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Mistral Small 3.1 24B vs Qwen3.5-122B-A10B

Mistral Small 3.1 24B costs 3.7x less per output token than Qwen3.5-122B-A10B ($0.555 vs $2.08 per million). Updated October 22, 2026; 36 benchmarks have a result for both.

Replace Mistral Small 3.1 24B Replace Qwen3.5-122B-A10B
Mistral Small 3.1 24BMistral AI
Coding
No data
Overall
No data
Output $/M
$0.555
0
Qwen3.5-122B-A10BQwen
Coding
No data
Overall
No data
Output $/M
$2.08
36
vs boards won of 36 shared

Mistral Small 3.1 24B or Qwen3.5-122B-A10B: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick Mistral Small 3.1 24B for output tokens 3.7x cheaper ($0.555 vs $2.08 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.

Model AMistral Small 3.1 24BMistral AINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-122B-A10BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
36
Mistral Small 3.1 24B only
2
Qwen3.5-122B-A10B only
15
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, Qwen3.5-122B-A10B not benchmarked.
Widest category
Math, 27.5 points apart on average over 2 shared benchmarks.
Widest single gap
LMArena Text: Chinese: Mistral Small 3.1 24B 1253 against Qwen3.5-122B-A10B 1463, 35.0 points apart on a 0 to 100 scale.
Output price
Mistral Small 3.1 24B $0.555 against Qwen3.5-122B-A10B $2.08 per million output tokens, 3.7x apart.
Context window
Mistral Small 3.1 24B takes 128K tokens, Qwen3.5-122B-A10B 262K.

