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GLM 5.3 vs Qwen3.5-Flash

Updated October 21, 2026. 49 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 Replace Qwen3.5-Flash
GLM 5.3Z.ai
Coding
73.1
Overall
44.8
Output $/M
$1.40
48
Qwen3.5-FlashQwen
Coding
6.2
Overall
No data
Output $/M
$0.26
1
vs boards won of 49 shared

Decision reading

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 6.2 for Qwen3.5-Flash.

Pick Qwen3.5-Flash when output tokens cost 5.4x less, or when the benchmark that matters is LiveCodeBench (Vals AI), where it leads.

Model AGLM 5.3Z.ai73.1/1002 independent benchmarks$0.36 per solved taskModel BQwen3.5-FlashQwen6.2/1001 independent benchmark$0.40 per solved task
Shared results
49
GLM 5.3 only
51
Qwen3.5-Flash only
6
Like-for-like categories
6 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 73.1 from 2 benchmarks, Qwen3.5-Flash 6.2 from 1 benchmark.
Widest category
Coding, 32.8 points apart on average over 8 shared benchmarks.
Widest single gap
LMArena WebDev: React: GLM 5.3 1641 against Qwen3.5-Flash 1229, 61.8 points apart on a 0 to 100 scale.
Output price
GLM 5.3 $1.40 against Qwen3.5-Flash $0.26 per million output tokens, 5.4x apart.
Context window
GLM 5.3 takes 1.05M tokens, Qwen3.5-Flash 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 5.3BarsQwen3.5-FlashTies
Coding71
Intelligence10
Reasoning70
Math40
Knowledge90
General200
All shared boards481
CodingLike-for-likeFour of these boards feed the Vibe Coding Index71
Coding benchmarks for GLM 5.3 and Qwen3.5-Flash
BenchmarkGLM 5.3Qwen3.5-FlashLeader
LiveBench coding82.8%Not measuredOnly GLM 5.3 measured
LMArena WebDev16211244GLM 5.3
AA Coding Index74.8Not measuredOnly GLM 5.3 measured
LiveCodeBench (Vals AI)80.5%83.3%Qwen3.5-Flash
SWE-bench Verified (Vals AI)95.4%64.4%GLM 5.3
Terminal-Bench 4 (Vals AI)38.9%Not measuredOnly GLM 5.3 measured
Vibe Code Bench (Vals AI)78.1%Not measuredOnly GLM 5.3 measured
Code migration (Vals AI)44.2%Not measuredOnly GLM 5.3 measured
ProgramBench (Vals AI)1.5%Not measuredOnly GLM 5.3 measured
IOI (Vals AI)68.4%Not measuredOnly GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)71.5%Not measuredOnly GLM 5.3 measured
Vibe Code Bench 1-100 (Vals AI)20.0%Not measuredOnly GLM 5.3 measured
SkillsBench (Vals AI)47.5%Not measuredOnly GLM 5.3 measured
CyberBench (Vals AI)72.1%Not measuredOnly GLM 5.3 measured
SciCode59.0%Not measuredOnly GLM 5.3 measured
FrontierCode40.1%Not measuredOnly GLM 5.3 measured
FrontierSWE30.2%Not measuredOnly GLM 5.3 measured
WeirdML75.4%Not measuredOnly GLM 5.3 measured
ALE-Bench1317.4265.9GLM 5.3
LMArena Text: coding14971412GLM 5.3
LMArena Text: software and IT14931416GLM 5.3
LMArena WebDev: React16411229GLM 5.3
LMArena WebDev: HTML15951291GLM 5.3
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 5.3 and Qwen3.5-Flash
BenchmarkGLM 5.3Qwen3.5-FlashLeader
AA Intelligence Index44.8Not measuredOnly GLM 5.3 measured
Epoch Capabilities Index156.0143.5GLM 5.3
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index16 boards, none shared
Agentic work benchmarks for GLM 5.3 and Qwen3.5-Flash
BenchmarkGLM 5.3Qwen3.5-FlashLeader
AA Agentic Index53.1Not measuredOnly GLM 5.3 measured
Design Arena: agents building mobile apps1194Not measuredOnly GLM 5.3 measured
Design Arena: HTML slides1189Not measuredOnly GLM 5.3 measured
Design Arena: PowerPoint slides1255Not measuredOnly GLM 5.3 measured
LiveBench research73.2%Not measuredOnly GLM 5.3 measured
LiveBench browser0.0%Not measuredOnly GLM 5.3 measured
LiveBench long agentic35.9%Not measuredOnly GLM 5.3 measured
SREBench (Vals AI)9.2%Not measuredOnly GLM 5.3 measured
Finance Agent (Vals AI)55.8%Not measuredOnly GLM 5.3 measured
Tax Agent Bench (Vals AI)39.7%Not measuredOnly GLM 5.3 measured
Legal Research Bench (Vals AI)49.0%Not measuredOnly GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)14.6%Not measuredOnly GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)56.3%Not measuredOnly GLM 5.3 measured
Terminal-Bench-Science (Vals AI)5.7%Not measuredOnly GLM 5.3 measured
APEX-Agents56.6%Not measuredOnly GLM 5.3 measured
