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GLM 4.7 Flash vs Qwen3.6 Plus

Updated October 21, 2026. 43 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.7 Flash Replace Qwen3.6 Plus
GLM 4.7 FlashZ.ai
Coding
No data
Overall
14.9
Output $/M
$0.40
1
Qwen3.6 PlusQwen
Coding
44.1
Overall
27.0
Output $/M
$1.95
42
vs boards won of 43 shared

Decision reading

Only Qwen3.6 Plus has a coding score, 44.1 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding.

GLM 4.7 Flash has the edge where output tokens cost 4.9x less, and where the benchmark that matters is τ²-Bench Telecom, where it leads.

Model AGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.6 PlusQwen44.1/1003 independent benchmarks$1.19 per solved task
Shared results
43
GLM 4.7 Flash only
0
Qwen3.6 Plus only
40
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 4.7 Flash not benchmarked, Qwen3.6 Plus 44.1 from 3 benchmarks.
Widest category
Math, 21.3 points apart on average over 3 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.7 Flash 41.7% against Qwen3.6 Plus 78.3%, 36.7 points apart on a 0 to 100 scale.
Output price
GLM 4.7 Flash $0.40 against Qwen3.6 Plus $1.95 per million output tokens, 4.9x apart.
Context window
GLM 4.7 Flash takes 200K 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 4.7 FlashBarsQwen3.6 PlusTies
Coding03
Intelligence01
Agentic work10
Reasoning06
Math03
Knowledge04
Web design07
General018
All shared boards142
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GLM 4.7 Flash and Qwen3.6 Plus
BenchmarkGLM 4.7 FlashQwen3.6 PlusLeader
LMArena WebDevNot measured1461Only Qwen3.6 Plus measured
AA Coding IndexNot measured54.5Only Qwen3.6 Plus measured
LiveCodeBench (Vals AI)Not measured86.0%Only Qwen3.6 Plus measured
SWE-bench Verified (Vals AI)Not measured73.4%Only Qwen3.6 Plus measured
Vibe Code Bench (Vals AI)Not measured25.6%Only Qwen3.6 Plus measured
Code migration (Vals AI)Not measured11.1%Only Qwen3.6 Plus measured
ProgramBench (Vals AI)Not measured0.0%Only Qwen3.6 Plus measured
Terminal-Bench 2.1 (Vals AI)Not measured53.2%Only Qwen3.6 Plus measured
Terminal-Bench Hard (Artificial Analysis run)22.0%43.9%Qwen3.6 Plus
ALE-BenchNot measured670.1Only Qwen3.6 Plus measured
LMArena Text: coding13821467Qwen3.6 Plus
LMArena Text: software and IT13891462Qwen3.6 Plus
LMArena WebDev: ReactNot measured1454Only Qwen3.6 Plus measured
LMArena WebDev: HTMLNot measured1463Only Qwen3.6 Plus measured
LMArena WebDev: from an imageNot measured1472Only Qwen3.6 Plus measured
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.7 Flash and Qwen3.6 Plus
BenchmarkGLM 4.7 FlashQwen3.6 PlusLeader
AA Intelligence Index14.927.0Qwen3.6 Plus
Epoch Capabilities IndexNot measured147.3Only Qwen3.6 Plus measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 Flash and Qwen3.6 Plus
BenchmarkGLM 4.7 FlashQwen3.6 PlusLeader
Finance Agent (Vals AI)Not measured40.9%Only Qwen3.6 Plus measured
Tax Agent Bench (Vals AI)Not measured7.7%Only Qwen3.6 Plus measured
Legal Research Bench (Vals AI)Not measured14.9%Only Qwen3.6 Plus measured
Excel Modeling Benchmark (Vals AI)Not measured32.9%Only Qwen3.6 Plus measured
τ²-Bench Telecom98.8%97.7%GLM 4.7 Flash
Vending-Bench 2Not measured$5,115Only Qwen3.6 Plus measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GLM 4.7 Flash and Qwen3.6 Plus
BenchmarkGLM 4.7 FlashQwen3.6 PlusLeader
GPQA DiamondNot measured87.4%Only Qwen3.6 Plus measured
MMMU Pro (Vals AI)Not measured84.2%Only Qwen3.6 Plus measured
GPQA Diamond (Artificial Analysis run)58.1%88.2%Qwen3.6 Plus
Humanity's Last Exam (Artificial Analysis run)7.6%27.8%Qwen3.6 Plus
GPQA Diamond (Epoch AI run)60.5%88.4%Qwen3.6 Plus
Chess puzzlesNot measured14.0%Only Qwen3.6 Plus measured
Mystery game puzzlesNot measured12.0%Only Qwen3.6 Plus measured
LMArena Text: hard prompts13561448Qwen3.6 Plus
LMArena Text: expert13571454Qwen3.6 Plus
LMArena Text: hard prompts in English13691455Qwen3.6 Plus
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GLM 4.7 Flash and Qwen3.6 Plus
BenchmarkGLM 4.7 FlashQwen3.6 PlusLeader
AIMENot measured94.6%Only Qwen3.6 Plus measured
OTIS Mock AIME58.3%93.3%Qwen3.6 Plus
FrontierMath, tiers 1 to 3Not measured38.2%Only Qwen3.6 Plus measured
LMArena Text: math13551449Qwen3.6 Plus
LMArena Text: mathematical work13691448Qwen3.6 Plus
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 4.7 Flash and Qwen3.6 Plus
BenchmarkGLM 4.7 FlashQwen3.6 PlusLeader
MMLU ProNot measured87.7%Only Qwen3.6 Plus measured
LegalBench (Vals AI)Not measured84.2%Only Qwen3.6 Plus measured
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)Not measured74.7%Only Qwen3.6 Plus measured
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: science13581439Qwen3.6 Plus
LMArena Text: medicine and healthcare13431442Qwen3.6 Plus
LMArena Text: legal and government13571437Qwen3.6 Plus
LMArena Text: business and finance13431430Qwen3.6 Plus
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for GLM 4.7 Flash and Qwen3.6 Plus
BenchmarkGLM 4.7 FlashQwen3.6 PlusLeader
Design Arena: websites11941243Qwen3.6 Plus
Design Arena: UI components12081233Qwen3.6 Plus
Design Arena: game dev11351211Qwen3.6 Plus
Design Arena: data viz11231228Qwen3.6 Plus
Design Arena: 3D11211202Qwen3.6 Plus
Design Arena: all code categories11781236Qwen3.6 Plus
Design Arena: SVG10381161Qwen3.6 Plus
Design Arena: ASCII artNot measured1114Only Qwen3.6 Plus measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
General benchmarks for GLM 4.7 Flash and Qwen3.6 Plus
BenchmarkGLM 4.7 FlashQwen3.6 PlusLeader
SAGE (Vals AI)Not measured44.9%Only Qwen3.6 Plus measured
MedScribe (Vals AI)Not measured77.0%Only Qwen3.6 Plus measured
Vals Multimodal IndexNot measured51.5%Only Qwen3.6 Plus measured
IFBench60.8%75.2%Qwen3.6 Plus
Long context reasoning (AA-LCR)41.7%78.3%Qwen3.6 Plus
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
LMArena Text13511436Qwen3.6 Plus
LMArena Text: instruction following13271424Qwen3.6 Plus
LMArena Text: creative writing12961404Qwen3.6 Plus
LMArena Text: English13731445Qwen3.6 Plus
LMArena Text: non-English13301424Qwen3.6 Plus
LMArena Text: Chinese14031477Qwen3.6 Plus
LMArena Text: German13361451Qwen3.6 Plus
LMArena Text: JapaneseNot measured1390Only Qwen3.6 Plus measured
LMArena Text: Korean12831379Qwen3.6 Plus
LMArena Text: French13321455Qwen3.6 Plus
LMArena Text: Spanish13501432Qwen3.6 Plus
LMArena Text: Russian13331434Qwen3.6 Plus
LMArena Text: Polish12911433Qwen3.6 Plus
LMArena Text: multi-turn13421438Qwen3.6 Plus
LMArena Text: longer queries13451439Qwen3.6 Plus
LMArena Text: writing and language13201418Qwen3.6 Plus
LMArena Text: entertainment and media13041402Qwen3.6 Plus
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 4.7 Flash and Qwen3.6 Plus
BenchmarkGLM 4.7 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 4.7 Flash$0.0003Fits in one request
Qwen3.6 Plus$0.0013Fits in one request

