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GLM 4.7 Flash vs Qwen3.5-Flash

Qwen3.5-Flash costs 1.5x less per output token than GLM 4.7 Flash ($0.26 vs $0.40 per million). Updated October 23, 2026; 29 benchmarks have a result for both.

Replace GLM 4.7 Flash Replace Qwen3.5-Flash
GLM 4.7 FlashZ.ai
Coding
No data
Overall
42.1
Output $/M
$0.40
0
Qwen3.5-FlashQwen
Coding
41.1
Overall
50.1
Output $/M
$0.26
29
vs boards won of 29 shared

GLM 4.7 Flash or Qwen3.5-Flash: which to pick

Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Model AGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-FlashQwen41.1/1002 independent benchmarks$0.40 per solved task
Shared results
29
GLM 4.7 Flash only
14
Qwen3.5-Flash only
26
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 4.7 Flash not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
Widest category
Math, 14.0 points apart on average over 3 shared benchmarks.
Widest single gap
OTIS Mock AIME: GLM 4.7 Flash 58.3% against Qwen3.5-Flash 84.4%, 26.1 points apart on a 0 to 100 scale.
Output price
GLM 4.7 Flash $0.40 against Qwen3.5-Flash $0.26 per million output tokens, 1.5x apart.
Context window
GLM 4.7 Flash takes 200K 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 4.7 FlashBarsQwen3.5-FlashTies
Coding02
Reasoning04
Math03
Knowledge04
General016
All shared boards029
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for GLM 4.7 Flash and Qwen3.5-Flash
BenchmarkGLM 4.7 FlashQwen3.5-FlashLeader
LMArena WebDevNot measured1244Only Qwen3.5-Flash measured
LiveCodeBench (Vals AI)Not measured83.3%Only Qwen3.5-Flash measured
SWE-bench Verified (Vals AI)Not measured64.4%Only Qwen3.5-Flash measured
Terminal-Bench Hard (Artificial Analysis run)22.0%Not measuredOnly GLM 4.7 Flash measured
ALE-BenchNot measured265.9Only Qwen3.5-Flash measured
LMArena Text: coding13821412Qwen3.5-Flash
LMArena Text: software and IT13891416Qwen3.5-Flash
LMArena WebDev: ReactNot measured1229Only Qwen3.5-Flash measured
LMArena WebDev: HTMLNot measured1291Only Qwen3.5-Flash measured
Neither model has a result on the other 34 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for GLM 4.7 Flash and Qwen3.5-Flash
BenchmarkGLM 4.7 FlashQwen3.5-FlashLeader
AA Intelligence Index14.9Not measuredOnly GLM 4.7 Flash measured
Epoch Capabilities IndexNot measured143.5Only Qwen3.5-Flash measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Agentic work benchmarks for GLM 4.7 Flash and Qwen3.5-Flash
BenchmarkGLM 4.7 FlashQwen3.5-FlashLeader
τ²-Bench Telecom98.8%Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 41 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for GLM 4.7 Flash and Qwen3.5-Flash
BenchmarkGLM 4.7 FlashQwen3.5-FlashLeader
GPQA DiamondNot measured82.8%Only Qwen3.5-Flash measured
MMMU Pro (Vals AI)Not measured81.9%Only Qwen3.5-Flash measured
GPQA Diamond (Artificial Analysis run)58.1%Not measuredOnly GLM 4.7 Flash measured
Humanity's Last Exam (Artificial Analysis run)7.6%Not measuredOnly GLM 4.7 Flash measured
GPQA Diamond (Epoch AI run)60.5%82.3%Qwen3.5-Flash
Chess puzzlesNot measured14.0%Only Qwen3.5-Flash measured
Mystery game puzzlesNot measured20.0%Only Qwen3.5-Flash measured
LMArena Text: hard prompts13561403Qwen3.5-Flash
LMArena Text: expert13571407Qwen3.5-Flash
LMArena Text: hard prompts in English13701409Qwen3.5-Flash
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-Flash
BenchmarkGLM 4.7 FlashQwen3.5-FlashLeader
AIMENot measured92.5%Only Qwen3.5-Flash measured
OTIS Mock AIME58.3%84.4%Qwen3.5-Flash
FrontierMath, tiers 1 to 3Not measured18.2%Only Qwen3.5-Flash measured
LMArena Text: math13551407Qwen3.5-Flash
LMArena Text: mathematical work13691413Qwen3.5-Flash
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-Flash
BenchmarkGLM 4.7 FlashQwen3.5-FlashLeader
MMLU ProNot measured84.1%Only Qwen3.5-Flash measured
LegalBench (Vals AI)Not measured84.3%Only Qwen3.5-Flash measured
