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GPT-4o (2024-05-13) vs Qwen3.5-122B-A10B

Qwen3.5-122B-A10B leads GPT-4o (2024-05-13) on the Artificial Analysis Intelligence Index, 17.7 to 7.3, and Qwen3.5-122B-A10B costs 7.2x less per output token ($2.08 vs $15.00 per million). Updated October 22, 2026; 35 benchmarks have a result for both.

Replace GPT-4o (2024-05-13) Replace Qwen3.5-122B-A10B
GPT-4o (2024-05-13)OpenAI
Coding
No data
Overall
27.1
Output $/M
$15.00
0
Qwen3.5-122B-A10BQwen
Coding
No data
Overall
No data
Output $/M
$2.08
35
vs boards won of 35 shared

GPT-4o (2024-05-13) 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 (2024-05-13) for work measured by AA Coding Index or LMArena Text: coding, where it leads.

Model AGPT-4o (2024-05-13)OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-122B-A10BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
35
GPT-4o (2024-05-13) only
9
Qwen3.5-122B-A10B only
16
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
GPT-4o (2024-05-13) not benchmarked, Qwen3.5-122B-A10B not benchmarked.
Widest category
Reasoning, 26.6 points apart on average over 5 shared benchmarks.
Widest single gap
GPQA Diamond (Artificial Analysis run): GPT-4o (2024-05-13) 52.6% against Qwen3.5-122B-A10B 85.7%, 33.1 points apart on a 0 to 100 scale.
Output price
GPT-4o (2024-05-13) $15.00 against Qwen3.5-122B-A10B $2.08 per million output tokens, 7.2x apart.
Context window
GPT-4o (2024-05-13) 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 (2024-05-13)BarsQwen3.5-122B-A10BTies
Coding03
Intelligence01
Reasoning05
Math02
Knowledge04
General020
All shared boards035
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-4o (2024-05-13) and Qwen3.5-122B-A10B
BenchmarkGPT-4o (2024-05-13)Qwen3.5-122B-A10BLeader
LMArena WebDevNot measured1361Only Qwen3.5-122B-A10B measured
AA Coding Index24.243.3Qwen3.5-122B-A10B
LiveCodeBench (Artificial Analysis run)33.4%Not measuredOnly GPT-4o (2024-05-13) 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: coding12971436Qwen3.5-122B-A10B
LMArena Text: software and IT12971436Qwen3.5-122B-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 34 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4o (2024-05-13) and Qwen3.5-122B-A10B
BenchmarkGPT-4o (2024-05-13)Qwen3.5-122B-A10BLeader
AA Intelligence Index7.317.7Qwen3.5-122B-A10B
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Agentic work benchmarks for GPT-4o (2024-05-13) and Qwen3.5-122B-A10B
BenchmarkGPT-4o (2024-05-13)Qwen3.5-122B-A10BLeader
τ²-Bench TelecomNot measured93.6%Only Qwen3.5-122B-A10B measured
METR time horizons40.8%Not measuredOnly GPT-4o (2024-05-13) measured
BALROG32.3%Not measuredOnly GPT-4o (2024-05-13) measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GPT-4o (2024-05-13) and Qwen3.5-122B-A10B
BenchmarkGPT-4o (2024-05-13)Qwen3.5-122B-A10BLeader
GPQA Diamond50.2%Not measuredOnly GPT-4o (2024-05-13) measured
GPQA Diamond (Artificial Analysis run)52.6%85.7%Qwen3.5-122B-A10B
Humanity's Last Exam (Artificial Analysis run)1.8%25.2%Qwen3.5-122B-A10B
GPQA Diamond (Epoch AI run)48.9%Not measuredOnly GPT-4o (2024-05-13) measured
Mystery game puzzlesNot measured17.0%Only Qwen3.5-122B-A10B measured
LMArena Text: hard prompts12811421Qwen3.5-122B-A10B
LMArena Text: expert12501431Qwen3.5-122B-A10B
LMArena Text: hard prompts in English12931431Qwen3.5-122B-A10B
LMArena Vision: diagramsNot measured1249Only Qwen3.5-122B-A10B measured
LMArena Vision: homeworkNot measured1261Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4o (2024-05-13) and Qwen3.5-122B-A10B
BenchmarkGPT-4o (2024-05-13)Qwen3.5-122B-A10BLeader
OTIS Mock AIME6.3%Not measuredOnly GPT-4o (2024-05-13) measured
MATH level 551.0%Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: math12841431Qwen3.5-122B-A10B
LMArena Text: mathematical work12841431Qwen3.5-122B-A10B
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GPT-4o (2024-05-13) and Qwen3.5-122B-A10B
BenchmarkGPT-4o (2024-05-13)Qwen3.5-122B-A10BLeader
MMLU Pro (Artificial Analysis run)74.0%Not measuredOnly GPT-4o (2024-05-13) measured
MMLU84.2%Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: science12951432Qwen3.5-122B-A10B
LMArena Text: medicine and healthcare12781423Qwen3.5-122B-A10B
LMArena Text: legal and government13141422Qwen3.5-122B-A10B
LMArena Text: business and finance12621414Qwen3.5-122B-A10B
Neither model has a result on the other 14 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GPT-4o (2024-05-13) and Qwen3.5-122B-A10B
BenchmarkGPT-4o (2024-05-13)Qwen3.5-122B-A10BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
General benchmarks for GPT-4o (2024-05-13) and Qwen3.5-122B-A10B
BenchmarkGPT-4o (2024-05-13)Qwen3.5-122B-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 Text13001417Qwen3.5-122B-A10B
LMArena Text: instruction following12781399Qwen3.5-122B-A10B
LMArena Text: creative writing12911368Qwen3.5-122B-A10B
LMArena Vision11371244Qwen3.5-122B-A10B
LMArena Text: English13121428Qwen3.5-122B-A10B
LMArena Text: non-English12831401Qwen3.5-122B-A10B
LMArena Text: Chinese12771463Qwen3.5-122B-A10B
LMArena Text: German12831425Qwen3.5-122B-A10B
LMArena Text: Japanese12571367Qwen3.5-122B-A10B
LMArena Text: Korean12341353Qwen3.5-122B-A10B
LMArena Text: French13041443Qwen3.5-122B-A10B
LMArena Text: Spanish12921425Qwen3.5-122B-A10B
LMArena Text: Russian12851400Qwen3.5-122B-A10B
LMArena Text: Polish13651398Qwen3.5-122B-A10B
LMArena Text: multi-turn13021416Qwen3.5-122B-A10B
LMArena Text: longer queries12881410Qwen3.5-122B-A10B
LMArena Text: writing and language12981387Qwen3.5-122B-A10B
LMArena Text: entertainment and media12891366Qwen3.5-122B-A10B
LMArena Vision: English11471249Qwen3.5-122B-A10B
LMArena Vision: Chinese11321301Qwen3.5-122B-A10B
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 21 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4o (2024-05-13) and Qwen3.5-122B-A10B
BenchmarkGPT-4o (2024-05-13)Qwen3.5-122B-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 (2024-05-13)$0.013Fits in one request
Qwen3.5-122B-A10B$0.0013Fits in one request

