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GLM 4.6V vs Qwen3 VL 235B A22B Instruct

GLM 4.6V leads Qwen3 VL 235B A22B Instruct on the Artificial Analysis Intelligence Index, 11.2 to 9.9, and GLM 4.6V costs 2.1x less per output token ($0.90 vs $1.90 per million). Updated October 22, 2026; 38 benchmarks have a result for both.

Replace GLM 4.6V Replace Qwen3 VL 235B A22B Instruct
GLM 4.6VZ.ai
Coding
No data
Overall
No data
Output $/M
$0.90
2
Qwen3 VL 235B A22B InstructQwen
Coding
No data
Overall
No data
Output $/M
$1.90
36
vs boards won of 38 shared

GLM 4.6V or Qwen3 VL 235B A22B Instruct: which to pick

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

Pick GLM 4.6V for output tokens 2.1x cheaper ($0.90 vs $1.90 per million) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model AGLM 4.6VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3 VL 235B A22B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
38
GLM 4.6V only
0
Qwen3 VL 235B A22B Instruct only
11
Like-for-like categories
7 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 4.6V not benchmarked, Qwen3 VL 235B A22B Instruct not benchmarked.
Widest category
Agentic work, 22.5 points apart on average over 1 shared benchmark.
Widest single gap
IFBench: GLM 4.6V 30.1% against Qwen3 VL 235B A22B Instruct 56.5%, 26.4 points apart on a 0 to 100 scale.
Output price
GLM 4.6V $0.90 against Qwen3 VL 235B A22B Instruct $1.90 per million output tokens, 2.1x apart.
Context window
GLM 4.6V takes 131K tokens, Qwen3 VL 235B A22B Instruct 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 4.6VBarsQwen3 VL 235B A22B InstructTies
Coding13
Intelligence10
Agentic work01
Reasoning07
Math02
Knowledge05
General018
All shared boards236
CodingLike-for-likeSix of these boards feed the Vibe Coding Index13
Coding benchmarks for GLM 4.6V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.6VQwen3 VL 235B A22B InstructLeader
LiveCodeBench (Artificial Analysis run)41.1%64.6%Qwen3 VL 235B A22B Instruct
Terminal-Bench Hard (Artificial Analysis run)14.4%11.4%GLM 4.6V
LMArena Text: coding13891438Qwen3 VL 235B A22B Instruct
LMArena Text: software and IT13911442Qwen3 VL 235B A22B Instruct
Neither model has a result on the other 35 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.6V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.6VQwen3 VL 235B A22B InstructLeader
AA Intelligence Index11.29.9GLM 4.6V
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.6V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.6VQwen3 VL 235B A22B InstructLeader
τ²-Bench Telecom31.6%54.1%Qwen3 VL 235B A22B Instruct
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for GLM 4.6V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.6VQwen3 VL 235B A22B InstructLeader
GPQA Diamond (Artificial Analysis run)71.9%77.2%Qwen3 VL 235B A22B Instruct
Humanity's Last Exam (Artificial Analysis run)9.6%11.9%Qwen3 VL 235B A22B Instruct
LMArena Text: hard prompts13671428Qwen3 VL 235B A22B Instruct
LMArena Text: expert13711442Qwen3 VL 235B A22B Instruct
LMArena Text: hard prompts in English13911439Qwen3 VL 235B A22B Instruct
LMArena Vision: diagrams11821255Qwen3 VL 235B A22B Instruct
LMArena Vision: homework11551277Qwen3 VL 235B A22B Instruct
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 4.6V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.6VQwen3 VL 235B A22B InstructLeader
AA Math Index85.388.3Qwen3 VL 235B A22B Instruct
AIME 2025 (Artificial Analysis run)85.3%88.3%Qwen3 VL 235B A22B Instruct
LMArena Text: mathNot measured1425Only Qwen3 VL 235B A22B Instruct measured
LMArena Text: mathematical workNot measured1419Only Qwen3 VL 235B A22B Instruct measured
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for GLM 4.6V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.6VQwen3 VL 235B A22B InstructLeader
MMLU Pro (Artificial Analysis run)79.9%83.6%Qwen3 VL 235B A22B Instruct
LMArena Text: science13891444Qwen3 VL 235B A22B Instruct
LMArena Text: medicine and healthcare13891431Qwen3 VL 235B A22B Instruct
LMArena Text: legal and government13931438Qwen3 VL 235B A22B Instruct
LMArena Text: business and finance13591433Qwen3 VL 235B A22B Instruct
Neither model has a result on the other 13 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GLM 4.6V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.6VQwen3 VL 235B A22B InstructLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
General benchmarks for GLM 4.6V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.6VQwen3 VL 235B A22B InstructLeader
IFBench30.1%56.5%Qwen3 VL 235B A22B Instruct
Long context reasoning (AA-LCR)48.7%63.7%Qwen3 VL 235B A22B Instruct
LMArena Text13761420Qwen3 VL 235B A22B Instruct
LMArena Text: instruction following13501406Qwen3 VL 235B A22B Instruct
LMArena Text: creative writing13471366Qwen3 VL 235B A22B Instruct
LMArena Vision11641247Qwen3 VL 235B A22B Instruct
LMArena Text: English13951429Qwen3 VL 235B A22B Instruct
LMArena Text: non-English13581405Qwen3 VL 235B A22B Instruct
LMArena Text: Chinese14251463Qwen3 VL 235B A22B Instruct
LMArena Text: GermanNot measured1424Only Qwen3 VL 235B A22B Instruct measured
LMArena Text: JapaneseNot measured1385Only Qwen3 VL 235B A22B Instruct measured
LMArena Text: KoreanNot measured1394Only Qwen3 VL 235B A22B Instruct measured
LMArena Text: FrenchNot measured1452Only Qwen3 VL 235B A22B Instruct measured
LMArena Text: SpanishNot measured1428Only Qwen3 VL 235B A22B Instruct measured
LMArena Text: Russian13391408Qwen3 VL 235B A22B Instruct
LMArena Text: PolishNot measured1419Only Qwen3 VL 235B A22B Instruct measured
LMArena Text: multi-turn13591428Qwen3 VL 235B A22B Instruct
LMArena Text: longer queries13571419Qwen3 VL 235B A22B Instruct
LMArena Text: writing and language13471392Qwen3 VL 235B A22B Instruct
LMArena Text: entertainment and media13431368Qwen3 VL 235B A22B Instruct
LMArena Vision: English11791256Qwen3 VL 235B A22B Instruct
LMArena Vision: Chinese12171283Qwen3 VL 235B A22B Instruct
LMArena Vision: reading text11711253Qwen3 VL 235B A22B Instruct
LMArena Vision: humorNot measured1209Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: captioningNot measured1281Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: recognizing thingsNot measured1249Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: creative writing11131220Qwen3 VL 235B A22B Instruct
Neither model has a result on the other 19 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.6VQwen3 VL 235B A22B InstructLeader
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 4.6V$0.0008Fits in one request
Qwen3 VL 235B A22B Instruct$0.0012Fits in one request

