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GPT-4o-mini vs Qwen3.5-122B-A10B

Qwen3.5-122B-A10B leads GPT-4o-mini on the Artificial Analysis Intelligence Index, 17.7 to 6.7, and GPT-4o-mini costs 3.5x less per output token ($0.60 vs $2.08 per million). Updated October 22, 2026; 7 benchmarks have a result for both.

Replace GPT-4o-mini Replace Qwen3.5-122B-A10B
GPT-4o-miniOpenAI
Coding
No data
Overall
No data
Output $/M
$0.60
0
Qwen3.5-122B-A10BQwen
Coding
No data
Overall
No data
Output $/M
$2.08
7
vs boards won of 7 shared

GPT-4o-mini or Qwen3.5-122B-A10B: which to pick

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

Pick GPT-4o-mini for output tokens 3.5x cheaper ($0.60 vs $2.08 per million) and work measured by AA Coding Index or AA Intelligence Index, where it leads.

Model AGPT-4o-miniOpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-122B-A10BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
7
GPT-4o-mini only
22
Qwen3.5-122B-A10B only
44
Like-for-like categories
4 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
GPT-4o-mini not benchmarked, Qwen3.5-122B-A10B not benchmarked.
Widest category
General, 32.2 points apart on average over 3 shared benchmarks.
Widest single gap
IFBench: GPT-4o-mini 31.0% against Qwen3.5-122B-A10B 75.7%, 44.8 points apart on a 0 to 100 scale.
Output price
GPT-4o-mini $0.60 against Qwen3.5-122B-A10B $2.08 per million output tokens, 3.5x apart.
Context window
GPT-4o-mini 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
CategoryGPT-4o-miniBarsQwen3.5-122B-A10BTies
Coding01
Intelligence01
Reasoning02
General03
All shared boards07
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
Coding benchmarks for GPT-4o-mini and Qwen3.5-122B-A10B
BenchmarkGPT-4o-miniQwen3.5-122B-A10BLeader
LMArena WebDevNot measured1361Only Qwen3.5-122B-A10B measured
AA Coding Index11.443.3Qwen3.5-122B-A10B
LiveCodeBench (Vals AI)26.4%Not measuredOnly GPT-4o-mini measured
LiveCodeBench (Artificial Analysis run)23.4%Not measuredOnly GPT-4o-mini 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: codingNot measured1436Only Qwen3.5-122B-A10B measured
LMArena Text: software and ITNot measured1436Only Qwen3.5-122B-A10B measured
LMArena WebDev: ReactNot measured1353Only Qwen3.5-122B-A10B measured
LMArena WebDev: HTMLNot measured1351Only Qwen3.5-122B-A10B measured
SciCode1.5%Not measuredOnly GPT-4o-mini measured
Neither model has a result on the other 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4o-mini and Qwen3.5-122B-A10B
BenchmarkGPT-4o-miniQwen3.5-122B-A10BLeader
AA Intelligence Index6.717.7Qwen3.5-122B-A10B
Epoch Capabilities Index126.5Not measuredOnly GPT-4o-mini measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Agentic work benchmarks for GPT-4o-mini and Qwen3.5-122B-A10B
BenchmarkGPT-4o-miniQwen3.5-122B-A10BLeader
τ²-Bench TelecomNot measured93.6%Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for GPT-4o-mini and Qwen3.5-122B-A10B
BenchmarkGPT-4o-miniQwen3.5-122B-A10BLeader
GPQA Diamond44.2%Not measuredOnly GPT-4o-mini measured
MMMU Pro (Vals AI)56.6%Not measuredOnly GPT-4o-mini measured
GPQA Diamond (Artificial Analysis run)42.6%85.7%Qwen3.5-122B-A10B
Humanity's Last Exam (Artificial Analysis run)4.2%25.2%Qwen3.5-122B-A10B
ARC-AGI-20.0%Not measuredOnly GPT-4o-mini measured
Mystery game puzzlesNot measured17.0%Only Qwen3.5-122B-A10B measured
LMArena Text: hard promptsNot measured1421Only Qwen3.5-122B-A10B measured
LMArena Text: expertNot measured1431Only Qwen3.5-122B-A10B measured
LMArena Text: hard prompts in EnglishNot measured1431Only Qwen3.5-122B-A10B measured
LMArena Vision: diagramsNot measured1249Only Qwen3.5-122B-A10B measured
LMArena Vision: homeworkNot measured1261Only Qwen3.5-122B-A10B measured
Kagi LLM Benchmark28.8%Not measuredOnly GPT-4o-mini measured
Neither model has a result on the other 10 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Math benchmarks for GPT-4o-mini and Qwen3.5-122B-A10B
BenchmarkGPT-4o-miniQwen3.5-122B-A10BLeader
AIME11.5%Not measuredOnly GPT-4o-mini measured
MATH 500 (Vals AI)72.6%Not measuredOnly GPT-4o-mini measured
MGSM (Vals AI)86.2%Not measuredOnly GPT-4o-mini measured
AA Math Index14.7Not measuredOnly GPT-4o-mini measured
AIME 2025 (Artificial Analysis run)14.7%Not measuredOnly GPT-4o-mini measured
GSM8K91.3%Not measuredOnly GPT-4o-mini measured
LMArena Text: mathNot measured1431Only Qwen3.5-122B-A10B measured
LMArena Text: mathematical workNot measured1431Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 5 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
Knowledge benchmarks for GPT-4o-mini and Qwen3.5-122B-A10B
BenchmarkGPT-4o-miniQwen3.5-122B-A10BLeader
MMLU Pro62.7%Not measuredOnly GPT-4o-mini measured
CorpFin (Vals AI)45.5%Not measuredOnly GPT-4o-mini measured
TaxEval (Vals AI)60.5%Not measuredOnly GPT-4o-mini measured
MortgageTax (Vals AI)54.5%Not measuredOnly GPT-4o-mini measured
MedQA (Vals AI)72.4%Not measuredOnly GPT-4o-mini measured
MMLU Pro (Artificial Analysis run)64.8%Not measuredOnly GPT-4o-mini measured
MMLU81.8%Not measuredOnly GPT-4o-mini measured
LMArena Text: scienceNot measured1432Only Qwen3.5-122B-A10B measured
LMArena Text: medicine and healthcareNot measured1423Only Qwen3.5-122B-A10B measured
LMArena Text: legal and governmentNot measured1422Only Qwen3.5-122B-A10B measured
LMArena Text: business and financeNot measured1414Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 7 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GPT-4o-mini and Qwen3.5-122B-A10B
BenchmarkGPT-4o-miniQwen3.5-122B-A10BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
General benchmarks for GPT-4o-mini and Qwen3.5-122B-A10B
BenchmarkGPT-4o-miniQwen3.5-122B-A10BLeader
IFBench31.0%75.7%Qwen3.5-122B-A10B
Long context reasoning (AA-LCR)Not measured76.3%Only Qwen3.5-122B-A10B measured
DTBench54.4%84.3%Qwen3.5-122B-A10B
LMCA10.4%32.2%Qwen3.5-122B-A10B
PIQA88.7%Not measuredOnly GPT-4o-mini measured
LMArena TextNot measured1417Only Qwen3.5-122B-A10B measured
LMArena Text: instruction followingNot measured1399Only Qwen3.5-122B-A10B measured
LMArena Text: creative writingNot measured1368Only Qwen3.5-122B-A10B measured
LMArena VisionNot measured1244Only Qwen3.5-122B-A10B measured
LMArena Text: EnglishNot measured1428Only Qwen3.5-122B-A10B measured
LMArena Text: non-EnglishNot measured1401Only Qwen3.5-122B-A10B measured
LMArena Text: ChineseNot measured1463Only Qwen3.5-122B-A10B measured
LMArena Text: GermanNot measured1425Only Qwen3.5-122B-A10B measured
LMArena Text: JapaneseNot measured1367Only Qwen3.5-122B-A10B measured
LMArena Text: KoreanNot measured1353Only Qwen3.5-122B-A10B measured
LMArena Text: FrenchNot measured1443Only Qwen3.5-122B-A10B measured
LMArena Text: SpanishNot measured1425Only Qwen3.5-122B-A10B measured
LMArena Text: RussianNot measured1400Only Qwen3.5-122B-A10B measured
LMArena Text: PolishNot measured1398Only Qwen3.5-122B-A10B measured
LMArena Text: multi-turnNot measured1416Only Qwen3.5-122B-A10B measured
LMArena Text: longer queriesNot measured1410Only Qwen3.5-122B-A10B measured
LMArena Text: writing and languageNot measured1387Only Qwen3.5-122B-A10B measured
LMArena Text: entertainment and mediaNot measured1366Only Qwen3.5-122B-A10B measured
LMArena Vision: EnglishNot measured1249Only Qwen3.5-122B-A10B measured
LMArena Vision: ChineseNot measured1301Only Qwen3.5-122B-A10B measured
LMArena Vision: reading textNot measured1251Only Qwen3.5-122B-A10B measured
LMArena Vision: humorNot measured1243Only Qwen3.5-122B-A10B measured
LMArena Vision: creative writingNot measured1220Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 18 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4o-mini and Qwen3.5-122B-A10B
BenchmarkGPT-4o-miniQwen3.5-122B-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

