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GLM 5.1 vs Qwen3.5 397B A17B

Updated October 21, 2026. 52 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 5.1 Replace Qwen3.5 397B A17B
GLM 5.1Z.ai
Coding
44.0
Overall
26.1
Output $/M
$4.40
48
Qwen3.5 397B A17BQwen
Coding
28.6
Overall
21.4
Output $/M
$2.34
3
vs boards won of 52 shared, 1 tied

Decision reading

Pick GLM 5.1 for coding: it scores 44.0 on the Vibe Coding Index against 28.6 for Qwen3.5 397B A17B.

Pick Qwen3.5 397B A17B when output tokens cost 1.9x less, or when the context window is larger, 262K against 205K tokens.

Model AGLM 5.1Z.ai44.0/1001 independent benchmark$0.60 per solved taskModel BQwen3.5 397B A17BQwen28.6/1001 independent benchmarkNo cost per solved task
Shared results
52
GLM 5.1 only
37
Qwen3.5 397B A17B only
13
Like-for-like categories
8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 5.1 44.0 from 1 benchmark, Qwen3.5 397B A17B 28.6 from 1 benchmark.
Widest category
Web design, 15.8 points apart on average over 7 shared benchmarks.
Widest single gap
Design Arena: data viz: GLM 5.1 1366 against Qwen3.5 397B A17B 1175, 23.9 points apart on a 0 to 100 scale.
Output price
GLM 5.1 $4.40 against Qwen3.5 397B A17B $2.34 per million output tokens, 1.9x apart.
Context window
GLM 5.1 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 5.1BarsQwen3.5 397B A17BTies
Coding701 tie
Intelligence20
Agentic work20
Reasoning51
Math40
Knowledge40
Web design70
General172
All shared boards4831 tie
CodingLike-for-likeFour of these boards feed the Vibe Coding Index70
Coding benchmarks for GLM 5.1 and Qwen3.5 397B A17B
BenchmarkGLM 5.1Qwen3.5 397B A17BLeader
LMArena WebDev15081400GLM 5.1
AA Coding Index55.8Not measuredOnly GLM 5.1 measured
LiveCodeBench (Vals AI)81.4%Not measuredOnly GLM 5.1 measured
SWE-bench Verified (Vals AI)76.4%Not measuredOnly GLM 5.1 measured
Vibe Code Bench (Vals AI)31.5%Not measuredOnly GLM 5.1 measured
Code migration (Vals AI)25.8%Not measuredOnly GLM 5.1 measured
ProgramBench (Vals AI)0.0%Not measuredOnly GLM 5.1 measured
Terminal-Bench 2.1 (Vals AI)56.9%Not measuredOnly GLM 5.1 measured
SciCode44.8%44.8%Tie
Terminal-Bench Hard (Artificial Analysis run)43.2%40.9%GLM 5.1
WeirdML57.1%Not measuredOnly GLM 5.1 measured
ALE-Bench887.1621.9GLM 5.1
LMArena Text: coding14851464GLM 5.1
LMArena Text: software and IT14841463GLM 5.1
LMArena WebDev: React15051388GLM 5.1
LMArena WebDev: HTML15081410GLM 5.1
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.1 and Qwen3.5 397B A17B
BenchmarkGLM 5.1Qwen3.5 397B A17BLeader
AA Intelligence Index26.121.4GLM 5.1
Epoch Capabilities Index149.8146.8GLM 5.1
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for GLM 5.1 and Qwen3.5 397B A17B
BenchmarkGLM 5.1Qwen3.5 397B A17BLeader
AA Agentic Index23.9Not measuredOnly GLM 5.1 measured
Design Arena: agents building web apps1175Not measuredOnly GLM 5.1 measured
Design Arena: agents building full-stack apps1167Not measuredOnly GLM 5.1 measured
Design Arena: agents building mobile apps1162Not measuredOnly GLM 5.1 measured
Design Arena: agents building Android apps1186Not measuredOnly GLM 5.1 measured
Design Arena: agents building Godot games1053Not measuredOnly GLM 5.1 measured
Design Arena: agents building games1164Not measuredOnly GLM 5.1 measured
Design Arena: HTML slides1170Not measuredOnly GLM 5.1 measured
Design Arena: PowerPoint slides1189Not measuredOnly GLM 5.1 measured
Design Arena: spreadsheets1315Not measuredOnly GLM 5.1 measured
Finance Agent (Vals AI)44.8%Not measuredOnly GLM 5.1 measured
Legal Research Bench (Vals AI)27.9%Not measuredOnly GLM 5.1 measured
τ²-Bench Telecom97.7%95.6%GLM 5.1
APEX-Agents40.9%24.9%GLM 5.1
Vending-Bench 2$5,634Not measuredOnly GLM 5.1 measured
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
Reasoning benchmarks for GLM 5.1 and Qwen3.5 397B A17B
BenchmarkGLM 5.1Qwen3.5 397B A17BLeader
GPQA Diamond84.5%Not measuredOnly GLM 5.1 measured
GPQA Diamond (Artificial Analysis run)86.8%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)30.1%29.0%GLM 5.1
GPQA Diamond (Epoch AI run)89.9%86.4%GLM 5.1
SimpleBench55.1%Not measuredOnly GLM 5.1 measured
Chess puzzles20.0%Not measuredOnly GLM 5.1 measured
Mystery game puzzlesNot measured18.0%Only Qwen3.5 397B A17B measured
LMArena Text: hard prompts14731448GLM 5.1
LMArena Text: expert14781462GLM 5.1
LMArena Text: hard prompts in English14821450GLM 5.1
