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GLM 5.1 vs Qwen3.5-27B

Updated October 21, 2026. 41 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 Qwen3.5-27B
GLM 5.1Z.ai
Coding
44.0
Overall
26.1
Output $/M
$4.40
40
Qwen3.5-27BQwen
Coding
22.6
Overall
22.9
Output $/M
$2.16
1
vs boards won of 41 shared

Decision reading

Pick GLM 5.1 for coding: it scores 44.0 on the Vibe Coding Index against 22.6 for Qwen3.5-27B.

Pick Qwen3.5-27B when output tokens cost 2.0x 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 BQwen3.5-27BQwen22.6/1001 independent benchmarkNo cost per solved task
Shared results
41
GLM 5.1 only
48
Qwen3.5-27B only
12
Like-for-like categories
7 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, Qwen3.5-27B 22.6 from 1 benchmark.
Widest category
Agentic work, 18.9 points apart on average over 2 shared benchmarks.
Widest single gap
Vending-Bench 2: GLM 5.1 $5,634 against Qwen3.5-27B $202, 34.0 points apart on a 0 to 100 scale.
Output price
GLM 5.1 $4.40 against Qwen3.5-27B $2.16 per million output tokens, 2.0x apart.
Context window
GLM 5.1 takes 205K tokens, Qwen3.5-27B 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 5.1BarsQwen3.5-27BTies
Coding80
Intelligence10
Agentic work20
Reasoning50
Math20
Knowledge40
General181
All shared boards401
CodingLike-for-likeFour of these boards feed the Vibe Coding Index80
Coding benchmarks for GLM 5.1 and Qwen3.5-27B
BenchmarkGLM 5.1Qwen3.5-27BLeader
LMArena WebDev15081358GLM 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%Not measuredOnly GLM 5.1 measured
Terminal-Bench Hard (Artificial Analysis run)43.2%32.6%GLM 5.1
WeirdML57.1%39.5%GLM 5.1
ALE-Bench887.1349.4GLM 5.1
LMArena Text: coding14851427GLM 5.1
LMArena Text: software and IT14841431GLM 5.1
LMArena WebDev: React15051346GLM 5.1
LMArena WebDev: HTML15081386GLM 5.1
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 5.1 and Qwen3.5-27B
BenchmarkGLM 5.1Qwen3.5-27BLeader
AA Intelligence Index26.122.9GLM 5.1
Epoch Capabilities Index149.8Not measuredOnly GLM 5.1 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for GLM 5.1 and Qwen3.5-27B
BenchmarkGLM 5.1Qwen3.5-27BLeader
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%93.9%GLM 5.1
APEX-Agents40.9%Not measuredOnly GLM 5.1 measured
Vending-Bench 2$5,634$202GLM 5.1
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for GLM 5.1 and Qwen3.5-27B
BenchmarkGLM 5.1Qwen3.5-27BLeader
GPQA Diamond84.5%Not measuredOnly GLM 5.1 measured
GPQA Diamond (Artificial Analysis run)86.8%85.8%GLM 5.1
Humanity's Last Exam (Artificial Analysis run)30.1%23.9%GLM 5.1
GPQA Diamond (Epoch AI run)89.9%Not measuredOnly GLM 5.1 measured
SimpleBench55.1%Not measuredOnly GLM 5.1 measured
Chess puzzles20.0%Not measuredOnly GLM 5.1 measured
LMArena Text: hard prompts14731415GLM 5.1
LMArena Text: expert14781427GLM 5.1
LMArena Text: hard prompts in English14821429GLM 5.1
LMArena Vision: diagramsNot measured1249Only Qwen3.5-27B measured
LMArena Vision: homeworkNot measured1256Only Qwen3.5-27B measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for GLM 5.1 and Qwen3.5-27B
BenchmarkGLM 5.1Qwen3.5-27BLeader
AIME91.9%Not measuredOnly GLM 5.1 measured
OTIS Mock AIME93.3%Not measuredOnly GLM 5.1 measured
FrontierMath, tiers 1 to 336.8%Not measuredOnly GLM 5.1 measured
ProofBench22.2%Not measuredOnly GLM 5.1 measured
LMArena Text: math14731429GLM 5.1
LMArena Text: mathematical work14701429GLM 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 Qwen3.5-27B
BenchmarkGLM 5.1Qwen3.5-27BLeader
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: science14781437GLM 5.1
LMArena Text: medicine and healthcare14741425GLM 5.1
LMArena Text: legal and government14651405GLM 5.1
LMArena Text: business and finance14521399GLM 5.1
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 5.1 and Qwen3.5-27B
BenchmarkGLM 5.1Qwen3.5-27BLeader
Design Arena: websites1290Not measuredOnly GLM 5.1 measured
Design Arena: UI components1292Not measuredOnly GLM 5.1 measured
Design Arena: game dev1265Not measuredOnly GLM 5.1 measured
Design Arena: data viz1366Not measuredOnly GLM 5.1 measured
Design Arena: 3D1336Not measuredOnly GLM 5.1 measured
Design Arena: all code categories1268Not measuredOnly GLM 5.1 measured
Design Arena: SVG1217Not measuredOnly GLM 5.1 measured
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 index181
General benchmarks for GLM 5.1 and Qwen3.5-27B
BenchmarkGLM 5.1Qwen3.5-27BLeader
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%75.6%GLM 5.1
Long context reasoning (AA-LCR)73.7%77.7%Qwen3.5-27B
DTBenchNot measured82.4%Only Qwen3.5-27B measured
LMCANot measured34.0%Only Qwen3.5-27B measured
ExploitBench18.1%Not measuredOnly GLM 5.1 measured
LMArena Text14631409GLM 5.1
LMArena Text: instruction following14531393GLM 5.1
LMArena Text: creative writing14531362GLM 5.1
LMArena VisionNot measured1241Only Qwen3.5-27B measured
LMArena Text: English14761425GLM 5.1
LMArena Text: non-English14481390GLM 5.1
LMArena Text: Chinese15151478GLM 5.1
LMArena Text: German14671392GLM 5.1
LMArena Text: Japanese14351346GLM 5.1
LMArena Text: Korean14241359GLM 5.1
LMArena Text: French14701410GLM 5.1
LMArena Text: Spanish14681407GLM 5.1
LMArena Text: Russian14541390GLM 5.1
LMArena Text: Polish14491375GLM 5.1
LMArena Text: multi-turn14731410GLM 5.1
LMArena Text: longer queries14671413GLM 5.1
LMArena Text: writing and language14541380GLM 5.1
LMArena Text: entertainment and media14421363GLM 5.1
LMArena Vision: EnglishNot measured1246Only Qwen3.5-27B measured
LMArena Vision: ChineseNot measured1301Only Qwen3.5-27B measured
LMArena Vision: reading textNot measured1246Only Qwen3.5-27B measured
LMArena Vision: humorNot measured1227Only Qwen3.5-27B measured
LMArena Vision: captioningNot measured1170Only Qwen3.5-27B measured
LMArena Vision: recognizing thingsNot measured1210Only Qwen3.5-27B measured
LMArena Vision: creative writingNot measured1224Only Qwen3.5-27B 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 Qwen3.5-27B
BenchmarkGLM 5.1Qwen3.5-27BLeader
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
Qwen3.5-27B$0.0014Fits in one request

