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GLM 5.1 vs Qwen3.8 27B

Qwen3.8 27B leads GLM 5.1 on coding, 74.2 to 49.2 on the Vibe Coding Index, and Qwen3.8 27B costs 1.7x less per output token ($2.55 vs $4.40 per million). Updated October 22, 2026; 55 benchmarks have a result for both.

Replace GLM 5.1 Replace Qwen3.8 27B
GLM 5.1Z.ai
Coding
49.2
Overall
57.5
Output $/M
$4.40
33
Qwen3.8 27BQwen
Coding
74.2
Overall
59.6
Output $/M
$2.55
21
vs boards won of 55 shared, 1 tied

GLM 5.1 or Qwen3.8 27B: which to pick

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 49.2 for GLM 5.1.

Pick GLM 5.1 for a lower cost per solved task ($0.60 against $1.26) and work measured by Code migration (Vals AI) or LMArena Text: coding, where it leads.

Model AGLM 5.1Z.ai49.2/1003 independent benchmarks$0.60 per solved taskModel BQwen3.8 27BQwen74.2/1004 independent benchmarks$1.26 per solved task
Shared results
55
GLM 5.1 only
34
Qwen3.8 27B only
30
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 5.1 49.2 from 3 benchmarks, Qwen3.8 27B 74.2 from 4 benchmarks.
Widest category
Agentic work, 10.1 points apart on average over 4 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): GLM 5.1 31.5% against Qwen3.8 27B 64.8%, 33.4 points apart on a 0 to 100 scale.
Output price
GLM 5.1 $4.40 against Qwen3.8 27B $2.55 per million output tokens, 1.7x apart.
Context window
GLM 5.1 takes 205K tokens, Qwen3.8 27B 1M.

