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GLM 4.6V vs Qwen3.8 27B

Qwen3.8 27B leads GLM 4.6V on the Artificial Analysis Intelligence Index, 33.7 to 11.2, and GLM 4.6V costs 2.8x less per output token ($0.90 vs $2.55 per million). Updated October 22, 2026; 31 benchmarks have a result for both.

Replace GLM 4.6V Replace Qwen3.8 27B
GLM 4.6VZ.ai
Coding
No data
Overall
No data
Output $/M
$0.90
0
Qwen3.8 27BQwen
Coding
74.2
Overall
59.6
Output $/M
$2.55
31
vs boards won of 31 shared

GLM 4.6V or Qwen3.8 27B: which to pick

Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; GLM 4.6V has none, so the two cannot be ranked on coding.

Pick GLM 4.6V for output tokens 2.8x cheaper ($0.90 vs $2.55 per million).

Model AGLM 4.6VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.8 27BQwen74.2/1004 independent benchmarks$1.26 per solved task
Shared results
31
GLM 4.6V only
7
Qwen3.8 27B only
54
Like-for-like categories
5 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 4.6V not benchmarked, Qwen3.8 27B 74.2 from 4 benchmarks.
Widest category
Intelligence, 22.5 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.6V 48.7% against Qwen3.8 27B 82.0%, 33.3 points apart on a 0 to 100 scale.
Output price
GLM 4.6V $0.90 against Qwen3.8 27B $2.55 per million output tokens, 2.8x apart.
Context window
GLM 4.6V takes 131K 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 4.6VBarsQwen3.8 27BTies
Coding02
Intelligence01
Reasoning07
Knowledge04
General017
All shared boards031
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for GLM 4.6V and Qwen3.8 27B
BenchmarkGLM 4.6VQwen3.8 27BLeader
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDevNot measured1593Only Qwen3.8 27B measured
AA Coding IndexNot measured68.1Only Qwen3.8 27B measured
LiveCodeBench (Vals AI)Not measured84.0%Only Qwen3.8 27B measured
SWE-bench Verified (Vals AI)Not measured86.0%Only Qwen3.8 27B measured
Vibe Code Bench (Vals AI)Not measured64.8%Only Qwen3.8 27B measured
Code migration (Vals AI)Not measured14.2%Only Qwen3.8 27B measured
ProgramBench (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
IOI (Vals AI)Not measured39.1%Only Qwen3.8 27B measured
Terminal-Bench 2.1 (Vals AI)Not measured58.4%Only Qwen3.8 27B measured
SkillsBench (Vals AI)Not measured38.1%Only Qwen3.8 27B measured
CyberBench (Vals AI)Not measured67.8%Only Qwen3.8 27B measured
LiveCodeBench (Artificial Analysis run)41.1%Not measuredOnly GLM 4.6V measured
SciCodeNot measured46.6%Only Qwen3.8 27B measured
Terminal-Bench Hard (Artificial Analysis run)14.4%Not measuredOnly GLM 4.6V measured
LMArena Text: coding13891483Qwen3.8 27B
LMArena Text: software and IT13911478Qwen3.8 27B
LMArena WebDev: ReactNot measured1602Only Qwen3.8 27B measured
LMArena WebDev: HTMLNot measured1570Only Qwen3.8 27B measured
LMArena WebDev: from an imageNot measured1577Only Qwen3.8 27B measured
Neither model has a result on the other 19 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6V and Qwen3.8 27B
BenchmarkGLM 4.6VQwen3.8 27BLeader
AA Intelligence Index11.233.7Qwen3.8 27B
Epoch Capabilities IndexNot measured151.7Only Qwen3.8 27B measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
Agentic work benchmarks for GLM 4.6V and Qwen3.8 27B
BenchmarkGLM 4.6VQwen3.8 27BLeader
AA Agentic IndexNot measured45.8Only Qwen3.8 27B 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)Not measured48.5%Only Qwen3.8 27B measured
Tax Agent Bench (Vals AI)Not measured30.9%Only Qwen3.8 27B measured
Legal Research Bench (Vals AI)Not measured36.1%Only Qwen3.8 27B measured
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 Telecom31.6%Not measuredOnly GLM 4.6V measured
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
Neither model has a result on the other 30 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for GLM 4.6V and Qwen3.8 27B
BenchmarkGLM 4.6VQwen3.8 27BLeader
GPQA DiamondNot measured88.9%Only Qwen3.8 27B measured
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)71.9%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)9.6%33.9%Qwen3.8 27B
ARC-AGINot measured87.5%Only Qwen3.8 27B measured
ARC-AGI-2Not measured42.4%Only Qwen3.8 27B measured
LMArena Text: hard prompts13671459Qwen3.8 27B
LMArena Text: expert13711479Qwen3.8 27B
LMArena Text: hard prompts in English13911461Qwen3.8 27B
LMArena Vision: diagrams11821285Qwen3.8 27B
LMArena Vision: homework11551295Qwen3.8 27B
Neither model has a result on the other 10 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Math benchmarks for GLM 4.6V and Qwen3.8 27B
BenchmarkGLM 4.6VQwen3.8 27BLeader
AA Math Index85.3Not measuredOnly GLM 4.6V measured
AIME 2025 (Artificial Analysis run)85.3%Not measuredOnly GLM 4.6V measured
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
LMArena Text: mathNot measured1455Only Qwen3.8 27B measured
LMArena Text: mathematical workNot measured1458Only Qwen3.8 27B measured
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 4.6V and Qwen3.8 27B
BenchmarkGLM 4.6VQwen3.8 27BLeader
MMLU ProNot measured84.3%Only Qwen3.8 27B measured
LegalBench (Vals AI)Not measured82.4%Only Qwen3.8 27B measured
TaxEval (Vals AI)Not measured70.8%Only Qwen3.8 27B measured
MortgageTax (Vals AI)Not measured64.9%Only Qwen3.8 27B measured
MedCode (Vals AI)Not measured28.7%Only Qwen3.8 27B measured
MMLU Pro (Artificial Analysis run)79.9%Not measuredOnly GLM 4.6V measured
LMArena Text: science13891467Qwen3.8 27B
LMArena Text: medicine and healthcare13891437Qwen3.8 27B
LMArena Text: legal and government13931447Qwen3.8 27B
LMArena Text: business and finance13591438Qwen3.8 27B
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Web design benchmarks for GLM 4.6V and Qwen3.8 27B
BenchmarkGLM 4.6VQwen3.8 27BLeader
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
Neither model has a result on the other 8 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
General benchmarks for GLM 4.6V and Qwen3.8 27B
BenchmarkGLM 4.6VQwen3.8 27BLeader
LiveBench data analysisNot measured76.6%Only Qwen3.8 27B measured
SAGE (Vals AI)Not measured52.4%Only Qwen3.8 27B measured
MedScribe (Vals AI)Not measured83.8%Only Qwen3.8 27B measured
IFBench30.1%Not measuredOnly GLM 4.6V measured
Long context reasoning (AA-LCR)48.7%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
LMArena Text13761440Qwen3.8 27B
LMArena Text: instruction following13501436Qwen3.8 27B
LMArena Text: creative writing13471383Qwen3.8 27B
LMArena Vision11641269Qwen3.8 27B
LMArena Text: English13951448Qwen3.8 27B
LMArena Text: non-English13581428Qwen3.8 27B
LMArena Text: Chinese14251501Qwen3.8 27B
LMArena Text: GermanNot measured1443Only Qwen3.8 27B measured
LMArena Text: JapaneseNot measured1387Only Qwen3.8 27B measured
LMArena Text: KoreanNot measured1384Only Qwen3.8 27B measured
LMArena Text: FrenchNot measured1465Only Qwen3.8 27B measured
LMArena Text: SpanishNot measured1449Only Qwen3.8 27B measured
LMArena Text: Russian13391415Qwen3.8 27B
LMArena Text: PolishNot measured1439Only Qwen3.8 27B measured
LMArena Text: multi-turn13591440Qwen3.8 27B
LMArena Text: longer queries13571448Qwen3.8 27B
LMArena Text: writing and language13471405Qwen3.8 27B
LMArena Text: entertainment and media13431393Qwen3.8 27B
LMArena Vision: English11791267Qwen3.8 27B
LMArena Vision: Chinese12171326Qwen3.8 27B
LMArena Vision: reading text11711281Qwen3.8 27B
LMArena Vision: humorNot measured1235Only Qwen3.8 27B measured
LMArena Vision: creative writing11131237Qwen3.8 27B
Neither model has a result on the other 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6V and Qwen3.8 27B
BenchmarkGLM 4.6VQwen3.8 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 4.6V$0.0008Fits in one request
Qwen3.8 27B$0.0017Fits in one request

