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GLM 4.7 Flash vs Qwen3.5 397B A17B

Qwen3.5 397B A17B leads GLM 4.7 Flash on the Artificial Analysis Intelligence Index, 21.4 to 14.9, and GLM 4.7 Flash costs 5.8x less per output token ($0.40 vs $2.34 per million). Updated October 22, 2026; 43 benchmarks have a result for both.

Replace GLM 4.7 Flash Replace Qwen3.5 397B A17B
GLM 4.7 FlashZ.ai
Coding
No data
Overall
42.1
Output $/M
$0.40
3
Qwen3.5 397B A17BQwen
Coding
No data
Overall
57.1
Output $/M
$2.34
40
vs boards won of 43 shared

GLM 4.7 Flash or Qwen3.5 397B A17B: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick GLM 4.7 Flash for output tokens 5.8x cheaper ($0.40 vs $2.34 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model AGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5 397B A17BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
43
GLM 4.7 Flash only
0
Qwen3.5 397B A17B only
22
Like-for-like categories
8 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 4.7 Flash not benchmarked, Qwen3.5 397B A17B not benchmarked.
Widest category
Reasoning, 20.8 points apart on average over 6 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.7 Flash 41.7% against Qwen3.5 397B A17B 77.3%, 35.7 points apart on a 0 to 100 scale.
Output price
GLM 4.7 Flash $0.40 against Qwen3.5 397B A17B $2.34 per million output tokens, 5.8x apart.
Context window
GLM 4.7 Flash takes 200K tokens, Qwen3.5 397B A17B 262K.

