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GLM 4.7 vs Qwen3.5-Flash

GLM 4.7 leads Qwen3.5-Flash on coding, 55.1 to 41.1 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; 48 benchmarks have a result for both.

Replace GLM 4.7 Replace Qwen3.5-Flash
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
37
Qwen3.5-FlashQwen
Coding
41.1
Overall
50.1
Output $/M
$0.26
11
vs boards won of 48 shared

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

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 41.1 for Qwen3.5-Flash.

Pick Qwen3.5-Flash for output tokens 8.5x cheaper ($0.26 vs $2.20 per million) and a larger context window (1M against 205K tokens).

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BQwen3.5-FlashQwen41.1/1002 independent benchmarks$0.40 per solved task
Shared results
48
GLM 4.7 only
33
Qwen3.5-Flash only
7
Like-for-like categories
6 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 55.1 from 2 benchmarks, Qwen3.5-Flash 41.1 from 2 benchmarks.
Widest category
Coding, 13.6 points apart on average over 8 shared benchmarks.
Widest single gap
LMArena WebDev: React: GLM 4.7 1420 against Qwen3.5-Flash 1229, 31.8 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against Qwen3.5-Flash $0.26 per million output tokens, 8.5x apart.
Context window
GLM 4.7 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 4.7BarsQwen3.5-FlashTies
Coding71
Intelligence10
Reasoning51
Math22
Knowledge56
General171
All shared boards3711
CodingLike-for-likeSix of these boards feed the Vibe Coding Index71
Coding benchmarks for GLM 4.7 and Qwen3.5-Flash
BenchmarkGLM 4.7Qwen3.5-FlashLeader
LMArena WebDev14351244GLM 4.7
AA Coding Index45.3Not measuredOnly GLM 4.7 measured
LiveCodeBench (Vals AI)82.2%83.3%Qwen3.5-Flash
SWE-bench Verified (Vals AI)69.4%64.4%GLM 4.7
LiveCodeBench (Artificial Analysis run)89.4%Not measuredOnly GLM 4.7 measured
Terminal-Bench Hard (Artificial Analysis run)31.8%Not measuredOnly GLM 4.7 measured
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
ALE-Bench399.5265.9GLM 4.7
LMArena Text: coding14541412GLM 4.7
LMArena Text: software and IT14531416GLM 4.7
LMArena WebDev: React14201229GLM 4.7
LMArena WebDev: HTML14471291GLM 4.7
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.7 and Qwen3.5-Flash
BenchmarkGLM 4.7Qwen3.5-FlashLeader
AA Intelligence Index22.2Not measuredOnly GLM 4.7 measured
Epoch Capabilities Index143.6143.5GLM 4.7
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Agentic work benchmarks for GLM 4.7 and Qwen3.5-Flash
BenchmarkGLM 4.7Qwen3.5-FlashLeader
Design Arena: agents building full-stack apps1050Not measuredOnly GLM 4.7 measured
Design Arena: agents building mobile apps1105Not measuredOnly GLM 4.7 measured
Design Arena: agents building Android apps1112Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
τ²-Bench Telecom95.9%Not measuredOnly GLM 4.7 measured
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
BrowseComp-Plus64.7%Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
Reasoning benchmarks for GLM 4.7 and Qwen3.5-Flash
BenchmarkGLM 4.7Qwen3.5-FlashLeader
GPQA Diamond80.0%82.8%Qwen3.5-Flash
MMMU Pro (Vals AI)Not measured81.9%Only Qwen3.5-Flash measured
GPQA Diamond (Artificial Analysis run)85.9%Not measuredOnly GLM 4.7 measured
Humanity's Last Exam (Artificial Analysis run)27.4%Not measuredOnly GLM 4.7 measured
GPQA Diamond (Epoch AI run)83.3%82.3%GLM 4.7
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%14.0%GLM 4.7
Mystery game puzzlesNot measured20.0%Only Qwen3.5-Flash measured
LMArena Text: hard prompts14431403GLM 4.7
LMArena Text: expert14241407GLM 4.7
LMArena Text: hard prompts in English14521409GLM 4.7
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index22
Math benchmarks for GLM 4.7 and Qwen3.5-Flash
BenchmarkGLM 4.7Qwen3.5-FlashLeader
AIME93.3%92.5%GLM 4.7
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.0Not measuredOnly GLM 4.7 measured
AIME 2025 (Artificial Analysis run)95.0%Not measuredOnly GLM 4.7 measured
OTIS Mock AIME83.3%84.4%Qwen3.5-Flash
FrontierMath, tiers 1 to 3Not measured18.2%Only Qwen3.5-Flash measured
ProofBench6.0%Not measuredOnly GLM 4.7 measured
LMArena Text: math14241407GLM 4.7