GLM 4.7 Flash costs less

Repository review

50K input + 3K output tokens

GLM 4.7 Flash$0.0045Fits in one request
Qwen3.6 Plus$0.022Fits in one request

GLM 4.7 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 4.7 Flash$0.017Exceeds the context window
Qwen3.6 Plus$0.085Fits in one request

GLM 4.7 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 4.7 Flash and Qwen3.6 Plus
SpecificationZ.ai GLM 4.7 FlashQwen3.6 Plus
MakerZ.aiQwen
WeightsOpen weightsNo public weights listed
ReleasedJanuary 19, 2026April 2, 2026
Context window200K1M
Input $/Mtok$0.0653$0.325
Output $/Mtok$0.40$1.95
Coding indexNot benchmarked44.1
Independent measurements behind it03
Cost per solved taskNot computable$1.19
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 Flash better than Qwen3.6 Plus for coding?

Only Qwen3.6 Plus has a coding score, 44.1 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding. GLM 4.7 Flash has the edge where output tokens cost 4.9x less, and where the benchmark that matters is τ²-Bench Telecom, where it leads.

Which is cheaper, GLM 4.7 Flash or Qwen3.6 Plus?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Qwen3.6 Plus costs $0.325 and $1.95. Cost per solved task is known for Qwen3.6 Plus only.

Which has the bigger context window, GLM 4.7 Flash or Qwen3.6 Plus?

Qwen3.6 Plus, with 1M tokens against 200K.

How many benchmarks is the GLM 4.7 Flash vs Qwen3.6 Plus comparison based on?

43 benchmarks have a result for both models as of October 21, 2026. GLM 4.7 Flash's coding index rests on 0 independent benchmarks and Qwen3.6 Plus's on 3. Where only one model has a result, the page says so instead of naming a winner.

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