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 FlashBarsQwen3.5 397B A17BTies
Coding03
Intelligence01
Agentic work10
Reasoning06
Math03
Knowledge04
Web design25
General018
All shared boards340
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GLM 4.7 Flash and Qwen3.5 397B A17B
BenchmarkGLM 4.7 FlashQwen3.5 397B A17BLeader
LMArena WebDevNot measured1400Only Qwen3.5 397B A17B measured
SciCodeNot measured44.8%Only Qwen3.5 397B A17B measured
Terminal-Bench Hard (Artificial Analysis run)22.0%40.9%Qwen3.5 397B A17B
ALE-BenchNot measured621.9Only Qwen3.5 397B A17B measured
LMArena Text: coding13821464Qwen3.5 397B A17B
LMArena Text: software and IT13891463Qwen3.5 397B A17B
LMArena WebDev: ReactNot measured1388Only Qwen3.5 397B A17B measured
LMArena WebDev: HTMLNot measured1410Only Qwen3.5 397B A17B measured
Neither model has a result on the other 31 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.7 Flash and Qwen3.5 397B A17B
BenchmarkGLM 4.7 FlashQwen3.5 397B A17BLeader
AA Intelligence Index14.921.4Qwen3.5 397B A17B
Epoch Capabilities IndexNot measured146.8Only Qwen3.5 397B A17B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 Flash and Qwen3.5 397B A17B
BenchmarkGLM 4.7 FlashQwen3.5 397B A17BLeader
τ²-Bench Telecom98.8%95.6%GLM 4.7 Flash
APEX-AgentsNot measured24.9%Only Qwen3.5 397B A17B measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GLM 4.7 Flash and Qwen3.5 397B A17B
BenchmarkGLM 4.7 FlashQwen3.5 397B A17BLeader
GPQA Diamond (Artificial Analysis run)58.1%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)7.6%29.0%Qwen3.5 397B A17B
GPQA Diamond (Epoch AI run)60.5%86.4%Qwen3.5 397B A17B
Mystery game puzzlesNot measured18.0%Only Qwen3.5 397B A17B measured
LMArena Text: hard prompts13561448Qwen3.5 397B A17B
LMArena Text: expert13571462Qwen3.5 397B A17B
LMArena Text: hard prompts in English13691450Qwen3.5 397B A17B
LMArena Vision: diagramsNot measured1277Only Qwen3.5 397B A17B measured
LMArena Vision: homeworkNot measured1292Only Qwen3.5 397B A17B measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GLM 4.7 Flash and Qwen3.5 397B A17B
BenchmarkGLM 4.7 FlashQwen3.5 397B A17BLeader
OTIS Mock AIME58.3%88.9%Qwen3.5 397B A17B
FrontierMath, tiers 1 to 3Not measured31.2%Only Qwen3.5 397B A17B measured
LMArena Text: math13551453Qwen3.5 397B A17B
LMArena Text: mathematical work13691454Qwen3.5 397B A17B
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 4.7 Flash and Qwen3.5 397B A17B
BenchmarkGLM 4.7 FlashQwen3.5 397B A17BLeader
LMArena Text: science13581457Qwen3.5 397B A17B
LMArena Text: medicine and healthcare13431455Qwen3.5 397B A17B
LMArena Text: legal and government13571441Qwen3.5 397B A17B
LMArena Text: business and finance13431435Qwen3.5 397B A17B
Neither model has a result on the other 14 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index25
Web design benchmarks for GLM 4.7 Flash and Qwen3.5 397B A17B
BenchmarkGLM 4.7 FlashQwen3.5 397B A17BLeader
Design Arena: websites11941190GLM 4.7 Flash
Design Arena: UI components12051160GLM 4.7 Flash
Design Arena: game dev11341140Qwen3.5 397B A17B
Design Arena: data viz11231175Qwen3.5 397B A17B
Design Arena: 3D11201160Qwen3.5 397B A17B
Design Arena: all code categories11771179Qwen3.5 397B A17B
Design Arena: SVG10371139Qwen3.5 397B A17B
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
General benchmarks for GLM 4.7 Flash and Qwen3.5 397B A17B
BenchmarkGLM 4.7 FlashQwen3.5 397B A17BLeader
IFBench60.8%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)41.7%77.3%Qwen3.5 397B A17B
DTBenchNot measured87.5%Only Qwen3.5 397B A17B measured
LMCANot measured37.9%Only Qwen3.5 397B A17B measured
LMArena Text13511437Qwen3.5 397B A17B
LMArena Text: instruction following13271424Qwen3.5 397B A17B
LMArena Text: creative writing12961402Qwen3.5 397B A17B
LMArena VisionNot measured1262Only Qwen3.5 397B A17B measured
LMArena Text: English13731443Qwen3.5 397B A17B
LMArena Text: non-English13301430Qwen3.5 397B A17B
LMArena Text: Chinese14031498Qwen3.5 397B A17B
LMArena Text: German13361445Qwen3.5 397B A17B
LMArena Text: JapaneseNot measured1429Only Qwen3.5 397B A17B measured
LMArena Text: Korean12831384Qwen3.5 397B A17B
LMArena Text: French13321462Qwen3.5 397B A17B
LMArena Text: Spanish13501441Qwen3.5 397B A17B
LMArena Text: Russian13331430Qwen3.5 397B A17B
LMArena Text: Polish12911447Qwen3.5 397B A17B
LMArena Text: multi-turn13421446Qwen3.5 397B A17B
LMArena Text: longer queries13451441Qwen3.5 397B A17B
LMArena Text: writing and language13201415Qwen3.5 397B A17B
LMArena Text: entertainment and media13041399Qwen3.5 397B A17B
LMArena Vision: EnglishNot measured1268Only Qwen3.5 397B A17B measured
LMArena Vision: ChineseNot measured1314Only Qwen3.5 397B A17B measured
LMArena Vision: reading textNot measured1271Only Qwen3.5 397B A17B measured
LMArena Vision: humorNot measured1262Only Qwen3.5 397B A17B measured
LMArena Vision: captioningNot measured1230Only Qwen3.5 397B A17B measured
LMArena Vision: recognizing thingsNot measured1265Only Qwen3.5 397B A17B measured
LMArena Vision: creative writingNot measured1260Only Qwen3.5 397B A17B measured
Neither model has a result on the other 17 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 Qwen3.5 397B A17B
BenchmarkGLM 4.7 FlashQwen3.5 397B A17BLeader
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
Qwen3.5 397B A17B$0.0016Fits 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
Qwen3.5 397B A17B$0.027Fits 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
Qwen3.5 397B A17B$0.101Fits 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 Qwen3.5 397B A17B
SpecificationZ.ai GLM 4.7 FlashQwen3.5 397B A17B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedJanuary 19, 2026February 16, 2026
Context window200K262K
Input $/Mtok$0.0653$0.39
Output $/Mtok$0.40$2.34
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 Flash better than Qwen3.5 397B A17B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 5.8x cheaper ($0.40 vs $2.34 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, GLM 4.7 Flash or Qwen3.5 397B A17B?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.7 Flash or Qwen3.5 397B A17B?

Qwen3.5 397B A17B, with 262K tokens against 200K.

How many benchmarks is the GLM 4.7 Flash vs Qwen3.5 397B A17B comparison based on?

43 benchmarks have a result for both models as of October 22, 2026. GLM 4.7 Flash's coding index rests on 0 independent benchmarks and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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