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GLM 4.5 vs Qwen3.5-35B-A3B

Qwen3.5-35B-A3B leads GLM 4.5 on the Artificial Analysis Intelligence Index, 19.3 to 12.8, and Qwen3.5-35B-A3B costs 1.7x less per output token ($1.30 vs $2.20 per million). Updated October 22, 2026; 36 benchmarks have a result for both.

Replace GLM 4.5 Replace Qwen3.5-35B-A3B
GLM 4.5Z.ai
Coding
No data
Overall
No data
Output $/M
$2.20
29
Qwen3.5-35B-A3BQwen
Coding
No data
Overall
53.5
Output $/M
$1.30
7
vs boards won of 36 shared

GLM 4.5 or Qwen3.5-35B-A3B: which to pick

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

Pick GLM 4.5 for work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.

Model AGLM 4.5Z.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-35B-A3BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
36
GLM 4.5 only
25
Qwen3.5-35B-A3B only
9
Like-for-like categories
7 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
GLM 4.5 not benchmarked, Qwen3.5-35B-A3B not benchmarked.
Widest category
Agentic work, 46.2 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.5 43.0% against Qwen3.5-35B-A3B 89.2%, 46.2 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against Qwen3.5-35B-A3B $1.30 per million output tokens, 1.7x apart.
Context window
GLM 4.5 takes 131K tokens, Qwen3.5-35B-A3B 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 4.5BarsQwen3.5-35B-A3BTies
Coding31
Intelligence01
Agentic work01
Reasoning32
Math20
Knowledge40
General172
All shared boards297
CodingLike-for-likeSix of these boards feed the Vibe Coding Index31
Coding benchmarks for GLM 4.5 and Qwen3.5-35B-A3B
BenchmarkGLM 4.5Qwen3.5-35B-A3BLeader
SWE-bench Verified64.2%Not measuredOnly GLM 4.5 measured
LMArena WebDevNot measured1254Only Qwen3.5-35B-A3B measured
LiveCodeBench (Vals AI)67.5%Not measuredOnly GLM 4.5 measured
LiveCodeBench (Artificial Analysis run)73.8%Not measuredOnly GLM 4.5 measured
Terminal-Bench Hard (Artificial Analysis run)22.0%26.5%Qwen3.5-35B-A3B
WeirdML40.6%Not measuredOnly GLM 4.5 measured
ALE-Bench344.8221.8GLM 4.5
LMArena Text: coding14341410GLM 4.5
LMArena Text: software and IT14401415GLM 4.5
LMArena WebDev: ReactNot measured1238Only Qwen3.5-35B-A3B measured
LMArena WebDev: HTMLNot measured1301Only Qwen3.5-35B-A3B 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 GLM 4.5 and Qwen3.5-35B-A3B
BenchmarkGLM 4.5Qwen3.5-35B-A3BLeader
AA Intelligence Index12.819.3Qwen3.5-35B-A3B
Epoch Capabilities IndexNot measured142.5Only Qwen3.5-35B-A3B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.5 and Qwen3.5-35B-A3B
BenchmarkGLM 4.5Qwen3.5-35B-A3BLeader
τ²-Bench Telecom43.0%89.2%Qwen3.5-35B-A3B
Vending-Bench 2Not measured$463Only Qwen3.5-35B-A3B measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index32
Reasoning benchmarks for GLM 4.5 and Qwen3.5-35B-A3B
BenchmarkGLM 4.5Qwen3.5-35B-A3BLeader
GPQA Diamond72.2%Not measuredOnly GLM 4.5 measured
GPQA Diamond (Artificial Analysis run)78.2%84.5%Qwen3.5-35B-A3B
Humanity's Last Exam (Artificial Analysis run)13.0%21.0%Qwen3.5-35B-A3B
GPQA Diamond (Epoch AI run)Not measured83.5%Only Qwen3.5-35B-A3B measured
LMArena Text: hard prompts14291400GLM 4.5
LMArena Text: expert14331408GLM 4.5
LMArena Text: hard prompts in English14291411GLM 4.5
Kagi LLM Benchmark57.9%Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for GLM 4.5 and Qwen3.5-35B-A3B
BenchmarkGLM 4.5Qwen3.5-35B-A3BLeader
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
