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

Qwen3.5-122B-A10B leads GPT-4 on the Artificial Analysis Intelligence Index, 17.7 to 6.7, and Qwen3.5-122B-A10B costs 28.8x less per output token ($2.08 vs $60.00 per million). Updated October 22, 2026; 34 benchmarks have a result for both.

Replace GPT-4 Replace Qwen3.5-122B-A10B
GPT-4OpenAI
Coding
No data
Overall
No data
Output $/M
$60.00
0
Qwen3.5-122B-A10BQwen
Coding
No data
Overall
No data
Output $/M
$2.08
34
vs boards won of 34 shared

GPT-4 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-4 for work measured by AA Coding Index or LMArena Text: coding, where it leads.

Model AGPT-4OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-122B-A10BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
34
GPT-4 only
11
Qwen3.5-122B-A10B only
17
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-4 not benchmarked, Qwen3.5-122B-A10B not benchmarked.
Widest category
Knowledge, 43.0 points apart on average over 4 shared benchmarks.
Widest single gap
GPQA Diamond (Artificial Analysis run): GPT-4 34.9% against Qwen3.5-122B-A10B 85.7%, 50.8 points apart on a 0 to 100 scale.
Output price
GPT-4 $60.00 against Qwen3.5-122B-A10B $2.08 per million output tokens, 28.8x apart.
Context window
GPT-4 takes 8K 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-4BarsQwen3.5-122B-A10BTies
Coding03
Intelligence01
Reasoning04
Math02
Knowledge04
General020
All shared boards034
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-4 and Qwen3.5-122B-A10B
BenchmarkGPT-4Qwen3.5-122B-A10BLeader
SWE-bench Verified22.4%Not measuredOnly GPT-4 measured
SWE-bench Lite39.7%Not measuredOnly GPT-4 measured
SWE-bench (full test set)12.5%Not measuredOnly GPT-4 measured
LMArena WebDevNot measured1361Only Qwen3.5-122B-A10B measured
AA Coding Index13.143.3Qwen3.5-122B-A10B
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: coding12091436Qwen3.5-122B-A10B
LMArena Text: software and IT11951436Qwen3.5-122B-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 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4 and Qwen3.5-122B-A10B
BenchmarkGPT-4Qwen3.5-122B-A10BLeader
AA Intelligence Index6.717.7Qwen3.5-122B-A10B
Epoch Capabilities Index125.7Not measuredOnly GPT-4 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Agentic work benchmarks for GPT-4 and Qwen3.5-122B-A10B
BenchmarkGPT-4Qwen3.5-122B-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 index04
Reasoning benchmarks for GPT-4 and Qwen3.5-122B-A10B
BenchmarkGPT-4Qwen3.5-122B-A10BLeader
GPQA Diamond (Artificial Analysis run)34.9%85.7%Qwen3.5-122B-A10B
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
BIG-Bench Hard75.1%Not measuredOnly GPT-4 measured
LMArena Text: hard prompts12001421Qwen3.5-122B-A10B
LMArena Text: expert11491431Qwen3.5-122B-A10B
LMArena Text: hard prompts in English12091431Qwen3.5-122B-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-4 and Qwen3.5-122B-A10B
BenchmarkGPT-4Qwen3.5-122B-A10BLeader
GSM8K92.0%Not measuredOnly GPT-4 measured
LMArena Text: math12301431Qwen3.5-122B-A10B
LMArena Text: mathematical work12281431Qwen3.5-122B-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-4 and Qwen3.5-122B-A10B
BenchmarkGPT-4Qwen3.5-122B-A10BLeader
MMLU Pro (Artificial Analysis run)56.2%Not measuredOnly GPT-4 measured
MMLU86.4%Not measuredOnly GPT-4 measured
AI2 Reasoning Challenge96.3%Not measuredOnly GPT-4 measured
LMArena Text: science11771432Qwen3.5-122B-A10B
LMArena Text: medicine and healthcare11451423Qwen3.5-122B-A10B
LMArena Text: legal and government11911422Qwen3.5-122B-A10B
LMArena Text: business and finance11451414Qwen3.5-122B-A10B
Neither model has a result on the other 11 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GPT-4 and Qwen3.5-122B-A10B
BenchmarkGPT-4Qwen3.5-122B-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-4 and Qwen3.5-122B-A10B
BenchmarkGPT-4Qwen3.5-122B-A10BLeader
IFBench33.2%75.7%Qwen3.5-122B-A10B
Long context reasoning (AA-LCR)Not measured76.3%Only Qwen3.5-122B-A10B measured
DTBench62.7%84.3%Qwen3.5-122B-A10B
LMCA17.1%32.2%Qwen3.5-122B-A10B
HellaSwag95.3%Not measuredOnly GPT-4 measured
WinoGrande87.5%Not measuredOnly GPT-4 measured
LMArena Text12061417Qwen3.5-122B-A10B
LMArena Text: instruction following12011399Qwen3.5-122B-A10B
LMArena Text: creative writing11911368Qwen3.5-122B-A10B
LMArena VisionNot measured1244Only Qwen3.5-122B-A10B measured
LMArena Text: English12201428Qwen3.5-122B-A10B
LMArena Text: non-English11941401Qwen3.5-122B-A10B
LMArena Text: Chinese11841463Qwen3.5-122B-A10B
LMArena Text: German11981425Qwen3.5-122B-A10B
LMArena Text: Japanese11371367Qwen3.5-122B-A10B
LMArena Text: Korean10871353Qwen3.5-122B-A10B
LMArena Text: French12201443Qwen3.5-122B-A10B
LMArena Text: Spanish11971425Qwen3.5-122B-A10B
LMArena Text: Russian11951400Qwen3.5-122B-A10B
LMArena Text: Polish12651398Qwen3.5-122B-A10B
LMArena Text: multi-turn12061416Qwen3.5-122B-A10B
LMArena Text: longer queries11901410Qwen3.5-122B-A10B
LMArena Text: writing and language12191387Qwen3.5-122B-A10B
LMArena Text: entertainment and media11841366Qwen3.5-122B-A10B
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 17 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4 and Qwen3.5-122B-A10B
BenchmarkGPT-4Qwen3.5-122B-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-4$0.060Fits 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-4$1.68Exceeds the context window
Qwen3.5-122B-A10B$0.019Fits in one request

Qwen3.5-122B-A10B costs less

Agent loop

200K input + 10K output tokens

GPT-4$6.60Exceeds 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-4 and Qwen3.5-122B-A10B
SpecificationOpenAI GPT-4Qwen3.5-122B-A10B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedMay 28, 2023February 25, 2026
Context window8K262K
Input $/Mtok$30.00$0.26
Output $/Mtok$60.00$2.08
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4 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-4 for work measured by AA Coding Index or LMArena Text: coding, where it leads.

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

GPT-4 costs $30.00 per million input tokens and $60.00 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-4 or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B, with 262K tokens against 8K.

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

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