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GPT-5.3-Codex vs Qwen3.5-122B-A10B

GPT-5.3-Codex leads Qwen3.5-122B-A10B on the Artificial Analysis Intelligence Index, 32.5 to 17.7, and Qwen3.5-122B-A10B costs 6.7x less per output token ($2.08 vs $14.00 per million). Updated October 22, 2026; 10 benchmarks have a result for both.

Replace GPT-5.3-Codex Replace Qwen3.5-122B-A10B
GPT-5.3-CodexOpenAI
Coding
60.0
Overall
No data
Output $/M
$14.00
8
Qwen3.5-122B-A10BQwen
Coding
No data
Overall
No data
Output $/M
$2.08
2
vs boards won of 10 shared

GPT-5.3-Codex or Qwen3.5-122B-A10B: which to pick

Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Qwen3.5-122B-A10B has none, so the two cannot be ranked on coding.

Pick Qwen3.5-122B-A10B for output tokens 6.7x cheaper ($2.08 vs $14.00 per million) and work measured by τ²-Bench Telecom or IFBench, where it leads.

Model AGPT-5.3-CodexOpenAI60.0/1003 independent benchmarks$0.59 per solved taskModel BQwen3.5-122B-A10BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
10
GPT-5.3-Codex only
24
Qwen3.5-122B-A10B only
41
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
GPT-5.3-Codex 60.0 from 3 benchmarks, Qwen3.5-122B-A10B not benchmarked.
Widest category
Intelligence, 14.8 points apart on average over 1 shared benchmark.
Widest single gap
Terminal-Bench Hard (Artificial Analysis run): GPT-5.3-Codex 53.0% against Qwen3.5-122B-A10B 31.1%, 22.0 points apart on a 0 to 100 scale.
Output price
GPT-5.3-Codex $14.00 against Qwen3.5-122B-A10B $2.08 per million output tokens, 6.7x apart.
Context window
GPT-5.3-Codex takes 400K 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-5.3-CodexBarsQwen3.5-122B-A10BTies
Coding40
Intelligence10
Agentic work01
Reasoning20
General11
All shared boards82
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for GPT-5.3-Codex and Qwen3.5-122B-A10B
BenchmarkGPT-5.3-CodexQwen3.5-122B-A10BLeader
LMArena WebDev14071361GPT-5.3-Codex
AA Coding IndexNot measured43.3Only Qwen3.5-122B-A10B measured
LiveCodeBench (Vals AI)87.3%Not measuredOnly GPT-5.3-Codex measured
SWE-bench Verified (Vals AI)78.0%Not measuredOnly GPT-5.3-Codex measured
Vibe Code Bench (Vals AI)61.8%Not measuredOnly GPT-5.3-Codex measured
IOI (Vals AI)53.8%Not measuredOnly GPT-5.3-Codex measured
SciCodeNot measured39.7%Only Qwen3.5-122B-A10B measured
Terminal-Bench Hard (Artificial Analysis run)53.0%31.1%GPT-5.3-Codex
Terminal-Bench78.4%Not measuredOnly GPT-5.3-Codex measured
WeirdML79.3%Not measuredOnly GPT-5.3-Codex measured
ALE-Bench1655.2Not measuredOnly GPT-5.3-Codex measured
LMArena Text: codingNot measured1436Only Qwen3.5-122B-A10B measured
LMArena Text: software and ITNot measured1436Only Qwen3.5-122B-A10B measured
LMArena WebDev: React13951353GPT-5.3-Codex
LMArena WebDev: HTML14221351GPT-5.3-Codex
LMArena WebDev: from an image1443Not measuredOnly GPT-5.3-Codex measured
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GPT-5.3-Codex and Qwen3.5-122B-A10B
BenchmarkGPT-5.3-CodexQwen3.5-122B-A10BLeader
AA Intelligence Index32.517.7GPT-5.3-Codex
Epoch Capabilities Index157.1Not measuredOnly GPT-5.3-Codex measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-5.3-Codex and Qwen3.5-122B-A10B
BenchmarkGPT-5.3-CodexQwen3.5-122B-A10BLeader
Design Arena: agents building web apps1032Not measuredOnly GPT-5.3-Codex measured
Design Arena: agents building full-stack apps983Not measuredOnly GPT-5.3-Codex measured
Design Arena: agents building mobile apps1053Not measuredOnly GPT-5.3-Codex measured
Design Arena: agents building Android apps1084Not measuredOnly GPT-5.3-Codex measured
Design Arena: agents building Godot games1124Not measuredOnly GPT-5.3-Codex measured
τ²-Bench Telecom86.0%93.6%Qwen3.5-122B-A10B
