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GLM 4.6V vs Qwen3.5-122B-A10B

Qwen3.5-122B-A10B leads GLM 4.6V on the Artificial Analysis Intelligence Index, 17.7 to 11.2, and GLM 4.6V costs 2.3x less per output token ($0.90 vs $2.08 per million). Updated October 22, 2026; 34 benchmarks have a result for both.

Replace GLM 4.6V Replace Qwen3.5-122B-A10B
GLM 4.6VZ.ai
Coding
No data
Overall
No data
Output $/M
$0.90
0
Qwen3.5-122B-A10BQwen
Coding
No data
Overall
No data
Output $/M
$2.08
34
vs boards won of 34 shared

GLM 4.6V or Qwen3.5-122B-A10B: 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 2.3x cheaper ($0.90 vs $2.08 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model AGLM 4.6VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-122B-A10BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
34
GLM 4.6V only
4
Qwen3.5-122B-A10B only
17
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 4.6V not benchmarked, Qwen3.5-122B-A10B not benchmarked.
Widest category
Agentic work, 62.0 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.6V 31.6% against Qwen3.5-122B-A10B 93.6%, 62.0 points apart on a 0 to 100 scale.
Output price
GLM 4.6V $0.90 against Qwen3.5-122B-A10B $2.08 per million output tokens, 2.3x apart.
Context window
GLM 4.6V takes 131K tokens, Qwen3.5-122B-A10B 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 4.6VBarsQwen3.5-122B-A10BTies
Coding03
Intelligence01
Agentic work01
Reasoning07
Knowledge04
General018
All shared boards034
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GLM 4.6V and Qwen3.5-122B-A10B
BenchmarkGLM 4.6VQwen3.5-122B-A10BLeader
LMArena WebDevNot measured1361Only Qwen3.5-122B-A10B measured
AA Coding IndexNot measured43.3Only Qwen3.5-122B-A10B measured
LiveCodeBench (Artificial Analysis run)41.1%Not measuredOnly GLM 4.6V measured
SciCodeNot measured39.7%Only Qwen3.5-122B-A10B measured
Terminal-Bench Hard (Artificial Analysis run)14.4%31.1%Qwen3.5-122B-A10B
LMArena Text: coding13891436Qwen3.5-122B-A10B
LMArena Text: software and IT13911436Qwen3.5-122B-A10B
LMArena WebDev: ReactNot measured1353Only Qwen3.5-122B-A10B measured
LMArena WebDev: HTMLNot measured1351Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6V and Qwen3.5-122B-A10B
BenchmarkGLM 4.6VQwen3.5-122B-A10BLeader
AA Intelligence Index11.217.7Qwen3.5-122B-A10B
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 Qwen3.5-122B-A10B
BenchmarkGLM 4.6VQwen3.5-122B-A10BLeader
τ²-Bench Telecom31.6%93.6%Qwen3.5-122B-A10B
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 Qwen3.5-122B-A10B
BenchmarkGLM 4.6VQwen3.5-122B-A10BLeader
GPQA Diamond (Artificial Analysis run)71.9%85.7%Qwen3.5-122B-A10B
Humanity's Last Exam (Artificial Analysis run)9.6%25.2%Qwen3.5-122B-A10B
Mystery game puzzlesNot measured17.0%Only Qwen3.5-122B-A10B measured
LMArena Text: hard prompts13671421Qwen3.5-122B-A10B
LMArena Text: expert13711431Qwen3.5-122B-A10B
LMArena Text: hard prompts in English13911431Qwen3.5-122B-A10B
LMArena Vision: diagrams11821249Qwen3.5-122B-A10B
LMArena Vision: homework11551261Qwen3.5-122B-A10B
Neither model has a result on the other 14 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Math benchmarks for GLM 4.6V and Qwen3.5-122B-A10B
BenchmarkGLM 4.6VQwen3.5-122B-A10BLeader
AA Math Index85.3Not measuredOnly GLM 4.6V measured
AIME 2025 (Artificial Analysis run)85.3%Not measuredOnly GLM 4.6V measured