Vending-Bench 2$8,164Not measuredOnly GLM 5.3 measured
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index70
Reasoning benchmarks for GLM 5.3 and Qwen3.5-Flash
BenchmarkGLM 5.3Qwen3.5-FlashLeader
GPQA Diamond88.1%82.8%GLM 5.3
LiveBench reasoning85.8%Not measuredOnly GLM 5.3 measured
MysteryMechanism (Vals AI)23.0%Not measuredOnly GLM 5.3 measured
MMMU Pro (Vals AI)Not measured81.9%Only Qwen3.5-Flash measured
GPQA Diamond (Artificial Analysis run)91.7%Not measuredOnly GLM 5.3 measured
Humanity's Last Exam (Artificial Analysis run)42.3%Not measuredOnly GLM 5.3 measured
GPQA Diamond (Epoch AI run)90.9%82.3%GLM 5.3
Chess puzzles37.0%14.0%GLM 5.3
Mystery game puzzles33.0%20.0%GLM 5.3
LMArena Text: hard prompts14901403GLM 5.3
LMArena Text: expert15141407GLM 5.3
LMArena Text: hard prompts in English14961409GLM 5.3
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 5.3 and Qwen3.5-Flash
BenchmarkGLM 5.3Qwen3.5-FlashLeader
AIMENot measured92.5%Only Qwen3.5-Flash measured
OTIS Mock AIME91.1%84.4%GLM 5.3
FrontierMath, tiers 1 to 368.8%18.2%GLM 5.3
FrontierMath, tier 429.3%Not measuredOnly GLM 5.3 measured
ProofBench49.0%Not measuredOnly GLM 5.3 measured
LMArena Text: math14951407GLM 5.3
LMArena Text: mathematical work14971413GLM 5.3
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
Knowledge benchmarks for GLM 5.3 and Qwen3.5-Flash
BenchmarkGLM 5.3Qwen3.5-FlashLeader
MMLU Pro86.8%84.1%GLM 5.3
LegalBench (Vals AI)84.8%84.3%GLM 5.3
CaseLaw (Vals AI)Not measured56.0%Only Qwen3.5-Flash measured
CorpFin (Vals AI)Not measured63.6%Only Qwen3.5-Flash measured
TaxEval (Vals AI)72.4%72.2%GLM 5.3
MortgageTax (Vals AI)Not measured67.4%Only Qwen3.5-Flash measured
MedCode (Vals AI)42.9%33.0%GLM 5.3
SimpleQA Verified41.0%20.3%GLM 5.3
LMArena Text: science14981415GLM 5.3
LMArena Text: medicine and healthcare14811407GLM 5.3
LMArena Text: legal and government14731398GLM 5.3
LMArena Text: business and finance14591394GLM 5.3
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for GLM 5.3 and Qwen3.5-Flash
BenchmarkGLM 5.3Qwen3.5-FlashLeader
Design Arena: websites1295Not measuredOnly GLM 5.3 measured
Design Arena: UI components1314Not measuredOnly GLM 5.3 measured
Design Arena: game dev1311Not measuredOnly GLM 5.3 measured
Design Arena: data viz1260Not measuredOnly GLM 5.3 measured
Design Arena: 3D1332Not measuredOnly GLM 5.3 measured
Design Arena: all code categories1304Not measuredOnly GLM 5.3 measured
Design Arena: SVG1292Not measuredOnly GLM 5.3 measured
Design Arena: ASCII art1213Not measuredOnly GLM 5.3 measured
LiveBench design77.3%Not measuredOnly GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index200
General benchmarks for GLM 5.3 and Qwen3.5-Flash
BenchmarkGLM 5.3Qwen3.5-FlashLeader
Vals Index59.7%Not measuredOnly GLM 5.3 measured
LiveBench data analysis70.2%Not measuredOnly GLM 5.3 measured
Public Benefits Bench (Vals AI)68.5%Not measuredOnly GLM 5.3 measured
SAGE (Vals AI)Not measured42.5%Only Qwen3.5-Flash measured
MedScribe (Vals AI)88.8%70.6%GLM 5.3
Long context reasoning (AA-LCR)79.7%Not measuredOnly GLM 5.3 measured
DTBench87.7%82.9%GLM 5.3
LMCA55.5%29.1%GLM 5.3
LMArena Text14741397GLM 5.3
LMArena Text: instruction following14791374GLM 5.3
LMArena Text: creative writing14581343GLM 5.3
LMArena Text: English14821404GLM 5.3
LMArena Text: non-English14591385GLM 5.3
LMArena Text: Chinese15321445GLM 5.3
LMArena Text: German14881390GLM 5.3
LMArena Text: Japanese14601369GLM 5.3
LMArena Text: Korean14601344GLM 5.3
LMArena Text: French15031413GLM 5.3
LMArena Text: Spanish14621400GLM 5.3
LMArena Text: Russian14691380GLM 5.3
LMArena Text: Polish14661376GLM 5.3
LMArena Text: multi-turn14721393GLM 5.3
LMArena Text: longer queries14831392GLM 5.3
LMArena Text: writing and language14691369GLM 5.3
LMArena Text: entertainment and media14471347GLM 5.3
Neither model has a result on the other 21 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.3 and Qwen3.5-Flash
BenchmarkGLM 5.3Qwen3.5-FlashLeader
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$0.0008Fits in one request
Qwen3.5-Flash$0.0002Fits in one request

