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GLM 4.7 vs Qwen3 30B A3B

GLM 4.7 leads Qwen3 30B A3B on the Artificial Analysis Intelligence Index, 22.2 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; 47 benchmarks have a result for both.

Replace GLM 4.7 Replace Qwen3 30B A3B
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
47
Qwen3 30B A3BQwen
Coding
No data
Overall
38.2
Output $/M
$0.50
0
vs boards won of 47 shared

GLM 4.7 or Qwen3 30B A3B: which to pick

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Qwen3 30B A3B has none, so the two cannot be ranked on coding.

Pick Qwen3 30B A3B for output tokens 4.4x cheaper ($0.50 vs $2.20 per million).

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BQwen3 30B A3BQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
47
GLM 4.7 only
34
Qwen3 30B A3B only
5
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 4.7 55.1 from 2 benchmarks, Qwen3 30B A3B not benchmarked.
Widest category
Agentic work, 69.9 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.7 71.0% against Qwen3 30B A3B 0.0%, 71.0 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against Qwen3 30B A3B $0.50 per million output tokens, 4.4x apart.
Context window
GLM 4.7 takes 205K 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 4.7BarsQwen3 30B A3BTies
Coding40
Intelligence20
Agentic work10
Reasoning60
Math50
Knowledge50
Web design50
General190
All shared boards470
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for GLM 4.7 and Qwen3 30B A3B
BenchmarkGLM 4.7Qwen3 30B A3BLeader
LMArena WebDev1435Not measuredOnly GLM 4.7 measured
AA Coding Index45.3Not measuredOnly GLM 4.7 measured
LiveCodeBench (Vals AI)82.2%Not measuredOnly GLM 4.7 measured
SWE-bench Verified (Vals AI)69.4%Not measuredOnly GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)89.4%50.6%GLM 4.7
Terminal-Bench Hard (Artificial Analysis run)31.8%6.8%GLM 4.7
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
WeirdMLNot measured29.8%Only Qwen3 30B A3B measured
ALE-Bench399.5Not measuredOnly GLM 4.7 measured
LMArena Text: coding14541337GLM 4.7
LMArena Text: software and IT14531340GLM 4.7
LMArena WebDev: React1420Not measuredOnly GLM 4.7 measured
LMArena WebDev: HTML1447Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 4.7 and Qwen3 30B A3B
BenchmarkGLM 4.7Qwen3 30B A3BLeader
AA Intelligence Index22.27.6GLM 4.7
Epoch Capabilities Index143.6137.3GLM 4.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 and Qwen3 30B A3B
BenchmarkGLM 4.7Qwen3 30B A3BLeader
Design Arena: agents building full-stack apps1050Not measuredOnly GLM 4.7 measured
Design Arena: agents building mobile apps1105Not measuredOnly GLM 4.7 measured
Design Arena: agents building Android apps1112Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
τ²-Bench Telecom95.9%26.0%GLM 4.7
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
BrowseComp-Plus64.7%Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 4.7 and Qwen3 30B A3B
BenchmarkGLM 4.7Qwen3 30B A3BLeader
GPQA Diamond80.0%Not measuredOnly GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)85.9%61.6%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)27.4%6.2%GLM 4.7
GPQA Diamond (Epoch AI run)83.3%61.7%GLM 4.7
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts14431314GLM 4.7
LMArena Text: expert14241314GLM 4.7
LMArena Text: hard prompts in English14521329GLM 4.7
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index50
Math benchmarks for GLM 4.7 and Qwen3 30B A3B
BenchmarkGLM 4.7Qwen3 30B A3BLeader
AIME93.3%Not measuredOnly GLM 4.7 measured
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.072.3GLM 4.7
AIME 2025 (Artificial Analysis run)95.0%72.3%GLM 4.7
OTIS Mock AIME83.3%62.8%GLM 4.7
ProofBench6.0%Not measuredOnly GLM 4.7 measured