LMArena Text: mathematical work14111413Qwen3.5-Flash
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index56
Knowledge benchmarks for GLM 4.7 and Qwen3.5-Flash
BenchmarkGLM 4.7Qwen3.5-FlashLeader
MMLU Pro82.7%84.1%Qwen3.5-Flash
LegalBench (Vals AI)83.4%84.3%Qwen3.5-Flash
CaseLaw (Vals AI)54.9%56.0%Qwen3.5-Flash
CorpFin (Vals AI)46.4%63.6%Qwen3.5-Flash
TaxEval (Vals AI)68.8%72.2%Qwen3.5-Flash
MortgageTax (Vals AI)Not measured67.4%Only Qwen3.5-Flash measured
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%33.0%Qwen3.5-Flash
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%20.3%GLM 4.7
LMArena Text: science14571415GLM 4.7
LMArena Text: medicine and healthcare14691407GLM 4.7
LMArena Text: legal and government14441398GLM 4.7
LMArena Text: business and finance14291394GLM 4.7
Neither model has a result on the other 4 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 4.7 and Qwen3.5-Flash
BenchmarkGLM 4.7Qwen3.5-FlashLeader
Design Arena: websites1225Not measuredOnly GLM 4.7 measured
Design Arena: UI components1196Not measuredOnly GLM 4.7 measured
Design Arena: game dev1188Not measuredOnly GLM 4.7 measured
Design Arena: data viz1195Not measuredOnly GLM 4.7 measured
Design Arena: 3D1190Not measuredOnly GLM 4.7 measured
Design Arena: all code categories1216Not measuredOnly GLM 4.7 measured
Design Arena: SVG1142Not measuredOnly GLM 4.7 measured
Design Arena: ASCII art1165Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index171
General benchmarks for GLM 4.7 and Qwen3.5-Flash
BenchmarkGLM 4.7Qwen3.5-FlashLeader
SAGE (Vals AI)Not measured42.5%Only Qwen3.5-Flash measured
MedScribe (Vals AI)68.6%70.6%Qwen3.5-Flash
IFBench67.9%Not measuredOnly GLM 4.7 measured
Long context reasoning (AA-LCR)71.0%Not measuredOnly GLM 4.7 measured
DTBenchNot measured82.9%Only Qwen3.5-Flash measured
LMCANot measured29.1%Only Qwen3.5-Flash measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351397GLM 4.7
LMArena Text: instruction following14121374GLM 4.7
LMArena Text: creative writing14011343GLM 4.7
LMArena Text: English14531404GLM 4.7
LMArena Text: non-English14171385GLM 4.7
LMArena Text: Chinese14951445GLM 4.7
LMArena Text: German14261390GLM 4.7
LMArena Text: Japanese14391369GLM 4.7
LMArena Text: Korean13991344GLM 4.7
LMArena Text: French14321413GLM 4.7
LMArena Text: Spanish14341400GLM 4.7
LMArena Text: Russian14231380GLM 4.7
LMArena Text: Polish14351376GLM 4.7
LMArena Text: multi-turn14461393GLM 4.7
LMArena Text: longer queries14321392GLM 4.7
LMArena Text: writing and language14111369GLM 4.7
LMArena Text: entertainment and media14001347GLM 4.7
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 and Qwen3.5-Flash
BenchmarkGLM 4.7Qwen3.5-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$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 4.7$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 4.7$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 4.7 and Qwen3.5-Flash
SpecificationZ.ai GLM 4.7Qwen3.5-Flash
MakerZ.aiQwen
WeightsOpen weightsNo public weights listed
ReleasedDecember 22, 2025February 25, 2026
Context window205K1M
Input $/Mtok$0.60$0.065
Output $/Mtok$2.20$0.26
Coding index55.141.1
Independent measurements behind it22
Cost per solved task$0.29$0.40
SourceOpenRouter listingOpenRouter listing

Questions

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

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 41.1 for Qwen3.5-Flash. Pick Qwen3.5-Flash for output tokens 8.5x cheaper ($0.26 vs $2.20 per million) and a larger context window (1M against 205K tokens).

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

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and Qwen3.5-Flash $0.40.

Which has the bigger context window, GLM 4.7 or Qwen3.5-Flash?

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

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

48 benchmarks have a result for both models as of October 22, 2026. GLM 4.7'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.

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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-4-7-vs-qwen3-5-flash-02-23">LLMBenchmark.ai, GLM 4.7 vs Qwen3.5-Flash</a></p>