Qwen3.5-122B-A10B costs less

Repository review

50K input + 3K output tokens

GPT-4o (2024-05-13)$0.295Fits in one request
Qwen3.5-122B-A10B$0.019Fits in one request

Qwen3.5-122B-A10B costs less

Agent loop

200K input + 10K output tokens

GPT-4o (2024-05-13)$1.15Exceeds 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 GPT-4o (2024-05-13) and Qwen3.5-122B-A10B
SpecificationOpenAI GPT-4o (2024-05-13)Qwen3.5-122B-A10B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedMay 13, 2024February 25, 2026
Context window128K262K
Input $/Mtok$5.00$0.26
Output $/Mtok$15.00$2.08
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o (2024-05-13) 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 (2024-05-13) for work measured by AA Coding Index or LMArena Text: coding, where it leads.

Which is cheaper, GPT-4o (2024-05-13) or Qwen3.5-122B-A10B?

GPT-4o (2024-05-13) costs $5.00 per million input tokens and $15.00 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 (2024-05-13) or Qwen3.5-122B-A10B?

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

How many benchmarks is the GPT-4o (2024-05-13) vs Qwen3.5-122B-A10B comparison based on?

35 benchmarks have a result for both models as of October 22, 2026. GPT-4o (2024-05-13)'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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