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GLM 4.5 vs Qwen3.5-122B-A10B

Updated October 21, 2026. 35 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.5 Replace Qwen3.5-122B-A10B
GLM 4.5Z.ai
Coding
64.2
Overall
12.8
Output $/M
$2.20
21
Qwen3.5-122B-A10BQwen
Coding
22.9
Overall
17.7
Output $/M
$2.08
14
vs boards won of 35 shared

Decision reading

Pick GLM 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 22.9 for Qwen3.5-122B-A10B.

Pick Qwen3.5-122B-A10B when output tokens cost 1.1x less, or when the context window is larger, 262K against 131K tokens.

Model AGLM 4.5Z.ai64.2/1001 independent benchmarkNo cost per solved taskModel BQwen3.5-122B-A10BQwen22.9/1001 independent benchmarkNo cost per solved task
Shared results
35
GLM 4.5 only
25
Qwen3.5-122B-A10B only
16
Like-for-like categories
7 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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
GLM 4.5 64.2 from 1 benchmark, Qwen3.5-122B-A10B 22.9 from 1 benchmark.
Widest category
Agentic work, 50.6 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.5 43.0% against Qwen3.5-122B-A10B 93.6%, 50.6 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against Qwen3.5-122B-A10B $2.08 per million output tokens, 1.1x apart.
Context window
GLM 4.5 takes 131K 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
CategoryGLM 4.5BarsQwen3.5-122B-A10BTies
Coding12
Intelligence01
Agentic work01
Reasoning23
Math02
Knowledge40
General145
All shared boards2114
CodingLike-for-likeFour of these boards feed the Vibe Coding Index12
Coding benchmarks for GLM 4.5 and Qwen3.5-122B-A10B
BenchmarkGLM 4.5Qwen3.5-122B-A10BLeader
SWE-bench Verified64.2%Not measuredOnly GLM 4.5 measured
LMArena WebDevNot measured1361Only Qwen3.5-122B-A10B measured
AA Coding IndexNot measured43.3Only Qwen3.5-122B-A10B measured
LiveCodeBench (Vals AI)67.5%Not measuredOnly GLM 4.5 measured
LiveCodeBench (Artificial Analysis run)73.8%Not measuredOnly GLM 4.5 measured
SciCodeNot measured39.7%Only Qwen3.5-122B-A10B measured
Terminal-Bench Hard (Artificial Analysis run)22.0%31.1%Qwen3.5-122B-A10B
WeirdML40.6%Not measuredOnly GLM 4.5 measured
ALE-Bench344.8Not measuredOnly GLM 4.5 measured
LMArena Text: coding14341436Qwen3.5-122B-A10B
LMArena Text: software and IT14401436GLM 4.5
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 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.5 and Qwen3.5-122B-A10B
BenchmarkGLM 4.5Qwen3.5-122B-A10BLeader
AA Intelligence Index12.817.7Qwen3.5-122B-A10B
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.5 and Qwen3.5-122B-A10B
BenchmarkGLM 4.5Qwen3.5-122B-A10BLeader
τ²-Bench Telecom43.0%93.6%Qwen3.5-122B-A10B
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index23
Reasoning benchmarks for GLM 4.5 and Qwen3.5-122B-A10B
BenchmarkGLM 4.5Qwen3.5-122B-A10BLeader
GPQA Diamond72.2%Not measuredOnly GLM 4.5 measured
GPQA Diamond (Artificial Analysis run)78.2%85.7%Qwen3.5-122B-A10B
Humanity's Last Exam (Artificial Analysis run)13.0%25.2%Qwen3.5-122B-A10B
Mystery game puzzlesNot measured17.0%Only Qwen3.5-122B-A10B measured
LMArena Text: hard prompts14291421GLM 4.5
LMArena Text: expert14331431GLM 4.5
LMArena Text: hard prompts in English14291431Qwen3.5-122B-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 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 4.5 and Qwen3.5-122B-A10B
BenchmarkGLM 4.5Qwen3.5-122B-A10BLeader
AIME86.7%Not measuredOnly GLM 4.5 measured
MATH 500 (Vals AI)94.0%Not measuredOnly GLM 4.5 measured
MGSM (Vals AI)90.8%Not measuredOnly GLM 4.5 measured
AA Math Index73.7Not measuredOnly GLM 4.5 measured
AIME 2025 (Artificial Analysis run)73.7%Not measuredOnly GLM 4.5 measured
LMArena Text: math14271431Qwen3.5-122B-A10B
LMArena Text: mathematical work14301431Qwen3.5-122B-A10B
