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GLM 4.7 vs Qwen3.5-35B-A3B

GLM 4.7 leads Qwen3.5-35B-A3B on the Artificial Analysis Intelligence Index, 22.2 to 19.3, and Qwen3.5-35B-A3B costs 1.7x less per output token ($1.30 vs $2.20 per million). Updated October 22, 2026; 43 benchmarks have a result for both.

Replace GLM 4.7 Replace Qwen3.5-35B-A3B
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
40
Qwen3.5-35B-A3BQwen
Coding
No data
Overall
53.5
Output $/M
$1.30
3
vs boards won of 43 shared

GLM 4.7 or Qwen3.5-35B-A3B: which to pick

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Qwen3.5-35B-A3B has none, so the two cannot be ranked on coding.

Pick Qwen3.5-35B-A3B for output tokens 1.7x cheaper ($1.30 vs $2.20 per million) and a larger context window (262K against 205K tokens).

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BQwen3.5-35B-A3BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
43
GLM 4.7 only
38
Qwen3.5-35B-A3B only
2
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 4.7 55.1 from 2 benchmarks, Qwen3.5-35B-A3B not benchmarked.
Widest category
Coding, 15.1 points apart on average over 7 shared benchmarks.
Widest single gap
LMArena WebDev: React: GLM 4.7 1420 against Qwen3.5-35B-A3B 1238, 30.3 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against Qwen3.5-35B-A3B $1.30 per million output tokens, 1.7x apart.
Context window
GLM 4.7 takes 205K 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 4.7BarsQwen3.5-35B-A3BTies
Coding70
Intelligence20
Agentic work20
Reasoning51
Math30
Knowledge40
General172
All shared boards403
CodingLike-for-likeSix of these boards feed the Vibe Coding Index70
Coding benchmarks for GLM 4.7 and Qwen3.5-35B-A3B
BenchmarkGLM 4.7Qwen3.5-35B-A3BLeader
LMArena WebDev14351254GLM 4.7
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%Not measuredOnly GLM 4.7 measured
Terminal-Bench Hard (Artificial Analysis run)31.8%26.5%GLM 4.7
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
ALE-Bench399.5221.8GLM 4.7
LMArena Text: coding14541410GLM 4.7
LMArena Text: software and IT14531415GLM 4.7
LMArena WebDev: React14201238GLM 4.7
LMArena WebDev: HTML14471301GLM 4.7
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 4.7 and Qwen3.5-35B-A3B
BenchmarkGLM 4.7Qwen3.5-35B-A3BLeader
AA Intelligence Index22.219.3GLM 4.7
Epoch Capabilities Index143.6142.5GLM 4.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for GLM 4.7 and Qwen3.5-35B-A3B
BenchmarkGLM 4.7Qwen3.5-35B-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 apps1113Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
τ²-Bench Telecom95.9%89.2%GLM 4.7
Vending-Bench 2$2,377$463GLM 4.7
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 index51
Reasoning benchmarks for GLM 4.7 and Qwen3.5-35B-A3B
BenchmarkGLM 4.7Qwen3.5-35B-A3BLeader
GPQA Diamond80.0%Not measuredOnly GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)85.9%84.5%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)27.4%21.0%GLM 4.7
GPQA Diamond (Epoch AI run)83.3%83.5%Qwen3.5-35B-A3B
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts14431400GLM 4.7
LMArena Text: expert14241408GLM 4.7
LMArena Text: hard prompts in English14521411GLM 4.7
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 4.7 and Qwen3.5-35B-A3B
BenchmarkGLM 4.7Qwen3.5-35B-A3BLeader
AIME93.3%Not measuredOnly GLM 4.7 measured
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.0Not measuredOnly GLM 4.7 measured
AIME 2025 (Artificial Analysis run)95.0%Not measuredOnly GLM 4.7 measured
OTIS Mock AIME83.3%70.0%GLM 4.7
ProofBench6.0%Not measuredOnly GLM 4.7 measured
LMArena Text: math14241404GLM 4.7
LMArena Text: mathematical work14111410GLM 4.7
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 4.7 and Qwen3.5-35B-A3B
BenchmarkGLM 4.7Qwen3.5-35B-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%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571413GLM 4.7
LMArena Text: medicine and healthcare14691421GLM 4.7
LMArena Text: legal and government14441399GLM 4.7
LMArena Text: business and finance14291394GLM 4.7
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 4.7 and Qwen3.5-35B-A3B
BenchmarkGLM 4.7Qwen3.5-35B-A3BLeader
Design Arena: websites1225Not measuredOnly GLM 4.7 measured
Design Arena: UI components1197Not measuredOnly GLM 4.7 measured
Design Arena: game dev1188Not measuredOnly GLM 4.7 measured
Design Arena: data viz1196Not measuredOnly GLM 4.7 measured
Design Arena: 3D1190Not measuredOnly GLM 4.7 measured
Design Arena: all code categories1216Not measuredOnly GLM 4.7 measured
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 index172
General benchmarks for GLM 4.7 and Qwen3.5-35B-A3B
BenchmarkGLM 4.7Qwen3.5-35B-A3BLeader
MedScribe (Vals AI)68.6%Not measuredOnly GLM 4.7 measured
IFBench67.9%72.5%Qwen3.5-35B-A3B
Long context reasoning (AA-LCR)71.0%72.0%Qwen3.5-35B-A3B
DTBenchNot measured80.0%Only Qwen3.5-35B-A3B measured
LMCANot measured29.5%Only Qwen3.5-35B-A3B measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351395GLM 4.7
LMArena Text: instruction following14121379GLM 4.7
LMArena Text: creative writing14011347GLM 4.7
LMArena Text: English14531407GLM 4.7
LMArena Text: non-English14171378GLM 4.7
LMArena Text: Chinese14951457GLM 4.7
LMArena Text: German14261367GLM 4.7
LMArena Text: Japanese14391326GLM 4.7
LMArena Text: Korean13991355GLM 4.7
LMArena Text: French14321412GLM 4.7
LMArena Text: Spanish14341390GLM 4.7
LMArena Text: Russian14231376GLM 4.7
LMArena Text: Polish14351385GLM 4.7
LMArena Text: multi-turn14461390GLM 4.7
LMArena Text: longer queries14321389GLM 4.7
LMArena Text: writing and language14111365GLM 4.7
LMArena Text: entertainment and media14001347GLM 4.7
Neither model has a result on the other 22 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 Qwen3.5-35B-A3B
BenchmarkGLM 4.7Qwen3.5-35B-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 4.7$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 4.7$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 4.7$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 4.7 and Qwen3.5-35B-A3B
SpecificationZ.ai GLM 4.7Qwen3.5-35B-A3B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025February 25, 2026
Context window205K262K
Input $/Mtok$0.60$0.1625
Output $/Mtok$2.20$1.30
Coding index55.1Not benchmarked
Independent measurements behind it21
Cost per solved task$0.29Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than Qwen3.5-35B-A3B for coding?

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Qwen3.5-35B-A3B has none, so the two cannot be ranked on coding. Pick Qwen3.5-35B-A3B for output tokens 1.7x cheaper ($1.30 vs $2.20 per million) and a larger context window (262K against 205K tokens).

Which is cheaper, GLM 4.7 or Qwen3.5-35B-A3B?

GLM 4.7 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 GLM 4.7 only.

Which has the bigger context window, GLM 4.7 or Qwen3.5-35B-A3B?

Qwen3.5-35B-A3B, with 262K tokens against 205K.

How many benchmarks is the GLM 4.7 vs Qwen3.5-35B-A3B comparison based on?

43 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and Qwen3.5-35B-A3B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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