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GLM 5.3 Flash vs Qwen3.6 Plus

Updated October 21, 2026. 68 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 5.3 Flash Replace Qwen3.6 Plus
GLM 5.3 FlashZ.ai
Coding
71.0
Overall
41.8
Output $/M
$0.50
61
Qwen3.6 PlusQwen
Coding
37.4
Overall
27.0
Output $/M
$1.95
6
vs boards won of 68 shared, 1 tied

Decision reading

Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 37.4 for Qwen3.6 Plus.

Pick Qwen3.6 Plus when what matters is LiveCodeBench (Vals AI) or ALE-Bench, where it leads.

Model AGLM 5.3 FlashZ.ai71.0/1002 independent benchmarks$0.02 per solved taskModel BQwen3.6 PlusQwen37.4/1001 independent benchmark$1.19 per solved task
Shared results
68
GLM 5.3 Flash only
34
Qwen3.6 Plus only
15
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 5.3 Flash 71.0 from 2 benchmarks, Qwen3.6 Plus 37.4 from 1 benchmark.
Widest category
Agentic work, 23.6 points apart on average over 4 shared benchmarks.
Widest single gap
Legal Research Bench (Vals AI): GLM 5.3 Flash 45.2% against Qwen3.6 Plus 14.9%, 30.3 points apart on a 0 to 100 scale.
Output price
GLM 5.3 Flash $0.50 against Qwen3.6 Plus $1.95 per million output tokens, 3.9x apart.
Context window
GLM 5.3 Flash takes 1.05M tokens, Qwen3.6 Plus 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 5.3 FlashBarsQwen3.6 PlusTies
Coding1121 tie
Intelligence20
Agentic work40
Reasoning82
Math40
Knowledge52
Web design80
General190
All shared boards6161 tie
CodingLike-for-likeFour of these boards feed the Vibe Coding Index112
Coding benchmarks for GLM 5.3 Flash and Qwen3.6 Plus
BenchmarkGLM 5.3 FlashQwen3.6 PlusLeader
LiveBench coding80.6%Not measuredOnly GLM 5.3 Flash measured
LMArena WebDev16071461GLM 5.3 Flash
AA Coding Index71.554.5GLM 5.3 Flash
LiveCodeBench (Vals AI)80.5%86.0%Qwen3.6 Plus
SWE-bench Verified (Vals AI)92.0%73.4%GLM 5.3 Flash
Terminal-Bench 4 (Vals AI)37.4%Not measuredOnly GLM 5.3 Flash measured
Vibe Code Bench (Vals AI)30.8%25.6%GLM 5.3 Flash
Code migration (Vals AI)20.5%11.1%GLM 5.3 Flash
ProgramBench (Vals AI)0.0%0.0%Tie
IOI (Vals AI)52.5%Not measuredOnly GLM 5.3 Flash measured
Terminal-Bench 2.1 (Vals AI)62.9%53.2%GLM 5.3 Flash
Vibe Code Bench 1-100 (Vals AI)16.0%Not measuredOnly GLM 5.3 Flash measured
SkillsBench (Vals AI)40.2%Not measuredOnly GLM 5.3 Flash measured
CyberBench (Vals AI)67.9%Not measuredOnly GLM 5.3 Flash measured
SciCode51.6%Not measuredOnly GLM 5.3 Flash measured
Terminal-Bench Hard (Artificial Analysis run)Not measured43.9%Only Qwen3.6 Plus measured
FrontierCode31.8%Not measuredOnly GLM 5.3 Flash measured
FrontierSWE18.1%Not measuredOnly GLM 5.3 Flash measured
ALE-Bench303.6670.1Qwen3.6 Plus
LMArena Text: coding15071467GLM 5.3 Flash
LMArena Text: software and IT15001462GLM 5.3 Flash
LMArena WebDev: React16241454GLM 5.3 Flash
LMArena WebDev: HTML15771463GLM 5.3 Flash
LMArena WebDev: from an image15781472GLM 5.3 Flash
Neither model has a result on the other 14 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.3 Flash and Qwen3.6 Plus
BenchmarkGLM 5.3 FlashQwen3.6 PlusLeader
AA Intelligence Index41.827.0GLM 5.3 Flash
Epoch Capabilities Index153.1147.3GLM 5.3 Flash
Agentic workLike-for-likePublic boards, shown as published, kept out of the index40
Agentic work benchmarks for GLM 5.3 Flash and Qwen3.6 Plus
BenchmarkGLM 5.3 FlashQwen3.6 PlusLeader
AA Agentic Index50.9Not measuredOnly GLM 5.3 Flash measured
LiveBench research70.1%Not measuredOnly GLM 5.3 Flash measured
LiveBench browser84.0%Not measuredOnly GLM 5.3 Flash measured
LiveBench long agentic28.4%Not measuredOnly GLM 5.3 Flash measured
SREBench (Vals AI)0.8%Not measuredOnly GLM 5.3 Flash measured
Finance Agent (Vals AI)57.9%40.9%GLM 5.3 Flash
Tax Agent Bench (Vals AI)31.6%7.7%GLM 5.3 Flash
Legal Research Bench (Vals AI)45.2%14.9%GLM 5.3 Flash
Harvey's Legal Agent Benchmark (Vals AI)10.4%Not measuredOnly GLM 5.3 Flash measured
Excel Modeling Benchmark (Vals AI)55.9%32.9%GLM 5.3 Flash
Terminal-Bench-Science (Vals AI)2.9%Not measuredOnly GLM 5.3 Flash measured
τ²-Bench TelecomNot measured97.7%Only Qwen3.6 Plus measured
APEX-Agents52.8%Not measuredOnly GLM 5.3 Flash measured
Vending-Bench 2Not measured$5,115Only Qwen3.6 Plus measured
Neither model has a result on the other 25 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index82
Reasoning benchmarks for GLM 5.3 Flash and Qwen3.6 Plus
BenchmarkGLM 5.3 FlashQwen3.6 PlusLeader
GPQA Diamond86.4%87.4%Qwen3.6 Plus
LiveBench reasoning77.6%Not measuredOnly GLM 5.3 Flash measured
MMMU Pro (Vals AI)86.0%84.2%GLM 5.3 Flash
GPQA Diamond (Artificial Analysis run)91.2%88.2%GLM 5.3 Flash