GPT-4o-mini$0.0004Fits in one request
Qwen3.5-122B-A10B$0.0013Fits in one request

GPT-4o-mini costs less

Repository review

50K input + 3K output tokens

GPT-4o-mini$0.0093Fits in one request
Qwen3.5-122B-A10B$0.019Fits in one request

GPT-4o-mini costs less

Agent loop

200K input + 10K output tokens

GPT-4o-mini$0.036Exceeds the context window
Qwen3.5-122B-A10B$0.073Fits in one request

GPT-4o-mini 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 GPT-4o-mini and Qwen3.5-122B-A10B
SpecificationOpenAI GPT-4o-miniQwen3.5-122B-A10B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedJuly 18, 2024February 25, 2026
Context window128K262K
Input $/Mtok$0.15$0.26
Output $/Mtok$0.60$2.08
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o-mini better than Qwen3.5-122B-A10B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o-mini for output tokens 3.5x cheaper ($0.60 vs $2.08 per million) and work measured by AA Coding Index or AA Intelligence Index, where it leads.

Which is cheaper, GPT-4o-mini or Qwen3.5-122B-A10B?

GPT-4o-mini costs $0.15 per million input tokens and $0.60 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, GPT-4o-mini or Qwen3.5-122B-A10B?

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

How many benchmarks is the GPT-4o-mini vs Qwen3.5-122B-A10B comparison based on?

7 benchmarks have a result for both models as of October 22, 2026. GPT-4o-mini'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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