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GLM 4.7 vs Qwen3.5 397B A17B

GLM 4.7 leads Qwen3.5 397B A17B on the Artificial Analysis Intelligence Index, 22.2 to 21.4, at about the same output price ($2.20 vs $2.34 per million). Updated October 22, 2026; 49 benchmarks have a result for both.

Replace GLM 4.7 Replace Qwen3.5 397B A17B
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
20
Qwen3.5 397B A17BQwen
Coding
No data
Overall
57.1
Output $/M
$2.34
29
vs boards won of 49 shared

GLM 4.7 or Qwen3.5 397B A17B: which to pick

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Qwen3.5 397B A17B has none, so the two cannot be ranked on coding.

Pick Qwen3.5 397B A17B for a larger context window (262K against 205K tokens) and work measured by Terminal-Bench Hard (Artificial Analysis run) or ALE-Bench, where it leads.

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BQwen3.5 397B A17BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
49
GLM 4.7 only
32
Qwen3.5 397B A17B only
16
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, Qwen3.5 397B A17B not benchmarked.
Widest category
Math, 5.8 points apart on average over 3 shared benchmarks.
Widest single gap
IFBench: GLM 4.7 67.9% against Qwen3.5 397B A17B 78.8%, 10.9 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against Qwen3.5 397B A17B $2.34 per million output tokens, 1.1x apart.
Context window
GLM 4.7 takes 205K tokens, Qwen3.5 397B A17B 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 397B A17BTies
Coding34
Intelligence11
Agentic work10
Reasoning15
Math03
Knowledge31
Web design70
General415
All shared boards2029
CodingLike-for-likeSix of these boards feed the Vibe Coding Index34
Coding benchmarks for GLM 4.7 and Qwen3.5 397B A17B
BenchmarkGLM 4.7Qwen3.5 397B A17BLeader
LMArena WebDev14351400GLM 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
SciCodeNot measured44.8%Only Qwen3.5 397B A17B measured
Terminal-Bench Hard (Artificial Analysis run)31.8%40.9%Qwen3.5 397B A17B
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
ALE-Bench399.5621.9Qwen3.5 397B A17B
LMArena Text: coding14541464Qwen3.5 397B A17B
LMArena Text: software and IT14531463Qwen3.5 397B A17B
LMArena WebDev: React14201388GLM 4.7
LMArena WebDev: HTML14471410GLM 4.7
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
Intelligence benchmarks for GLM 4.7 and Qwen3.5 397B A17B
BenchmarkGLM 4.7Qwen3.5 397B A17BLeader
AA Intelligence Index22.221.4GLM 4.7
Epoch Capabilities Index143.6146.8Qwen3.5 397B A17B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 and Qwen3.5 397B A17B
BenchmarkGLM 4.7Qwen3.5 397B A17BLeader
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%95.6%GLM 4.7
APEX-AgentsNot measured24.9%Only Qwen3.5 397B A17B measured
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 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index15
Reasoning benchmarks for GLM 4.7 and Qwen3.5 397B A17B
BenchmarkGLM 4.7Qwen3.5 397B A17BLeader
GPQA Diamond80.0%Not measuredOnly GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)85.9%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)27.4%29.0%Qwen3.5 397B A17B
GPQA Diamond (Epoch AI run)83.3%86.4%Qwen3.5 397B A17B
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
Mystery game puzzlesNot measured18.0%Only Qwen3.5 397B A17B measured
LMArena Text: hard prompts14431448Qwen3.5 397B A17B
LMArena Text: expert14241462Qwen3.5 397B A17B
LMArena Text: hard prompts in English14521450GLM 4.7
LMArena Vision: diagramsNot measured1277Only Qwen3.5 397B A17B measured
LMArena Vision: homeworkNot measured1292Only Qwen3.5 397B A17B measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GLM 4.7 and Qwen3.5 397B A17B
BenchmarkGLM 4.7Qwen3.5 397B A17BLeader
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%88.9%Qwen3.5 397B A17B
FrontierMath, tiers 1 to 3Not measured31.2%Only Qwen3.5 397B A17B measured
ProofBench6.0%Not measuredOnly GLM 4.7 measured
LMArena Text: math14241453Qwen3.5 397B A17B
LMArena Text: mathematical work14111454Qwen3.5 397B A17B
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
Knowledge benchmarks for GLM 4.7 and Qwen3.5 397B A17B