LMArena Text: math14241355GLM 4.7
LMArena Text: mathematical work14111363GLM 4.7
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 4.7 and Qwen3 30B A3B
BenchmarkGLM 4.7Qwen3 30B A3BLeader
MMLU Pro82.7%Not measuredOnly GLM 4.7 measured
LegalBench (Vals AI)83.4%Not measuredOnly GLM 4.7 measured
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%Not measuredOnly GLM 4.7 measured
TaxEval (Vals AI)68.8%Not measuredOnly GLM 4.7 measured
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%Not measuredOnly GLM 4.7 measured
MMLU Pro (Artificial Analysis run)85.6%77.7%GLM 4.7
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571321GLM 4.7
LMArena Text: medicine and healthcare14691318GLM 4.7
LMArena Text: legal and government14441314GLM 4.7
LMArena Text: business and finance14291309GLM 4.7
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index50
Web design benchmarks for GLM 4.7 and Qwen3 30B A3B
BenchmarkGLM 4.7Qwen3 30B A3BLeader
Design Arena: websites1225954GLM 4.7
Design Arena: UI components1196943GLM 4.7
Design Arena: game dev1188898GLM 4.7
Design Arena: data viz1195967GLM 4.7
Design Arena: 3D1190Not measuredOnly GLM 4.7 measured
Design Arena: all code categories1216941GLM 4.7
Design Arena: SVG1142Not measuredOnly GLM 4.7 measured
Design Arena: ASCII art1165Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
General benchmarks for GLM 4.7 and Qwen3 30B A3B
BenchmarkGLM 4.7Qwen3 30B A3BLeader
MedScribe (Vals AI)68.6%Not measuredOnly GLM 4.7 measured
IFBench67.9%41.5%GLM 4.7
Long context reasoning (AA-LCR)71.0%0.0%GLM 4.7
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)Not measured75.3%Only Qwen3 30B A3B measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351317GLM 4.7
LMArena Text: instruction following14121283GLM 4.7
LMArena Text: creative writing14011271GLM 4.7
LMArena Text: English14531335GLM 4.7
LMArena Text: non-English14171295GLM 4.7
LMArena Text: Chinese14951347GLM 4.7
LMArena Text: German14261307GLM 4.7
LMArena Text: Japanese14391254GLM 4.7
LMArena Text: Korean13991261GLM 4.7
LMArena Text: French14321352GLM 4.7
LMArena Text: Spanish14341317GLM 4.7
LMArena Text: Russian14231291GLM 4.7
LMArena Text: Polish14351299GLM 4.7
LMArena Text: multi-turn14461307GLM 4.7
LMArena Text: longer queries14321312GLM 4.7
LMArena Text: writing and language14111290GLM 4.7
LMArena Text: entertainment and media14001258GLM 4.7
Neither model has a result on the other 20 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 and Qwen3 30B A3B
BenchmarkGLM 4.7Qwen3 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 4.7$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 4.7$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 4.7$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 4.7 and Qwen3 30B A3B
SpecificationZ.ai GLM 4.7Qwen3 30B A3B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025April 28, 2025
Context window205K41K
Input $/Mtok$0.60$0.12
Output $/Mtok$2.20$0.50
Coding index55.1Not benchmarked
Independent measurements behind it20
Cost per solved task$0.29Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than Qwen3 30B A3B for coding?

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Qwen3 30B A3B has none, so the two cannot be ranked on coding. Pick Qwen3 30B A3B for output tokens 4.4x cheaper ($0.50 vs $2.20 per million).

Which is cheaper, GLM 4.7 or Qwen3 30B A3B?

GLM 4.7 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 GLM 4.7 only.

Which has the bigger context window, GLM 4.7 or Qwen3 30B A3B?

GLM 4.7, with 205K tokens against 41K.

How many benchmarks is the GLM 4.7 vs Qwen3 30B A3B comparison based on?

47 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 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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