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 5.1BarsQwen3.8 27BTies
Coding391 tie
Intelligence02
Agentic work04
Reasoning24
Math30
Knowledge80
General172
All shared boards33211 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index39
Coding benchmarks for GLM 5.1 and Qwen3.8 27B
BenchmarkGLM 5.1Qwen3.8 27BLeader
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDev15081593Qwen3.8 27B
AA Coding Index55.868.1Qwen3.8 27B
LiveCodeBench (Vals AI)81.4%84.0%Qwen3.8 27B
SWE-bench Verified (Vals AI)76.4%86.0%Qwen3.8 27B
Vibe Code Bench (Vals AI)31.5%64.8%Qwen3.8 27B
Code migration (Vals AI)25.8%14.2%GLM 5.1
ProgramBench (Vals AI)0.0%0.0%Tie
IOI (Vals AI)Not measured39.1%Only Qwen3.8 27B measured
Terminal-Bench 2.1 (Vals AI)56.9%58.4%Qwen3.8 27B
SkillsBench (Vals AI)Not measured38.1%Only Qwen3.8 27B measured
CyberBench (Vals AI)Not measured67.8%Only Qwen3.8 27B measured
SciCode44.8%46.6%Qwen3.8 27B
Terminal-Bench Hard (Artificial Analysis run)43.2%Not measuredOnly GLM 5.1 measured
WeirdML57.1%Not measuredOnly GLM 5.1 measured
ALE-Bench887.1Not measuredOnly GLM 5.1 measured
LMArena Text: coding14851483GLM 5.1
LMArena Text: software and IT14841478GLM 5.1
LMArena WebDev: React15051602Qwen3.8 27B
LMArena WebDev: HTML15081570Qwen3.8 27B
LMArena WebDev: from an imageNot measured1577Only Qwen3.8 27B measured
Neither model has a result on the other 18 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 5.1 and Qwen3.8 27B
BenchmarkGLM 5.1Qwen3.8 27BLeader
AA Intelligence Index26.133.7Qwen3.8 27B
Epoch Capabilities Index149.8151.7Qwen3.8 27B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index04
Agentic work benchmarks for GLM 5.1 and Qwen3.8 27B
BenchmarkGLM 5.1Qwen3.8 27BLeader
AA Agentic Index23.945.8Qwen3.8 27B
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 apps1182Not 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 slides1120Not measuredOnly GLM 5.1 measured
Design Arena: PowerPoint slides1163Not measuredOnly GLM 5.1 measured
Design Arena: spreadsheets1315Not measuredOnly GLM 5.1 measured
LiveBench researchNot measured59.4%Only Qwen3.8 27B measured
LiveBench browserNot measured32.0%Only Qwen3.8 27B measured
LiveBench long agenticNot measured27.9%Only Qwen3.8 27B measured
Finance Agent (Vals AI)44.8%48.5%Qwen3.8 27B
Tax Agent Bench (Vals AI)Not measured30.9%Only Qwen3.8 27B measured
Legal Research Bench (Vals AI)27.9%36.1%Qwen3.8 27B
Excel Modeling Benchmark (Vals AI)Not measured59.7%Only Qwen3.8 27B measured
Terminal-Bench-Science (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
τ²-Bench Telecom97.7%Not measuredOnly GLM 5.1 measured
APEX-Agents40.9%47.5%Qwen3.8 27B
Vending-Bench 2$5,634Not measuredOnly GLM 5.1 measured
Neither model has a result on the other 20 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index24
Reasoning benchmarks for GLM 5.1 and Qwen3.8 27B
BenchmarkGLM 5.1Qwen3.8 27BLeader
GPQA Diamond84.5%88.9%Qwen3.8 27B
LiveBench reasoningNot measured80.0%Only Qwen3.8 27B measured
MMMU Pro (Vals AI)Not measured83.9%Only Qwen3.8 27B measured
GPQA Diamond (Artificial Analysis run)86.8%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)30.1%33.9%Qwen3.8 27B
GPQA Diamond (Epoch AI run)89.9%Not measuredOnly GLM 5.1 measured
ARC-AGINot measured87.5%Only Qwen3.8 27B measured
ARC-AGI-2Not measured42.4%Only Qwen3.8 27B measured
SimpleBench55.1%Not measuredOnly GLM 5.1 measured
Chess puzzles20.0%Not measuredOnly GLM 5.1 measured
LMArena Text: hard prompts14731459GLM 5.1
LMArena Text: expert14781479Qwen3.8 27B
LMArena Text: hard prompts in English14821461GLM 5.1
LMArena Vision: diagramsNot measured1285Only Qwen3.8 27B measured
LMArena Vision: homeworkNot measured1295Only Qwen3.8 27B measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 5.1 and Qwen3.8 27B
BenchmarkGLM 5.1Qwen3.8 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%16.0%GLM 5.1
LMArena Text: math14731455GLM 5.1
LMArena Text: mathematical work14701458GLM 5.1
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
Knowledge benchmarks for GLM 5.1 and Qwen3.8 27B
BenchmarkGLM 5.1Qwen3.8 27BLeader
MMLU Pro86.9%84.3%GLM 5.1
LegalBench (Vals AI)84.4%82.4%GLM 5.1
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%70.8%GLM 5.1
MortgageTax (Vals AI)Not measured64.9%Only Qwen3.8 27B measured
MedCode (Vals AI)41.6%28.7%GLM 5.1
SimpleQA Verified34.0%Not measuredOnly GLM 5.1 measured
LMArena Text: science14781467GLM 5.1
LMArena Text: medicine and healthcare14741437GLM 5.1
LMArena Text: legal and government14651447GLM 5.1
LMArena Text: business and finance14521438GLM 5.1
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for GLM 5.1 and Qwen3.8 27B
BenchmarkGLM 5.1Qwen3.8 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: SVG1218Not measuredOnly GLM 5.1 measured
Design Arena: ASCII art1141Not measuredOnly GLM 5.1 measured
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index172
General benchmarks for GLM 5.1 and Qwen3.8 27B
BenchmarkGLM 5.1Qwen3.8 27BLeader
LiveBench data analysisNot measured76.6%Only Qwen3.8 27B measured
Public Benefits Bench (Vals AI)61.8%Not measuredOnly GLM 5.1 measured
SAGE (Vals AI)Not measured52.4%Only Qwen3.8 27B measured
MedScribe (Vals AI)72.3%83.8%Qwen3.8 27B
IFBench76.3%Not measuredOnly GLM 5.1 measured
Long context reasoning (AA-LCR)73.7%82.0%Qwen3.8 27B
DTBenchNot measured88.0%Only Qwen3.8 27B measured
LMCANot measured41.4%Only Qwen3.8 27B measured
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
ExploitBench18.1%Not measuredOnly GLM 5.1 measured
LMArena Text14631440GLM 5.1
LMArena Text: instruction following14531436GLM 5.1
LMArena Text: creative writing14531383GLM 5.1
LMArena VisionNot measured1269Only Qwen3.8 27B measured
LMArena Text: English14761448GLM 5.1
LMArena Text: non-English14481428GLM 5.1
LMArena Text: Chinese15151501GLM 5.1
LMArena Text: German14671443GLM 5.1
LMArena Text: Japanese14351387GLM 5.1
LMArena Text: Korean14241384GLM 5.1
LMArena Text: French14701465GLM 5.1
LMArena Text: Spanish14681449GLM 5.1
LMArena Text: Russian14541415GLM 5.1
LMArena Text: Polish14491439GLM 5.1
LMArena Text: multi-turn14731440GLM 5.1
LMArena Text: longer queries14671448GLM 5.1
LMArena Text: writing and language14541405GLM 5.1
LMArena Text: entertainment and media14421393GLM 5.1
LMArena Vision: EnglishNot measured1267Only Qwen3.8 27B measured
LMArena Vision: ChineseNot measured1326Only Qwen3.8 27B measured
LMArena Vision: reading textNot measured1281Only Qwen3.8 27B measured
LMArena Vision: humorNot measured1235Only Qwen3.8 27B measured
LMArena Vision: creative writingNot measured1237Only Qwen3.8 27B measured
Neither model has a result on the other 13 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 Qwen3.8 27B
BenchmarkGLM 5.1Qwen3.8 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
Qwen3.8 27B$0.0017Fits in one request

