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GLM 4.5 vs Qwen3 30B A3B

GLM 4.5 leads Qwen3 30B A3B on the Artificial Analysis Intelligence Index, 12.8 to 7.6, and Qwen3 30B A3B costs 4.4x less per output token ($0.50 vs $2.20 per million). Updated October 22, 2026; 46 benchmarks have a result for both.

Replace GLM 4.5 Replace Qwen3 30B A3B
GLM 4.5Z.ai
Coding
No data
Overall
No data
Output $/M
$2.20
45
Qwen3 30B A3BQwen
Coding
No data
Overall
38.2
Output $/M
$0.50
1
vs boards won of 46 shared

GLM 4.5 or Qwen3 30B A3B: which to pick

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

Pick GLM 4.5 for a larger context window (131K against 41K tokens) and work measured by Creative writing (Lech Mazur), where it leads.

Model AGLM 4.5Z.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3 30B A3BQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
46
GLM 4.5 only
15
Qwen3 30B A3B only
6
Like-for-like categories
8 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 30B A3B not benchmarked.
Widest category
Web design, 26.9 points apart on average over 5 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.5 52.7% against Qwen3 30B A3B 0.0%, 52.7 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against Qwen3 30B A3B $0.50 per million output tokens, 4.4x apart.
Context window
GLM 4.5 takes 131K tokens, Qwen3 30B A3B 41K.

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 30B A3BTies
Coding50
Intelligence10
Agentic work10
Reasoning50
Math40
Knowledge50
Web design50
General191
All shared boards451
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
Coding benchmarks for GLM 4.5 and Qwen3 30B A3B
BenchmarkGLM 4.5Qwen3 30B A3BLeader
SWE-bench Verified64.2%Not measuredOnly GLM 4.5 measured
LiveCodeBench (Vals AI)67.5%Not measuredOnly GLM 4.5 measured
LiveCodeBench (Artificial Analysis run)73.8%50.6%GLM 4.5
Terminal-Bench Hard (Artificial Analysis run)22.0%6.8%GLM 4.5
WeirdML40.6%29.8%GLM 4.5
ALE-Bench344.8Not measuredOnly GLM 4.5 measured
LMArena Text: coding14341337GLM 4.5
LMArena Text: software and IT14401340GLM 4.5
Neither model has a result on the other 31 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.5 and Qwen3 30B A3B
BenchmarkGLM 4.5Qwen3 30B A3BLeader
AA Intelligence Index12.87.6GLM 4.5
Epoch Capabilities IndexNot measured137.3Only Qwen3 30B A3B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.5 and Qwen3 30B A3B
BenchmarkGLM 4.5Qwen3 30B A3BLeader
τ²-Bench Telecom43.0%26.0%GLM 4.5
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for GLM 4.5 and Qwen3 30B A3B
BenchmarkGLM 4.5Qwen3 30B A3BLeader
GPQA Diamond72.2%Not measuredOnly GLM 4.5 measured
GPQA Diamond (Artificial Analysis run)78.2%61.6%GLM 4.5
Humanity's Last Exam (Artificial Analysis run)13.0%6.2%GLM 4.5
GPQA Diamond (Epoch AI run)Not measured61.7%Only Qwen3 30B A3B measured
LMArena Text: hard prompts14291314GLM 4.5
LMArena Text: expert14331314GLM 4.5
LMArena Text: hard prompts in English14291329GLM 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 index40
Math benchmarks for GLM 4.5 and Qwen3 30B A3B
BenchmarkGLM 4.5Qwen3 30B 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 Index73.772.3GLM 4.5
AIME 2025 (Artificial Analysis run)73.7%72.3%GLM 4.5
OTIS Mock AIMENot measured62.8%Only Qwen3 30B A3B measured
LMArena Text: math14271355GLM 4.5
LMArena Text: mathematical work14301363GLM 4.5
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for GLM 4.5 and Qwen3 30B A3B
BenchmarkGLM 4.5Qwen3 30B 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%77.7%GLM 4.5
LMArena Text: science14441321GLM 4.5
LMArena Text: medicine and healthcare14551318GLM 4.5
LMArena Text: legal and government14381314GLM 4.5
LMArena Text: business and finance14201309GLM 4.5
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index50
Web design benchmarks for GLM 4.5 and Qwen3 30B A3B
BenchmarkGLM 4.5Qwen3 30B A3BLeader
Design Arena: websites1169954GLM 4.5
Design Arena: UI components1141943GLM 4.5
Design Arena: game dev1144898GLM 4.5
Design Arena: data viz1159967GLM 4.5
Design Arena: 3D1170Not measuredOnly GLM 4.5 measured
Design Arena: all code categories1165941GLM 4.5
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 index191
General benchmarks for GLM 4.5 and Qwen3 30B A3B
BenchmarkGLM 4.5Qwen3 30B A3BLeader
IFBench44.1%41.5%GLM 4.5
Long context reasoning (AA-LCR)52.7%0.0%GLM 4.5
DTBenchNot measured60.3%Only Qwen3 30B A3B measured
LMCANot measured15.8%Only Qwen3 30B A3B measured
Fiction.LiveBenchNot measured40.6%Only Qwen3 30B A3B measured
Creative writing (Lech Mazur)73.4%75.3%Qwen3 30B A3B
LMArena Text14301317GLM 4.5
LMArena Text: instruction following14041283GLM 4.5
LMArena Text: creative writing13951271GLM 4.5
LMArena Text: English14371335GLM 4.5
LMArena Text: non-English14171295GLM 4.5
LMArena Text: Chinese14651347GLM 4.5
LMArena Text: German14071307GLM 4.5
LMArena Text: Japanese14151254GLM 4.5
LMArena Text: Korean13801261GLM 4.5
LMArena Text: French14181352GLM 4.5
LMArena Text: Spanish14541317GLM 4.5
LMArena Text: Russian14131291GLM 4.5
LMArena Text: Polish14221299GLM 4.5
LMArena Text: multi-turn14151307GLM 4.5
LMArena Text: longer queries14121312GLM 4.5
LMArena Text: writing and language14031290GLM 4.5
LMArena Text: entertainment and media14041258GLM 4.5
Neither model has a result on the other 23 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 30B A3B
BenchmarkGLM 4.5Qwen3 30B 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 30B A3B$0.0004Fits in one request

Qwen3 30B A3B costs less

Repository review

50K input + 3K output tokens

GLM 4.5$0.037Fits in one request
Qwen3 30B A3B$0.0075Exceeds the context window

Qwen3 30B A3B costs less

Agent loop

200K input + 10K output tokens

GLM 4.5$0.142Exceeds the context window
Qwen3 30B A3B$0.029Exceeds the context window

Qwen3 30B 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 30B A3B
SpecificationZ.ai GLM 4.5Qwen3 30B A3B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025April 28, 2025
Context window131K41K
Input $/Mtok$0.60$0.12
Output $/Mtok$2.20$0.50
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 better than Qwen3 30B A3B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 for a larger context window (131K against 41K tokens) and work measured by Creative writing (Lech Mazur), where it leads.

Which is cheaper, GLM 4.5 or Qwen3 30B A3B?

GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3 30B A3B costs $0.12 and $0.50. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.5 or Qwen3 30B A3B?

GLM 4.5, with 131K tokens against 41K.

How many benchmarks is the GLM 4.5 vs Qwen3 30B A3B comparison based on?

46 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 30B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.

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