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)Not measured72.2%Only Qwen3.5-Flash measured
MortgageTax (Vals AI)Not measured67.4%Only Qwen3.5-Flash measured
MedCode (Vals AI)Not measured33.0%Only Qwen3.5-Flash measured
SimpleQA VerifiedNot measured20.3%Only Qwen3.5-Flash measured
LMArena Text: science13581415Qwen3.5-Flash
LMArena Text: medicine and healthcare13431407Qwen3.5-Flash
LMArena Text: legal and government13581397Qwen3.5-Flash
LMArena Text: business and finance13431394Qwen3.5-Flash
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.7 Flash and Qwen3.5-Flash
BenchmarkGLM 4.7 FlashQwen3.5-FlashLeader
Design Arena: websites1194Not measuredOnly GLM 4.7 Flash measured
Design Arena: UI components1205Not measuredOnly GLM 4.7 Flash measured
Design Arena: game dev1134Not measuredOnly GLM 4.7 Flash measured
Design Arena: data viz1123Not measuredOnly GLM 4.7 Flash measured
Design Arena: 3D1121Not measuredOnly GLM 4.7 Flash measured
Design Arena: all code categories1177Not measuredOnly GLM 4.7 Flash measured
Design Arena: SVG1037Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index016
General benchmarks for GLM 4.7 Flash and Qwen3.5-Flash
BenchmarkGLM 4.7 FlashQwen3.5-FlashLeader
SAGE (Vals AI)Not measured42.5%Only Qwen3.5-Flash measured
MedScribe (Vals AI)Not measured70.6%Only Qwen3.5-Flash measured
IFBench60.8%Not measuredOnly GLM 4.7 Flash measured
Long context reasoning (AA-LCR)41.7%Not measuredOnly GLM 4.7 Flash measured
DTBenchNot measured82.9%Only Qwen3.5-Flash measured
LMCANot measured29.1%Only Qwen3.5-Flash measured
LMArena Text13511397Qwen3.5-Flash
LMArena Text: instruction following13271375Qwen3.5-Flash
LMArena Text: creative writing12961343Qwen3.5-Flash
LMArena Text: English13731404Qwen3.5-Flash
LMArena Text: non-English13301385Qwen3.5-Flash
LMArena Text: Chinese14031446Qwen3.5-Flash
LMArena Text: German13361391Qwen3.5-Flash
LMArena Text: JapaneseNot measured1369Only Qwen3.5-Flash measured
LMArena Text: Korean12831344Qwen3.5-Flash
LMArena Text: French13321413Qwen3.5-Flash
LMArena Text: Spanish13501399Qwen3.5-Flash
LMArena Text: Russian13331380Qwen3.5-Flash
LMArena Text: Polish12901376Qwen3.5-Flash
LMArena Text: multi-turn13421393Qwen3.5-Flash
LMArena Text: longer queries13441392Qwen3.5-Flash
LMArena Text: writing and language13201369Qwen3.5-Flash
LMArena Text: entertainment and media13041347Qwen3.5-Flash
Neither model has a result on the other 25 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-Flash
BenchmarkGLM 4.7 FlashQwen3.5-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 4.7 Flash$0.0003Fits in one request
Qwen3.5-Flash$0.0002Fits in one request

Qwen3.5-Flash costs less

Repository review

50K input + 3K output tokens

GLM 4.7 Flash$0.0045Fits in one request
Qwen3.5-Flash$0.0040Fits in one request

Qwen3.5-Flash costs less

Agent loop

200K input + 10K output tokens

GLM 4.7 Flash$0.017Exceeds 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 4.7 Flash and Qwen3.5-Flash
SpecificationZ.ai GLM 4.7 FlashQwen3.5-Flash
MakerZ.aiQwen
WeightsOpen weightsNo public weights listed
ReleasedJanuary 19, 2026February 25, 2026
Context window200K1M
Input $/Mtok$0.0653$0.065
Output $/Mtok$0.40$0.26
Coding indexNot benchmarked41.1
Independent measurements behind it02
Cost per solved taskNot computable$0.40
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 Flash better than Qwen3.5-Flash for coding?

Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, GLM 4.7 Flash or Qwen3.5-Flash?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 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 4.7 Flash or Qwen3.5-Flash?

Qwen3.5-Flash, with 1M tokens against 200K.

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

29 benchmarks have a result for both models as of October 23, 2026. GLM 4.7 Flash's coding index rests on 0 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.

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