Enter opens the highlighted result. Esc closes.

GLM 4.6 vs Qwen3.5-Flash

Qwen3.5-Flash leads GLM 4.6 on coding, 41.1 to 8.8 on the Vibe Coding Index, and Qwen3.5-Flash costs 8.5x less per output token ($0.26 vs $2.20 per million). Updated October 22, 2026; 38 benchmarks have a result for both.

Replace GLM 4.6 Replace Qwen3.5-Flash
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
32
Qwen3.5-FlashQwen
Coding
41.1
Overall
50.1
Output $/M
$0.26
6
vs boards won of 38 shared

GLM 4.6 or Qwen3.5-Flash: which to pick

Pick Qwen3.5-Flash for coding: it scores 41.1 on the Vibe Coding Index against 8.8 for GLM 4.6.

Pick GLM 4.6 for work measured by LMArena WebDev or ALE-Bench, where it leads.

Model AGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved taskModel BQwen3.5-FlashQwen41.1/1002 independent benchmarks$0.40 per solved task
Shared results
38
GLM 4.6 only
31
Qwen3.5-Flash only
17
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 4.6 8.8 from 2 benchmarks, Qwen3.5-Flash 41.1 from 2 benchmarks.
Widest category
General, 7.7 points apart on average over 17 shared benchmarks.
Widest single gap
LMArena WebDev: GLM 4.6 1341 against Qwen3.5-Flash 1244, 13.9 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against Qwen3.5-Flash $0.26 per million output tokens, 8.5x apart.
Context window
GLM 4.6 takes 205K 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 4.6BarsQwen3.5-FlashTies
Coding51
Reasoning31
Math30
Knowledge44
General170
All shared boards326
CodingLike-for-likeSix of these boards feed the Vibe Coding Index51
Coding benchmarks for GLM 4.6 and Qwen3.5-Flash
BenchmarkGLM 4.6Qwen3.5-FlashLeader
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev13411244GLM 4.6
AA Coding Index45.8Not measuredOnly GLM 4.6 measured
LiveCodeBench (Vals AI)81.0%83.3%Qwen3.5-Flash
SWE-bench Verified (Vals AI)Not measured64.4%Only Qwen3.5-Flash measured
Vibe Code Bench (Vals AI)3.1%Not measuredOnly GLM 4.6 measured
LiveCodeBench (Artificial Analysis run)69.5%Not measuredOnly GLM 4.6 measured
Terminal-Bench Hard (Artificial Analysis run)28.8%Not measuredOnly GLM 4.6 measured
Terminal-Bench24.5%Not measuredOnly GLM 4.6 measured
ALE-Bench340.8265.9GLM 4.6
LMArena Text: coding14491412GLM 4.6
LMArena Text: software and IT14551416GLM 4.6
LMArena WebDev: ReactNot measured1229Only Qwen3.5-Flash measured
LMArena WebDev: HTML13501291GLM 4.6
Neither model has a result on the other 25 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for GLM 4.6 and Qwen3.5-Flash
BenchmarkGLM 4.6Qwen3.5-FlashLeader
AA Intelligence Index18.5Not measuredOnly GLM 4.6 measured
Epoch Capabilities IndexNot measured143.5Only Qwen3.5-Flash measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Agentic work benchmarks for GLM 4.6 and Qwen3.5-Flash
BenchmarkGLM 4.6Qwen3.5-FlashLeader
Design Arena: agents building full-stack apps1029Not measuredOnly GLM 4.6 measured
Design Arena: agents building mobile apps1096Not measuredOnly GLM 4.6 measured
Design Arena: agents building Android apps1084Not measuredOnly GLM 4.6 measured
Design Arena: agents building Godot games1180Not measuredOnly GLM 4.6 measured
τ²-Bench Telecom76.9%Not measuredOnly GLM 4.6 measured
Berkeley Function Calling Leaderboard (BFCL)72.4%Not measuredOnly GLM 4.6 measured
BrowseComp-Plus48.2%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index31
Reasoning benchmarks for GLM 4.6 and Qwen3.5-Flash
BenchmarkGLM 4.6Qwen3.5-FlashLeader
GPQA Diamond74.5%82.8%Qwen3.5-Flash
MMMU Pro (Vals AI)Not measured81.9%Only Qwen3.5-Flash measured
GPQA Diamond (Artificial Analysis run)78.0%Not measuredOnly GLM 4.6 measured
Humanity's Last Exam (Artificial Analysis run)14.5%Not measuredOnly GLM 4.6 measured
GPQA Diamond (Epoch AI run)Not measured82.3%Only Qwen3.5-Flash measured
Chess puzzlesNot measured14.0%Only Qwen3.5-Flash measured
Mystery game puzzlesNot measured20.0%Only Qwen3.5-Flash measured
LMArena Text: hard prompts14401403GLM 4.6
LMArena Text: expert14321407GLM 4.6
LMArena Text: hard prompts in English14451409GLM 4.6
Kagi LLM Benchmark45.7%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 4.6 and Qwen3.5-Flash
BenchmarkGLM 4.6Qwen3.5-FlashLeader
AIME92.7%92.5%GLM 4.6
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.0Not measuredOnly GLM 4.6 measured
AIME 2025 (Artificial Analysis run)86.0%Not measuredOnly GLM 4.6 measured
OTIS Mock AIMENot measured84.4%Only Qwen3.5-Flash measured
FrontierMath, tiers 1 to 3Not measured18.2%Only Qwen3.5-Flash measured
LMArena Text: math14321407GLM 4.6
LMArena Text: mathematical work14471413GLM 4.6
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index44
Knowledge benchmarks for GLM 4.6 and Qwen3.5-Flash
BenchmarkGLM 4.6Qwen3.5-FlashLeader
MMLU Pro82.2%84.1%Qwen3.5-Flash
LegalBench (Vals AI)79.6%84.3%Qwen3.5-Flash
CaseLaw (Vals AI)Not measured56.0%Only Qwen3.5-Flash measured
CorpFin (Vals AI)56.8%63.6%Qwen3.5-Flash
TaxEval (Vals AI)66.2%72.2%Qwen3.5-Flash
MortgageTax (Vals AI)Not measured67.4%Only Qwen3.5-Flash measured
MedQA (Vals AI)92.2%Not measuredOnly GLM 4.6 measured
MedCode (Vals AI)Not measured33.0%Only Qwen3.5-Flash measured
MMLU Pro (Artificial Analysis run)82.9%Not measuredOnly GLM 4.6 measured
SimpleQA VerifiedNot measured20.3%Only Qwen3.5-Flash measured
LMArena Text: science14561415GLM 4.6
LMArena Text: medicine and healthcare14551407GLM 4.6
LMArena Text: legal and government14481398GLM 4.6
LMArena Text: business and finance14161394GLM 4.6
Neither model has a result on the other 4 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.6 and Qwen3.5-Flash
BenchmarkGLM 4.6Qwen3.5-FlashLeader
Design Arena: websites1174Not measuredOnly GLM 4.6 measured
Design Arena: UI components1145Not measuredOnly GLM 4.6 measured
Design Arena: game dev1150Not measuredOnly GLM 4.6 measured
Design Arena: data viz1161Not measuredOnly GLM 4.6 measured
Design Arena: 3D1128Not measuredOnly GLM 4.6 measured
Design Arena: all code categories1165Not measuredOnly GLM 4.6 measured
Design Arena: SVG1108Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
General benchmarks for GLM 4.6 and Qwen3.5-Flash
BenchmarkGLM 4.6Qwen3.5-FlashLeader
SAGE (Vals AI)Not measured42.5%Only Qwen3.5-Flash measured
MedScribe (Vals AI)Not measured70.6%Only Qwen3.5-Flash measured
IFBench43.4%Not measuredOnly GLM 4.6 measured
Long context reasoning (AA-LCR)54.0%Not measuredOnly GLM 4.6 measured
DTBenchNot measured82.9%Only Qwen3.5-Flash measured
LMCANot measured29.1%Only Qwen3.5-Flash measured
LMArena Text14401397GLM 4.6
LMArena Text: instruction following14101374GLM 4.6
LMArena Text: creative writing14121343GLM 4.6
LMArena Text: English14491404GLM 4.6
LMArena Text: non-English14261385GLM 4.6
LMArena Text: Chinese14991445GLM 4.6
LMArena Text: German14471390GLM 4.6
LMArena Text: Japanese13931369GLM 4.6
LMArena Text: Korean14001344GLM 4.6
LMArena Text: French14591413GLM 4.6
LMArena Text: Spanish14361400GLM 4.6
LMArena Text: Russian14181380GLM 4.6
LMArena Text: Polish14471376GLM 4.6
LMArena Text: multi-turn14271393GLM 4.6
LMArena Text: longer queries14221392GLM 4.6
LMArena Text: writing and language14151369GLM 4.6
LMArena Text: entertainment and media14091347GLM 4.6
Neither model has a result on the other 23 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6 and Qwen3.5-Flash
BenchmarkGLM 4.6Qwen3.5-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 4.6$0.0017Fits in one request
Qwen3.5-Flash$0.0002Fits in one request