BenchmarkGLM 4.7Qwen3.5 397B A17BLeader
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: science14571457GLM 4.7
LMArena Text: medicine and healthcare14691455GLM 4.7
LMArena Text: legal and government14441441GLM 4.7
LMArena Text: business and finance14291435Qwen3.5 397B A17B
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index70
Web design benchmarks for GLM 4.7 and Qwen3.5 397B A17B
BenchmarkGLM 4.7Qwen3.5 397B A17BLeader
Design Arena: websites12251190GLM 4.7
Design Arena: UI components11971161GLM 4.7
Design Arena: game dev11881140GLM 4.7
Design Arena: data viz11961175GLM 4.7
Design Arena: 3D11901160GLM 4.7
Design Arena: all code categories12161179GLM 4.7
Design Arena: SVG11421139GLM 4.7
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 index415
General benchmarks for GLM 4.7 and Qwen3.5 397B A17B
BenchmarkGLM 4.7Qwen3.5 397B A17BLeader
MedScribe (Vals AI)68.6%Not measuredOnly GLM 4.7 measured
IFBench67.9%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)71.0%77.3%Qwen3.5 397B A17B
DTBenchNot measured87.5%Only Qwen3.5 397B A17B measured
LMCANot measured37.9%Only Qwen3.5 397B A17B measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351437Qwen3.5 397B A17B
LMArena Text: instruction following14121424Qwen3.5 397B A17B
LMArena Text: creative writing14011402Qwen3.5 397B A17B
LMArena VisionNot measured1262Only Qwen3.5 397B A17B measured
LMArena Text: English14531443GLM 4.7
LMArena Text: non-English14171430Qwen3.5 397B A17B
LMArena Text: Chinese14951498Qwen3.5 397B A17B
LMArena Text: German14261445Qwen3.5 397B A17B
LMArena Text: Japanese14391429GLM 4.7
LMArena Text: Korean13991384GLM 4.7
LMArena Text: French14321462Qwen3.5 397B A17B
LMArena Text: Spanish14341441Qwen3.5 397B A17B
LMArena Text: Russian14231430Qwen3.5 397B A17B
LMArena Text: Polish14351447Qwen3.5 397B A17B
LMArena Text: multi-turn14461446Qwen3.5 397B A17B
LMArena Text: longer queries14321441Qwen3.5 397B A17B
LMArena Text: writing and language14111415Qwen3.5 397B A17B
LMArena Text: entertainment and media14001399GLM 4.7
LMArena Vision: EnglishNot measured1268Only Qwen3.5 397B A17B measured
LMArena Vision: ChineseNot measured1314Only Qwen3.5 397B A17B measured
LMArena Vision: reading textNot measured1271Only Qwen3.5 397B A17B measured
LMArena Vision: humorNot measured1262Only Qwen3.5 397B A17B measured
LMArena Vision: captioningNot measured1230Only Qwen3.5 397B A17B measured
LMArena Vision: recognizing thingsNot measured1265Only Qwen3.5 397B A17B measured
LMArena Vision: creative writingNot measured1260Only Qwen3.5 397B A17B measured
Neither model has a result on the other 14 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 397B A17B
BenchmarkGLM 4.7Qwen3.5 397B A17BLeader
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 397B A17B$0.0016Fits in one request

Qwen3.5 397B A17B costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.037Fits in one request
Qwen3.5 397B A17B$0.027Fits in one request

Qwen3.5 397B A17B costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.142Exceeds the context window
Qwen3.5 397B A17B$0.101Fits in one request

Qwen3.5 397B A17B 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 397B A17B
SpecificationZ.ai GLM 4.7Qwen3.5 397B A17B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025February 16, 2026
Context window205K262K
Input $/Mtok$0.60$0.39
Output $/Mtok$2.20$2.34
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 397B A17B for coding?

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Qwen3.5 397B A17B has none, so the two cannot be ranked on coding. Pick Qwen3.5 397B A17B for a larger context window (262K against 205K tokens) and work measured by Terminal-Bench Hard (Artificial Analysis run) or ALE-Bench, where it leads.

Which is cheaper, GLM 4.7 or Qwen3.5 397B A17B?

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, GLM 4.7 or Qwen3.5 397B A17B?

Qwen3.5 397B A17B, with 262K tokens against 205K.

How many benchmarks is the GLM 4.7 vs Qwen3.5 397B A17B comparison based on?

49 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 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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