METR time horizons74.5%Not measuredOnly GPT-5.3-Codex measured
Vending-Bench 2$5,940Not measuredOnly GPT-5.3-Codex measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
Reasoning benchmarks for GPT-5.3-Codex and Qwen3.5-122B-A10B
BenchmarkGPT-5.3-CodexQwen3.5-122B-A10BLeader
GPQA Diamond (Artificial Analysis run)91.5%85.7%GPT-5.3-Codex
Humanity's Last Exam (Artificial Analysis run)42.5%25.2%GPT-5.3-Codex
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
Neither model has a result on the other 14 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Math benchmarks for GPT-5.3-Codex and Qwen3.5-122B-A10B
BenchmarkGPT-5.3-CodexQwen3.5-122B-A10BLeader
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 11 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Knowledge benchmarks for GPT-5.3-Codex and Qwen3.5-122B-A10B
BenchmarkGPT-5.3-CodexQwen3.5-122B-A10BLeader
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 14 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GPT-5.3-Codex and Qwen3.5-122B-A10B
BenchmarkGPT-5.3-CodexQwen3.5-122B-A10BLeader
Design Arena: websites1162Not measuredOnly GPT-5.3-Codex measured
Design Arena: UI components1134Not measuredOnly GPT-5.3-Codex measured
Design Arena: game dev1161Not measuredOnly GPT-5.3-Codex measured
Design Arena: data viz1164Not measuredOnly GPT-5.3-Codex measured
Design Arena: 3D1007Not measuredOnly GPT-5.3-Codex measured
Design Arena: all code categories1144Not measuredOnly GPT-5.3-Codex measured
Design Arena: SVG1129Not measuredOnly GPT-5.3-Codex measured
Design Arena: ASCII art1152Not measuredOnly GPT-5.3-Codex measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
General benchmarks for GPT-5.3-Codex and Qwen3.5-122B-A10B
BenchmarkGPT-5.3-CodexQwen3.5-122B-A10BLeader
IFBench75.4%75.7%Qwen3.5-122B-A10B
Long context reasoning (AA-LCR)83.3%76.3%GPT-5.3-Codex
DTBenchNot measured84.3%Only Qwen3.5-122B-A10B measured
LMCANot measured32.2%Only Qwen3.5-122B-A10B 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 19 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-5.3-Codex and Qwen3.5-122B-A10B
BenchmarkGPT-5.3-CodexQwen3.5-122B-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-5.3-Codex$0.0088Fits 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-5.3-Codex$0.130Fits 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-5.3-Codex$0.490Fits in one request
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-5.3-Codex and Qwen3.5-122B-A10B
SpecificationOpenAI GPT-5.3-CodexQwen3.5-122B-A10B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedFebruary 24, 2026February 25, 2026
Context window400K262K
Input $/Mtok$1.75$0.26
Output $/Mtok$14.00$2.08
Coding index60.0Not benchmarked
Independent measurements behind it31
Cost per solved task$0.59Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5.3-Codex better than Qwen3.5-122B-A10B for coding?

Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; Qwen3.5-122B-A10B has none, so the two cannot be ranked on coding. Pick Qwen3.5-122B-A10B for output tokens 6.7x cheaper ($2.08 vs $14.00 per million) and work measured by τ²-Bench Telecom or IFBench, where it leads.

Which is cheaper, GPT-5.3-Codex or Qwen3.5-122B-A10B?

GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for GPT-5.3-Codex only.

Which has the bigger context window, GPT-5.3-Codex or Qwen3.5-122B-A10B?

GPT-5.3-Codex, with 400K tokens against 262K.

How many benchmarks is the GPT-5.3-Codex vs Qwen3.5-122B-A10B comparison based on?

10 benchmarks have a result for both models as of October 22, 2026. GPT-5.3-Codex's coding index rests on 3 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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