Qwen3.5-Flash costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.037Fits in one request
Qwen3.5-Flash$0.0040Fits in one request

Qwen3.5-Flash costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.142Exceeds the context window
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 4.6 and Qwen3.5-Flash
SpecificationZ.ai GLM 4.6Qwen3.5-Flash
MakerZ.aiQwen
WeightsOpen weightsNo public weights listed
ReleasedSeptember 30, 2025February 25, 2026
Context window205K1M
Input $/Mtok$0.60$0.065
Output $/Mtok$2.20$0.26
Coding index8.841.1
Independent measurements behind it22
Cost per solved taskNot computable$0.40
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than Qwen3.5-Flash for coding?

Pick Qwen3.5-Flash for coding: it scores 41.1 on the Vibe Coding Index against 8.8 for GLM 4.6. Pick GLM 4.6 for work measured by LMArena WebDev or ALE-Bench, where it leads.

Which is cheaper, GLM 4.6 or Qwen3.5-Flash?

GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Cost per solved task is known for Qwen3.5-Flash only.

Which has the bigger context window, GLM 4.6 or Qwen3.5-Flash?

Qwen3.5-Flash, with 1M tokens against 205K.

How many benchmarks is the GLM 4.6 vs Qwen3.5-Flash comparison based on?

38 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.

Compare next

Embed this comparison on your site

Free under CC BY 4.0, while the source link stays visible. Preview the frame · Terms and other embeds

<iframe src="https://llmbenchmark.ai/embed/compare/glm-4-6-vs-qwen3-5-flash-02-23" title="GLM 4.6 vs Qwen3.5-Flash, LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-4-6-vs-qwen3-5-flash-02-23">LLMBenchmark.ai, GLM 4.6 vs Qwen3.5-Flash</a></p>