Humanity's Last Exam (Artificial Analysis run)39.9%27.8%GLM 5.3 Flash
GPQA Diamond (Epoch AI run)90.2%88.4%GLM 5.3 Flash
ARC-AGI91.0%Not measuredOnly GLM 5.3 Flash measured
ARC-AGI-265.8%Not measuredOnly GLM 5.3 Flash measured
Chess puzzles21.0%14.0%GLM 5.3 Flash
Mystery game puzzles8.0%12.0%Qwen3.6 Plus
LMArena Text: hard prompts14901448GLM 5.3 Flash
LMArena Text: expert15111454GLM 5.3 Flash
LMArena Text: hard prompts in English14881455GLM 5.3 Flash
LMArena Vision: diagrams1317Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: homework1318Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for GLM 5.3 Flash and Qwen3.6 Plus
BenchmarkGLM 5.3 FlashQwen3.6 PlusLeader
AIMENot measured94.6%Only Qwen3.6 Plus measured
OTIS Mock AIME93.9%93.3%GLM 5.3 Flash
FrontierMath, tiers 1 to 355.8%38.2%GLM 5.3 Flash
FrontierMath, tier 417.1%Not measuredOnly GLM 5.3 Flash measured
ProofBench21.0%Not measuredOnly GLM 5.3 Flash measured
LMArena Text: math15031449GLM 5.3 Flash
LMArena Text: mathematical work14971448GLM 5.3 Flash
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index52
Knowledge benchmarks for GLM 5.3 Flash and Qwen3.6 Plus
BenchmarkGLM 5.3 FlashQwen3.6 PlusLeader
MMLU Pro86.1%87.7%Qwen3.6 Plus
LegalBench (Vals AI)83.9%84.2%Qwen3.6 Plus
CaseLaw (Vals AI)Not measured51.5%Only Qwen3.6 Plus measured
CorpFin (Vals AI)Not measured61.9%Only Qwen3.6 Plus measured
TaxEval (Vals AI)75.6%74.7%GLM 5.3 Flash
MortgageTax (Vals AI)Not measured68.0%Only Qwen3.6 Plus measured
MedCode (Vals AI)Not measured36.9%Only Qwen3.6 Plus measured
SimpleQA VerifiedNot measured44.1%Only Qwen3.6 Plus measured
LMArena Text: science14841439GLM 5.3 Flash
LMArena Text: medicine and healthcare14751442GLM 5.3 Flash
LMArena Text: legal and government14731437GLM 5.3 Flash
LMArena Text: business and finance14601430GLM 5.3 Flash
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for GLM 5.3 Flash and Qwen3.6 Plus
BenchmarkGLM 5.3 FlashQwen3.6 PlusLeader
Design Arena: websites12721243GLM 5.3 Flash
Design Arena: UI components13161233GLM 5.3 Flash
Design Arena: game dev12791211GLM 5.3 Flash
Design Arena: data viz12621228GLM 5.3 Flash
Design Arena: 3D13161202GLM 5.3 Flash
Design Arena: all code categories12821236GLM 5.3 Flash
Design Arena: SVG12841160GLM 5.3 Flash
Design Arena: ASCII art12691115GLM 5.3 Flash
LiveBench design77.2%Not measuredOnly GLM 5.3 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
General benchmarks for GLM 5.3 Flash and Qwen3.6 Plus
BenchmarkGLM 5.3 FlashQwen3.6 PlusLeader
Vals Index50.9%Not measuredOnly GLM 5.3 Flash measured
LiveBench data analysis76.4%Not measuredOnly GLM 5.3 Flash measured
SAGE (Vals AI)Not measured44.9%Only Qwen3.6 Plus measured
MedScribe (Vals AI)88.9%77.0%GLM 5.3 Flash
Vals Multimodal IndexNot measured51.5%Only Qwen3.6 Plus measured
IFBenchNot measured75.2%Only Qwen3.6 Plus measured
Long context reasoning (AA-LCR)80.0%78.3%GLM 5.3 Flash
DTBenchNot measured81.9%Only Qwen3.6 Plus measured
LMCANot measured33.1%Only Qwen3.6 Plus measured
CL-benchNot measured20.3%Only Qwen3.6 Plus measured
Surface Evolver Bench52.5%Not measuredOnly GLM 5.3 Flash measured
LMArena Text14711436GLM 5.3 Flash
LMArena Text: instruction following14781424GLM 5.3 Flash
LMArena Text: creative writing14411404GLM 5.3 Flash
LMArena Vision1294Not measuredOnly GLM 5.3 Flash measured
LMArena Text: English14741445GLM 5.3 Flash
LMArena Text: non-English14621424GLM 5.3 Flash
LMArena Text: Chinese15311477GLM 5.3 Flash
LMArena Text: German14621451GLM 5.3 Flash
LMArena Text: Japanese14441390GLM 5.3 Flash
LMArena Text: Korean14361379GLM 5.3 Flash
LMArena Text: French14911455GLM 5.3 Flash
LMArena Text: Spanish14751432GLM 5.3 Flash
LMArena Text: Russian14651434GLM 5.3 Flash
LMArena Text: Polish14501433GLM 5.3 Flash
LMArena Text: multi-turn14691438GLM 5.3 Flash
LMArena Text: longer queries14811439GLM 5.3 Flash
LMArena Text: writing and language14551418GLM 5.3 Flash
LMArena Text: entertainment and media14401402GLM 5.3 Flash
LMArena Vision: English1295Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: Chinese1363Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: reading text1299Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: humor1279Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: creative writing1270Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 12 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.3 Flash and Qwen3.6 Plus
BenchmarkGLM 5.3 FlashQwen3.6 PlusLeader
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 5.3 Flash$0.0004Fits in one request
Qwen3.6 Plus$0.0013Fits in one request