Qwen3.5-Flash costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.0081Fits in one request
Qwen3.5-Flash$0.0040Fits in one request

Qwen3.5-Flash costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.030Fits in one request
Qwen3.5-Flash$0.016Fits in one request

Qwen3.5-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 and Qwen3.5-Flash
SpecificationZ.ai GLM 5.3Qwen3.5-Flash
MakerZ.aiQwen
WeightsOpen weightsNo public weights listed
ReleasedAugust 18, 2026February 25, 2026
Context window1.05M1M
Input $/Mtok$0.079$0.065
Output $/Mtok$1.40$0.26
Coding index73.16.2
Independent measurements behind it21
Cost per solved task$0.36$0.40
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 better than Qwen3.5-Flash for coding?

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 6.2 for Qwen3.5-Flash. Pick Qwen3.5-Flash when output tokens cost 5.4x less, or when the benchmark that matters is LiveCodeBench (Vals AI), where it leads.

Which is cheaper, GLM 5.3 or Qwen3.5-Flash?

GLM 5.3 costs $0.079 per million input tokens and $1.40 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and Qwen3.5-Flash $0.40.

Which has the bigger context window, GLM 5.3 or Qwen3.5-Flash?

GLM 5.3, with 1.05M tokens against 1M.

How many benchmarks is the GLM 5.3 vs Qwen3.5-Flash comparison based on?

49 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 2 independent benchmarks and Qwen3.5-Flash's on 1. Where only one model has a result, the page says so instead of naming a winner.

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