena Text: French12731465Qwen3.8 27B
LMArena Text: Spanish12841449Qwen3.8 27B
LMArena Text: Russian12631415Qwen3.8 27B
LMArena Text: Polish12421439Qwen3.8 27B
LMArena Text: multi-turn12701440Qwen3.8 27B
LMArena Text: longer queries12991448Qwen3.8 27B
LMArena Text: writing and language12681405Qwen3.8 27B
LMArena Text: entertainment and media12501393Qwen3.8 27B
LMArena Vision: English11571267Qwen3.8 27B
LMArena Vision: Chinese11551326Qwen3.8 27B
LMArena Vision: reading text11321281Qwen3.8 27B
LMArena Vision: humor11421235Qwen3.8 27B
LMArena Vision: captioning1153Not measuredOnly Mistral Small 3.1 24B measured
LMArena Vision: recognizing things1155Not measuredOnly Mistral Small 3.1 24B measured
LMArena Vision: creative writing11231237Qwen3.8 27B
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 Small 3.1 24B and Qwen3.8 27B
BenchmarkMistral Small 3.1 24BQwen3.8 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 Small 3.1 24B$0.0006Fits in one request
Qwen3.8 27B$0.0017Fits in one request

Mistral Small 3.1 24B costs less

Repository review

50K input + 3K output tokens

Mistral Small 3.1 24B$0.019Fits in one request
Qwen3.8 27B$0.029Fits in one request

Mistral Small 3.1 24B costs less

Agent loop

200K input + 10K output tokens

Mistral Small 3.1 24B$0.076Exceeds the context window
Qwen3.8 27B$0.111Fits in one request

Mistral Small 3.1 24B 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 Small 3.1 24B and Qwen3.8 27B
SpecificationMistral Small 3.1 24BQwen3.8 27B
MakerMistral AIQwen
WeightsOpen weightsOpen weights
ReleasedMarch 17, 2025August 14, 2026
Context window128K1M
Input $/Mtok$0.351$0.425
Output $/Mtok$0.555$2.55
Coding indexNot benchmarked74.2
Independent measurements behind it04
Cost per solved taskNot computable$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is Mistral Small 3.1 24B better than Qwen3.8 27B for coding?

Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; Mistral Small 3.1 24B has none, so the two cannot be ranked on coding. Pick Mistral Small 3.1 24B for output tokens 4.6x cheaper ($0.555 vs $2.55 per million).

Which is cheaper, Mistral Small 3.1 24B or Qwen3.8 27B?

Mistral Small 3.1 24B costs $0.351 per million input tokens and $0.555 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 Small 3.1 24B or Qwen3.8 27B?

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

How many benchmarks is the Mistral Small 3.1 24B vs Qwen3.8 27B comparison based on?

36 benchmarks have a result for both models as of October 22, 2026. Mistral Small 3.1 24B'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.

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