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 4.5 and Qwen3.5-122B-A10B
BenchmarkGLM 4.5Qwen3.5-122B-A10BLeader
MMLU Pro81.2%Not measuredOnly GLM 4.5 measured
LegalBench (Vals AI)75.6%Not measuredOnly GLM 4.5 measured
CorpFin (Vals AI)61.0%Not measuredOnly GLM 4.5 measured
TaxEval (Vals AI)72.4%Not measuredOnly GLM 4.5 measured
MedQA (Vals AI)90.0%Not measuredOnly GLM 4.5 measured
MMLU Pro (Artificial Analysis run)83.5%Not measuredOnly GLM 4.5 measured
LMArena Text: science14441432GLM 4.5
LMArena Text: medicine and healthcare14551423GLM 4.5
LMArena Text: legal and government14381422GLM 4.5
LMArena Text: business and finance14201414GLM 4.5
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.5 and Qwen3.5-122B-A10B
BenchmarkGLM 4.5Qwen3.5-122B-A10BLeader
Design Arena: websites1170Not measuredOnly GLM 4.5 measured
Design Arena: UI components1144Not measuredOnly GLM 4.5 measured
Design Arena: game dev1145Not measuredOnly GLM 4.5 measured
Design Arena: data viz1159Not measuredOnly GLM 4.5 measured
Design Arena: 3D1170Not measuredOnly GLM 4.5 measured
Design Arena: all code categories1165Not measuredOnly GLM 4.5 measured
Design Arena: SVG1096Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index145
General benchmarks for GLM 4.5 and Qwen3.5-122B-A10B
BenchmarkGLM 4.5Qwen3.5-122B-A10BLeader
IFBench44.1%75.7%Qwen3.5-122B-A10B
Long context reasoning (AA-LCR)52.7%76.3%Qwen3.5-122B-A10B
DTBenchNot measured84.3%Only Qwen3.5-122B-A10B measured
LMCANot measured32.2%Only Qwen3.5-122B-A10B measured
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
LMArena Text14301417GLM 4.5
LMArena Text: instruction following14041399GLM 4.5
LMArena Text: creative writing13951368GLM 4.5
LMArena VisionNot measured1244Only Qwen3.5-122B-A10B measured
LMArena Text: English14371428GLM 4.5
LMArena Text: non-English14171401GLM 4.5
LMArena Text: Chinese14651463GLM 4.5
LMArena Text: German14071425Qwen3.5-122B-A10B
LMArena Text: Japanese14151367GLM 4.5
LMArena Text: Korean13801353GLM 4.5
LMArena Text: French14181443Qwen3.5-122B-A10B
LMArena Text: Spanish14541425GLM 4.5
LMArena Text: Russian14131400GLM 4.5
LMArena Text: Polish14221398GLM 4.5
LMArena Text: multi-turn14151416Qwen3.5-122B-A10B
LMArena Text: longer queries14121410GLM 4.5
LMArena Text: writing and language14031387GLM 4.5
LMArena Text: entertainment and media14041366GLM 4.5
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 GLM 4.5 and Qwen3.5-122B-A10B
BenchmarkGLM 4.5Qwen3.5-122B-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

GLM 4.5$0.0017Fits 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

GLM 4.5$0.037Fits 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

GLM 4.5$0.142Exceeds 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 GLM 4.5 and Qwen3.5-122B-A10B
SpecificationZ.ai GLM 4.5Qwen3.5-122B-A10B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025February 25, 2026
Context window131K262K
Input $/Mtok$0.60$0.26
Output $/Mtok$2.20$2.08
Coding index64.222.9
Independent measurements behind it11
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 better than Qwen3.5-122B-A10B for coding?

Pick GLM 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 22.9 for Qwen3.5-122B-A10B. Pick Qwen3.5-122B-A10B when output tokens cost 1.1x less, or when the context window is larger, 262K against 131K tokens.

Which is cheaper, GLM 4.5 or Qwen3.5-122B-A10B?

GLM 4.5 costs $0.60 per million input tokens and $2.20 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, GLM 4.5 or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B, with 262K tokens against 131K.

How many benchmarks is the GLM 4.5 vs Qwen3.5-122B-A10B comparison based on?

35 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 1 independent benchmark 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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