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 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for GLM 5.1 and Qwen3.5 397B A17B
BenchmarkGLM 5.1Qwen3.5 397B A17BLeader
AIME91.9%Not measuredOnly GLM 5.1 measured
OTIS Mock AIME93.3%88.9%GLM 5.1
FrontierMath, tiers 1 to 336.8%31.2%GLM 5.1
ProofBench22.2%Not measuredOnly GLM 5.1 measured
LMArena Text: math14731453GLM 5.1
LMArena Text: mathematical work14701454GLM 5.1
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 5.1 and Qwen3.5 397B A17B
BenchmarkGLM 5.1Qwen3.5 397B A17BLeader
MMLU Pro86.9%Not measuredOnly GLM 5.1 measured
LegalBench (Vals AI)84.4%Not measuredOnly GLM 5.1 measured
CaseLaw (Vals AI)51.5%Not measuredOnly GLM 5.1 measured
CorpFin (Vals AI)64.5%Not measuredOnly GLM 5.1 measured
TaxEval (Vals AI)71.2%Not measuredOnly GLM 5.1 measured
MedCode (Vals AI)41.6%Not measuredOnly GLM 5.1 measured
SimpleQA Verified34.0%Not measuredOnly GLM 5.1 measured
LMArena Text: science14781457GLM 5.1
LMArena Text: medicine and healthcare14741455GLM 5.1
LMArena Text: legal and government14651441GLM 5.1
LMArena Text: business and finance14521435GLM 5.1
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index70
Web design benchmarks for GLM 5.1 and Qwen3.5 397B A17B
BenchmarkGLM 5.1Qwen3.5 397B A17BLeader
Design Arena: websites12901191GLM 5.1
Design Arena: UI components12921163GLM 5.1
Design Arena: game dev12651141GLM 5.1
Design Arena: data viz13661175GLM 5.1
Design Arena: 3D13361161GLM 5.1
Design Arena: all code categories12681180GLM 5.1
Design Arena: SVG12171139GLM 5.1
Design Arena: ASCII art1142Not measuredOnly GLM 5.1 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 5.1 and Qwen3.5 397B A17B
BenchmarkGLM 5.1Qwen3.5 397B A17BLeader
Public Benefits Bench (Vals AI)61.8%Not measuredOnly GLM 5.1 measured
MedScribe (Vals AI)72.3%Not measuredOnly GLM 5.1 measured
IFBench76.3%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)73.7%77.3%Qwen3.5 397B A17B
DTBenchNot measured87.5%Only Qwen3.5 397B A17B measured
LMCANot measured37.9%Only Qwen3.5 397B A17B measured
ExploitBench18.1%Not measuredOnly GLM 5.1 measured
LMArena Text14631437GLM 5.1
LMArena Text: instruction following14531424GLM 5.1
LMArena Text: creative writing14531402GLM 5.1
LMArena VisionNot measured1262Only Qwen3.5 397B A17B measured
LMArena Text: English14761443GLM 5.1
LMArena Text: non-English14481430GLM 5.1
LMArena Text: Chinese15151498GLM 5.1
LMArena Text: German14671445GLM 5.1
LMArena Text: Japanese14351429GLM 5.1
LMArena Text: Korean14241384GLM 5.1
LMArena Text: French14701462GLM 5.1
LMArena Text: Spanish14681441GLM 5.1
LMArena Text: Russian14541430GLM 5.1
LMArena Text: Polish14491447GLM 5.1
LMArena Text: multi-turn14731446GLM 5.1
LMArena Text: longer queries14671441GLM 5.1
LMArena Text: writing and language14541415GLM 5.1
LMArena Text: entertainment and media14421399GLM 5.1
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 5.1 and Qwen3.5 397B A17B
BenchmarkGLM 5.1Qwen3.5 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 5.1$0.0036Fits 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 5.1$0.083Fits 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 5.1$0.324Exceeds 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 5.1 and Qwen3.5 397B A17B
SpecificationZ.ai GLM 5.1Qwen3.5 397B A17B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedApril 7, 2026February 16, 2026
Context window205K262K
Input $/Mtok$1.40$0.39
Output $/Mtok$4.40$2.34
Coding index44.028.6
Independent measurements behind it11
Cost per solved task$0.60Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.1 better than Qwen3.5 397B A17B for coding?

Pick GLM 5.1 for coding: it scores 44.0 on the Vibe Coding Index against 28.6 for Qwen3.5 397B A17B. Pick Qwen3.5 397B A17B when output tokens cost 1.9x less, or when the context window is larger, 262K against 205K tokens.

Which is cheaper, GLM 5.1 or Qwen3.5 397B A17B?

GLM 5.1 costs $1.40 per million input tokens and $4.40 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for GLM 5.1 only.

Which has the bigger context window, GLM 5.1 or Qwen3.5 397B A17B?

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

How many benchmarks is the GLM 5.1 vs Qwen3.5 397B A17B comparison based on?

52 benchmarks have a result for both models as of October 21, 2026. GLM 5.1's coding index rests on 1 independent benchmark 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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