Qwen3.8 27B costs less

Repository review

50K input + 3K output tokens

GLM 5.1$0.083Fits in one request
Qwen3.8 27B$0.029Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

GLM 5.1$0.324Exceeds the context window
Qwen3.8 27B$0.111Fits in one request

Qwen3.8 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 Qwen3.8 27B
SpecificationZ.ai GLM 5.1Qwen3.8 27B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedApril 7, 2026August 14, 2026
Context window205K1M
Input $/Mtok$1.40$0.425
Output $/Mtok$4.40$2.55
Coding index49.274.2
Independent measurements behind it34
Cost per solved task$0.60$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.1 better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 49.2 for GLM 5.1. Pick GLM 5.1 for a lower cost per solved task ($0.60 against $1.26) and work measured by Code migration (Vals AI) or LMArena Text: coding, where it leads.

Which is cheaper, GLM 5.1 or Qwen3.8 27B?

GLM 5.1 costs $1.40 per million input tokens and $4.40 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Per solved SWE-bench Verified task, GLM 5.1 costs $0.60 and Qwen3.8 27B $1.26.

Which has the bigger context window, GLM 5.1 or Qwen3.8 27B?

Qwen3.8 27B, with 1M tokens against 205K.

How many benchmarks is the GLM 5.1 vs Qwen3.8 27B comparison based on?

55 benchmarks have a result for both models as of October 22, 2026. GLM 5.1's coding index rests on 3 independent benchmarks and Qwen3.8 27B's on 4. Where only one model has a result, the page says so instead of naming a winner.

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