Qwen3.5-27B costs less

Repository review

50K input + 3K output tokens

GLM 5.1$0.083Fits in one request
Qwen3.5-27B$0.020Fits in one request

Qwen3.5-27B costs less

Agent loop

200K input + 10K output tokens

GLM 5.1$0.324Exceeds the context window
Qwen3.5-27B$0.076Fits in one request

Qwen3.5-27B 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 Qwen3.5-27B
SpecificationZ.ai GLM 5.1Qwen3.5-27B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedApril 7, 2026February 25, 2026
Context window205K262K
Input $/Mtok$1.40$0.27
Output $/Mtok$4.40$2.16
Coding index44.022.6
Independent measurements behind it11
Cost per solved task$0.60Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.1 better than Qwen3.5-27B for coding?

Pick GLM 5.1 for coding: it scores 44.0 on the Vibe Coding Index against 22.6 for Qwen3.5-27B. Pick Qwen3.5-27B when output tokens cost 2.0x less, or when the context window is larger, 262K against 205K tokens.

Which is cheaper, GLM 5.1 or Qwen3.5-27B?

GLM 5.1 costs $1.40 per million input tokens and $4.40 per million output tokens; Qwen3.5-27B costs $0.27 and $2.16. Cost per solved task is known for GLM 5.1 only.

Which has the bigger context window, GLM 5.1 or Qwen3.5-27B?

Qwen3.5-27B, with 262K tokens against 205K.

How many benchmarks is the GLM 5.1 vs Qwen3.5-27B comparison based on?

41 benchmarks have a result for both models as of October 21, 2026. GLM 5.1's coding index rests on 1 independent benchmark and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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