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 24BBarsQwen3.5-122B-A10BTies
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 Qwen3.5-122B-A10B
BenchmarkMistral Small 3.1 24BQwen3.5-122B-A10BLeader
LMArena WebDevNot measured1361Only Qwen3.5-122B-A10B measured
AA Coding IndexNot measured43.3Only Qwen3.5-122B-A10B measured
SciCodeNot measured39.7%Only Qwen3.5-122B-A10B measured
Terminal-Bench Hard (Artificial Analysis run)Not measured31.1%Only Qwen3.5-122B-A10B measured
LMArena Text: coding13091436Qwen3.5-122B-A10B
LMArena Text: software and IT13011436Qwen3.5-122B-A10B
LMArena WebDev: ReactNot measured1353Only Qwen3.5-122B-A10B measured
LMArena WebDev: HTMLNot measured1351Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 35 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Intelligence benchmarks for Mistral Small 3.1 24B and Qwen3.5-122B-A10B
BenchmarkMistral Small 3.1 24BQwen3.5-122B-A10BLeader
AA Intelligence IndexNot measured17.7Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Agentic work benchmarks for Mistral Small 3.1 24B and Qwen3.5-122B-A10B
BenchmarkMistral Small 3.1 24BQwen3.5-122B-A10BLeader
τ²-Bench TelecomNot measured93.6%Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 41 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Mistral Small 3.1 24B and Qwen3.5-122B-A10B
BenchmarkMistral Small 3.1 24BQwen3.5-122B-A10BLeader
GPQA Diamond (Artificial Analysis run)Not measured85.7%Only Qwen3.5-122B-A10B measured
Humanity's Last Exam (Artificial Analysis run)Not measured25.2%Only Qwen3.5-122B-A10B measured
Mystery game puzzlesNot measured17.0%Only Qwen3.5-122B-A10B measured
LMArena Text: hard prompts12781421Qwen3.5-122B-A10B
LMArena Text: expert12571431Qwen3.5-122B-A10B
LMArena Text: hard prompts in English12901431Qwen3.5-122B-A10B
LMArena Vision: diagrams11191249Qwen3.5-122B-A10B
LMArena Vision: homework11581261Qwen3.5-122B-A10B
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Mistral Small 3.1 24B and Qwen3.5-122B-A10B
BenchmarkMistral Small 3.1 24BQwen3.5-122B-A10BLeader
LMArena Text: math12611431Qwen3.5-122B-A10B
LMArena Text: mathematical work12711431Qwen3.5-122B-A10B
Neither model has a result on the other 12 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Mistral Small 3.1 24B and Qwen3.5-122B-A10B
BenchmarkMistral Small 3.1 24BQwen3.5-122B-A10BLeader
LMArena Text: science12651432Qwen3.5-122B-A10B
LMArena Text: medicine and healthcare12671423Qwen3.5-122B-A10B
LMArena Text: legal and government12711422Qwen3.5-122B-A10B
LMArena Text: business and finance12571414Qwen3.5-122B-A10B
Neither model has a result on the other 16 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Mistral Small 3.1 24B and Qwen3.5-122B-A10B
BenchmarkMistral Small 3.1 24BQwen3.5-122B-A10BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index023
General benchmarks for Mistral Small 3.1 24B and Qwen3.5-122B-A10B
BenchmarkMistral Small 3.1 24BQwen3.5-122B-A10BLeader
IFBenchNot measured75.7%Only Qwen3.5-122B-A10B measured
Long context reasoning (AA-LCR)Not measured76.3%Only Qwen3.5-122B-A10B measured
DTBenchNot measured84.3%Only Qwen3.5-122B-A10B measured
LMCANot measured32.2%Only Qwen3.5-122B-A10B measured
LMArena Text12771417Qwen3.5-122B-A10B
LMArena Text: instruction following12631399Qwen3.5-122B-A10B
LMArena Text: creative writing12521368Qwen3.5-122B-A10B
LMArena Vision11361244Qwen3.5-122B-A10B
LMArena Text: English12951428Qwen3.5-122B-A10B
LMArena Text: non-English12551401Qwen3.5-122B-A10B
LMArena Text: Chinese12531463Qwen3.5-122B-A10B
LMArena Text: German12661425Qwen3.5-122B-A10B
LMArena Text: Japanese12081367Qwen3.5-122B-A10B
LMArena Text: Korean12071353Qwen3.5-122B-A10B
LMArena Text: French12731443Qwen3.5-122B-A10B
LMArena Text: Spanish12841425Qwen3.5-122B-A10B
LMArena Text: Russian12631400Qwen3.5-122B-A10B
LMArena Text: Polish12421398Qwen3.5-122B-A10B
LMArena Text: multi-turn12701416Qwen3.5-122B-A10B
LMArena Text: longer queries12991410Qwen3.5-122B-A10B
LMArena Text: writing and language12681387Qwen3.5-122B-A10B
LMArena Text: entertainment and media12501366Qwen3.5-122B-A10B
LMArena Vision: English11571249Qwen3.5-122B-A10B
LMArena Vision: Chinese11551301Qwen3.5-122B-A10B
LMArena Vision: reading text11321251Qwen3.5-122B-A10B
LMArena Vision: humor11421243Qwen3.5-122B-A10B
LMArena Vision: captioning1153Not measuredOnly Mistral Small 3.1 24B measured
LMArena Vision: recognizing things1155Not measuredOnly Mistral Small 3.1 24B measured
LMArena Vision: creative writing11231220Qwen3.5-122B-A10B
Neither model has a result on the other 19 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 Qwen3.5-122B-A10B
BenchmarkMistral Small 3.1 24BQwen3.5-122B-A10BLeader
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
Qwen3.5-122B-A10B$0.0013Fits 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
Qwen3.5-122B-A10B$0.019Fits 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
Qwen3.5-122B-A10B$0.073Fits in one request

Qwen3.5-122B-A10B 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 Qwen3.5-122B-A10B
SpecificationMistral Small 3.1 24BQwen3.5-122B-A10B
MakerMistral AIQwen
WeightsOpen weightsOpen weights
ReleasedMarch 17, 2025February 25, 2026
Context window128K262K
Input $/Mtok$0.351$0.26
Output $/Mtok$0.555$2.08
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Mistral Small 3.1 24B better than Qwen3.5-122B-A10B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Small 3.1 24B for output tokens 3.7x cheaper ($0.555 vs $2.08 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.

Which is cheaper, Mistral Small 3.1 24B or Qwen3.5-122B-A10B?

Mistral Small 3.1 24B costs $0.351 per million input tokens and $0.555 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for neither of them.

Which has the bigger context window, Mistral Small 3.1 24B or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B, with 262K tokens against 128K.

How many benchmarks is the Mistral Small 3.1 24B vs Qwen3.5-122B-A10B 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 Qwen3.5-122B-A10B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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