GLM 5.3 Flash costs less

Repository review

50K input + 3K output tokens

GLM 5.3 Flash$0.0090Fits in one request
Qwen3.6 Plus$0.022Fits in one request

GLM 5.3 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Flash$0.035Fits in one request
Qwen3.6 Plus$0.085Fits in one request

GLM 5.3 Flash 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 5.3 Flash and Qwen3.6 Plus
SpecificationZ.ai GLM 5.3 FlashQwen3.6 Plus
MakerZ.aiQwen
WeightsOpen weightsNo public weights listed
ReleasedAugust 26, 2026April 2, 2026
Context window1.05M1M
Input $/Mtok$0.15$0.325
Output $/Mtok$0.50$1.95
Coding index71.037.4
Independent measurements behind it21
Cost per solved task$0.02$1.19
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 Flash better than Qwen3.6 Plus for coding?

Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 37.4 for Qwen3.6 Plus. Pick Qwen3.6 Plus when what matters is LiveCodeBench (Vals AI) or ALE-Bench, where it leads.

Which is cheaper, GLM 5.3 Flash or Qwen3.6 Plus?

GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; Qwen3.6 Plus costs $0.325 and $1.95. Per solved SWE-bench Verified task, GLM 5.3 Flash costs $0.02 and Qwen3.6 Plus $1.19.

Which has the bigger context window, GLM 5.3 Flash or Qwen3.6 Plus?

GLM 5.3 Flash, with 1.05M tokens against 1M.

How many benchmarks is the GLM 5.3 Flash vs Qwen3.6 Plus comparison based on?

68 benchmarks have a result for both models as of October 21, 2026. GLM 5.3 Flash's coding index rests on 2 independent benchmarks and Qwen3.6 Plus's on 1. Where only one model has a result, the page says so instead of naming a winner.

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