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GLM 4.5V vs Qwen3 VL 235B A22B Instruct

Qwen3 VL 235B A22B Instruct leads GLM 4.5V on the Artificial Analysis Intelligence Index, 9.9 to 7.6, at about the same output price ($1.90 vs $1.80 per million). Updated October 22, 2026; 40 benchmarks have a result for both.

Replace GLM 4.5V Replace Qwen3 VL 235B A22B Instruct
GLM 4.5VZ.ai
Coding
No data
Overall
No data
Output $/M
$1.80
0
Qwen3 VL 235B A22B InstructQwen
Coding
No data
Overall
No data
Output $/M
$1.90
40
vs boards won of 40 shared

GLM 4.5V 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 4.5V for output tokens 1.1x cheaper ($1.80 vs $1.90 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model AGLM 4.5VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3 VL 235B A22B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
40
GLM 4.5V only
1
Qwen3 VL 235B A22B Instruct only
9
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 4.5V not benchmarked, Qwen3 VL 235B A22B Instruct not benchmarked.
Widest category
Agentic work, 31.6 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.5V 0.0% against Qwen3 VL 235B A22B Instruct 63.7%, 63.7 points apart on a 0 to 100 scale.
Output price
GLM 4.5V $1.80 against Qwen3 VL 235B A22B Instruct $1.90 per million output tokens, 1.1x apart.
Context window
GLM 4.5V takes 66K 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 4.5VBarsQwen3 VL 235B A22B InstructTies
Coding04
Intelligence01
Agentic work01
Reasoning07
Math04
Knowledge05
General018
All shared boards040
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for GLM 4.5V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.5VQwen3 VL 235B A22B InstructLeader
LiveCodeBench (Artificial Analysis run)60.4%64.6%Qwen3 VL 235B A22B Instruct
Terminal-Bench Hard (Artificial Analysis run)6.8%11.4%Qwen3 VL 235B A22B Instruct
LMArena Text: coding13471438Qwen3 VL 235B A22B Instruct
LMArena Text: software and IT13531442Qwen3 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 index01
Intelligence benchmarks for GLM 4.5V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.5VQwen3 VL 235B A22B InstructLeader
AA Intelligence Index7.69.9Qwen3 VL 235B A22B Instruct
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 4.5V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.5VQwen3 VL 235B A22B InstructLeader
τ²-Bench Telecom22.5%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 4.5V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.5VQwen3 VL 235B A22B InstructLeader
GPQA Diamond (Artificial Analysis run)68.4%77.2%Qwen3 VL 235B A22B Instruct
Humanity's Last Exam (Artificial Analysis run)6.3%11.9%Qwen3 VL 235B A22B Instruct
LMArena Text: hard prompts13351428Qwen3 VL 235B A22B Instruct
LMArena Text: expert13531442Qwen3 VL 235B A22B Instruct
LMArena Text: hard prompts in English13571439Qwen3 VL 235B A22B Instruct
LMArena Vision: diagrams11511255Qwen3 VL 235B A22B Instruct
LMArena Vision: homework11631277Qwen3 VL 235B A22B Instruct
Kagi LLM Benchmark59.8%Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for GLM 4.5V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.5VQwen3 VL 235B A22B InstructLeader
AA Math Index73.088.3Qwen3 VL 235B A22B Instruct
AIME 2025 (Artificial Analysis run)73.0%88.3%Qwen3 VL 235B A22B Instruct
LMArena Text: math13541425Qwen3 VL 235B A22B Instruct
LMArena Text: mathematical work13361419Qwen3 VL 235B A22B Instruct
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 4.5V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.5VQwen3 VL 235B A22B InstructLeader
MMLU Pro (Artificial Analysis run)78.8%83.6%Qwen3 VL 235B A22B Instruct
LMArena Text: science13301444Qwen3 VL 235B A22B Instruct
LMArena Text: medicine and healthcare13491431Qwen3 VL 235B A22B Instruct
LMArena Text: legal and government13381438Qwen3 VL 235B A22B Instruct
LMArena Text: business and finance13091433Qwen3 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 4.5V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.5VQwen3 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 4.5V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.5VQwen3 VL 235B A22B InstructLeader
IFBench34.2%56.5%Qwen3 VL 235B A22B Instruct
Long context reasoning (AA-LCR)0.0%63.7%Qwen3 VL 235B A22B Instruct
LMArena Text13331420Qwen3 VL 235B A22B Instruct
LMArena Text: instruction following13111406Qwen3 VL 235B A22B Instruct
LMArena Text: creative writing12941366Qwen3 VL 235B A22B Instruct
LMArena Vision11541247Qwen3 VL 235B A22B Instruct
LMArena Text: English13621429Qwen3 VL 235B A22B Instruct
LMArena Text: non-English13031405Qwen3 VL 235B A22B Instruct
LMArena Text: Chinese13371463Qwen3 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: Spanish13361428Qwen3 VL 235B A22B Instruct
LMArena Text: Russian12981408Qwen3 VL 235B A22B Instruct
LMArena Text: Polish13021419Qwen3 VL 235B A22B Instruct
LMArena Text: multi-turn13331428Qwen3 VL 235B A22B Instruct
LMArena Text: longer queries13051419Qwen3 VL 235B A22B Instruct
LMArena Text: writing and language13021392Qwen3 VL 235B A22B Instruct
LMArena Text: entertainment and media13031368Qwen3 VL 235B A22B Instruct
LMArena Vision: English11831256Qwen3 VL 235B A22B Instruct
LMArena Vision: ChineseNot measured1283Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: reading text11511253Qwen3 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 writingNot measured1220Only Qwen3 VL 235B A22B Instruct measured
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 4.5V and Qwen3 VL 235B A22B Instruct
BenchmarkGLM 4.5VQwen3 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 4.5V$0.0015Fits in one request
Qwen3 VL 235B A22B Instruct$0.0012Fits in one request

Qwen3 VL 235B A22B Instruct costs less

Repository review

50K input + 3K output tokens

GLM 4.5V$0.035Fits 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 4.5V$0.138Exceeds 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 4.5V and Qwen3 VL 235B A22B Instruct
SpecificationZ.ai GLM 4.5VQwen3 VL 235B A22B Instruct
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedAugust 11, 2025September 23, 2025
Context window66K262K
Input $/Mtok$0.60$0.21
Output $/Mtok$1.80$1.90
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5V 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 4.5V for output tokens 1.1x cheaper ($1.80 vs $1.90 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, GLM 4.5V or Qwen3 VL 235B A22B Instruct?

GLM 4.5V costs $0.60 per million input tokens and $1.80 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 4.5V or Qwen3 VL 235B A22B Instruct?

Qwen3 VL 235B A22B Instruct, with 262K tokens against 66K.

How many benchmarks is the GLM 4.5V vs Qwen3 VL 235B A22B Instruct comparison based on?

40 benchmarks have a result for both models as of October 22, 2026. GLM 4.5V'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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