OTIS Mock AIMENot measured70.0%Only Qwen3.5-35B-A3B measured
LMArena Text: math14271404GLM 4.5
LMArena Text: mathematical work14301410GLM 4.5
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 4.5 and Qwen3.5-35B-A3B
BenchmarkGLM 4.5Qwen3.5-35B-A3BLeader
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: science14441413GLM 4.5
LMArena Text: medicine and healthcare14551421GLM 4.5
LMArena Text: legal and government14381399GLM 4.5
LMArena Text: business and finance14201394GLM 4.5
Neither model has a result on the other 8 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 Qwen3.5-35B-A3B
BenchmarkGLM 4.5Qwen3.5-35B-A3BLeader
Design Arena: websites1169Not measuredOnly GLM 4.5 measured
Design Arena: UI components1141Not measuredOnly GLM 4.5 measured
Design Arena: game dev1144Not 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 index172
General benchmarks for GLM 4.5 and Qwen3.5-35B-A3B
BenchmarkGLM 4.5Qwen3.5-35B-A3BLeader
IFBench44.1%72.5%Qwen3.5-35B-A3B
Long context reasoning (AA-LCR)52.7%72.0%Qwen3.5-35B-A3B
DTBenchNot measured80.0%Only Qwen3.5-35B-A3B measured
LMCANot measured29.5%Only Qwen3.5-35B-A3B measured
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
LMArena Text14301395GLM 4.5
LMArena Text: instruction following14041379GLM 4.5
LMArena Text: creative writing13951347GLM 4.5
LMArena Text: English14371407GLM 4.5
LMArena Text: non-English14171378GLM 4.5
LMArena Text: Chinese14651457GLM 4.5
LMArena Text: German14071367GLM 4.5
LMArena Text: Japanese14151326GLM 4.5
LMArena Text: Korean13801355GLM 4.5
LMArena Text: French14181412GLM 4.5
LMArena Text: Spanish14541390GLM 4.5
LMArena Text: Russian14131376GLM 4.5
LMArena Text: Polish14221385GLM 4.5
LMArena Text: multi-turn14151390GLM 4.5
LMArena Text: longer queries14121389GLM 4.5
LMArena Text: writing and language14031365GLM 4.5
LMArena Text: entertainment and media14041347GLM 4.5
Neither model has a result on the other 24 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 Qwen3.5-35B-A3B
BenchmarkGLM 4.5Qwen3.5-35B-A3BLeader
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
Qwen3.5-35B-A3B$0.0008Fits in one request

Qwen3.5-35B-A3B costs less

Repository review

50K input + 3K output tokens

GLM 4.5$0.037Fits in one request
Qwen3.5-35B-A3B$0.012Fits in one request

Qwen3.5-35B-A3B costs less

Agent loop

200K input + 10K output tokens

GLM 4.5$0.142Exceeds the context window
Qwen3.5-35B-A3B$0.045Fits in one request

Qwen3.5-35B-A3B 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 Qwen3.5-35B-A3B
SpecificationZ.ai GLM 4.5Qwen3.5-35B-A3B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025February 25, 2026
Context window131K262K
Input $/Mtok$0.60$0.1625
Output $/Mtok$2.20$1.30
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 better than Qwen3.5-35B-A3B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 for work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.

Which is cheaper, GLM 4.5 or Qwen3.5-35B-A3B?

GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.5-35B-A3B costs $0.1625 and $1.30. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.5 or Qwen3.5-35B-A3B?

Qwen3.5-35B-A3B, with 262K tokens against 131K.

How many benchmarks is the GLM 4.5 vs Qwen3.5-35B-A3B comparison based on?

36 benchmarks have a result for both models as of October 22, 2026. GLM 4.5's coding index rests on 0 independent benchmarks and Qwen3.5-35B-A3B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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