GLM 4.6V costs less

Repository review

50K input + 3K output tokens

GLM 4.6V$0.018Fits in one request
Qwen3.8 27B$0.029Fits in one request

GLM 4.6V costs less

Agent loop

200K input + 10K output tokens

GLM 4.6V$0.069Exceeds the context window
Qwen3.8 27B$0.111Fits in one request

GLM 4.6V 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 4.6V and Qwen3.8 27B
SpecificationZ.ai GLM 4.6VQwen3.8 27B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedDecember 8, 2025August 14, 2026
Context window131K1M
Input $/Mtok$0.30$0.425
Output $/Mtok$0.90$2.55
Coding indexNot benchmarked74.2
Independent measurements behind it04
Cost per solved taskNot computable$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6V better than Qwen3.8 27B for coding?

Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; GLM 4.6V has none, so the two cannot be ranked on coding. Pick GLM 4.6V for output tokens 2.8x cheaper ($0.90 vs $2.55 per million).

Which is cheaper, GLM 4.6V or Qwen3.8 27B?

GLM 4.6V costs $0.30 per million input tokens and $0.90 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Cost per solved task is known for Qwen3.8 27B only.

Which has the bigger context window, GLM 4.6V or Qwen3.8 27B?

Qwen3.8 27B, with 1M tokens against 131K.

How many benchmarks is the GLM 4.6V vs Qwen3.8 27B comparison based on?

31 benchmarks have a result for both models as of October 22, 2026. GLM 4.6V's coding index rests on 0 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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