GLM 4.6V costs less

Repository review

50K input + 3K output tokens

GLM 4.6V$0.018Fits in one request
Qwen3 VL 235B A22B Instruct$0.016Fits in one request

Qwen3 VL 235B A22B Instruct costs less

Agent loop

200K input + 10K output tokens

GLM 4.6V$0.069Exceeds the context window
Qwen3 VL 235B A22B Instruct$0.061Fits in one request

Qwen3 VL 235B A22B Instruct 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 4.6V and Qwen3 VL 235B A22B Instruct
SpecificationZ.ai GLM 4.6VQwen3 VL 235B A22B Instruct
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedDecember 8, 2025September 23, 2025
Context window131K262K
Input $/Mtok$0.30$0.21
Output $/Mtok$0.90$1.90
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6V better than Qwen3 VL 235B A22B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.6V for output tokens 2.1x cheaper ($0.90 vs $1.90 per million) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, GLM 4.6V or Qwen3 VL 235B A22B Instruct?

GLM 4.6V costs $0.30 per million input tokens and $0.90 per million output tokens; Qwen3 VL 235B A22B Instruct costs $0.21 and $1.90. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.6V or Qwen3 VL 235B A22B Instruct?

Qwen3 VL 235B A22B Instruct, with 262K tokens against 131K.

How many benchmarks is the GLM 4.6V vs Qwen3 VL 235B A22B Instruct comparison based on?

38 benchmarks have a result for both models as of October 22, 2026. GLM 4.6V's coding index rests on 0 independent benchmarks and Qwen3 VL 235B A22B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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