LMArena Text: mathNot measured1431Only Qwen3.5-122B-A10B measured
LMArena Text: mathematical workNot measured1431Only Qwen3.5-122B-A10B measured
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 4.6V and Qwen3.5-122B-A10B
BenchmarkGLM 4.6VQwen3.5-122B-A10BLeader
MMLU Pro (Artificial Analysis run)79.9%Not measuredOnly GLM 4.6V measured
LMArena Text: science13891432Qwen3.5-122B-A10B
LMArena Text: medicine and healthcare13891423Qwen3.5-122B-A10B
LMArena Text: legal and government13931422Qwen3.5-122B-A10B
LMArena Text: business and finance13591414Qwen3.5-122B-A10B
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 Qwen3.5-122B-A10B
BenchmarkGLM 4.6VQwen3.5-122B-A10BLeader
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 Qwen3.5-122B-A10B
BenchmarkGLM 4.6VQwen3.5-122B-A10BLeader
IFBench30.1%75.7%Qwen3.5-122B-A10B
Long context reasoning (AA-LCR)48.7%76.3%Qwen3.5-122B-A10B
DTBenchNot measured84.3%Only Qwen3.5-122B-A10B measured
LMCANot measured32.2%Only Qwen3.5-122B-A10B measured
LMArena Text13761417Qwen3.5-122B-A10B
LMArena Text: instruction following13501399Qwen3.5-122B-A10B
LMArena Text: creative writing13471368Qwen3.5-122B-A10B
LMArena Vision11641244Qwen3.5-122B-A10B
LMArena Text: English13951428Qwen3.5-122B-A10B
LMArena Text: non-English13581401Qwen3.5-122B-A10B
LMArena Text: Chinese14251463Qwen3.5-122B-A10B
LMArena Text: GermanNot measured1425Only Qwen3.5-122B-A10B measured
LMArena Text: JapaneseNot measured1367Only Qwen3.5-122B-A10B measured
LMArena Text: KoreanNot measured1353Only Qwen3.5-122B-A10B measured
LMArena Text: FrenchNot measured1443Only Qwen3.5-122B-A10B measured
LMArena Text: SpanishNot measured1425Only Qwen3.5-122B-A10B measured
LMArena Text: Russian13391400Qwen3.5-122B-A10B
LMArena Text: PolishNot measured1398Only Qwen3.5-122B-A10B measured
LMArena Text: multi-turn13591416Qwen3.5-122B-A10B
LMArena Text: longer queries13571410Qwen3.5-122B-A10B
LMArena Text: writing and language13471387Qwen3.5-122B-A10B
LMArena Text: entertainment and media13431366Qwen3.5-122B-A10B
LMArena Vision: English11791249Qwen3.5-122B-A10B
LMArena Vision: Chinese12171301Qwen3.5-122B-A10B
LMArena Vision: reading text11711251Qwen3.5-122B-A10B
LMArena Vision: humorNot measured1243Only Qwen3.5-122B-A10B measured
LMArena Vision: creative writing11131220Qwen3.5-122B-A10B
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 Qwen3.5-122B-A10B
BenchmarkGLM 4.6VQwen3.5-122B-A10BLeader
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
Qwen3.5-122B-A10B$0.0013Fits in one request

GLM 4.6V costs less

Repository review

50K input + 3K output tokens

GLM 4.6V$0.018Fits in one request
Qwen3.5-122B-A10B$0.019Fits in one request

GLM 4.6V costs less

Agent loop

200K input + 10K output tokens

GLM 4.6V$0.069Exceeds the context window
Qwen3.5-122B-A10B$0.073Fits in one request

GLM 4.6V 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 Qwen3.5-122B-A10B
SpecificationZ.ai GLM 4.6VQwen3.5-122B-A10B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedDecember 8, 2025February 25, 2026
Context window131K262K
Input $/Mtok$0.30$0.26
Output $/Mtok$0.90$2.08
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6V better than Qwen3.5-122B-A10B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.6V for output tokens 2.3x cheaper ($0.90 vs $2.08 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, GLM 4.6V or Qwen3.5-122B-A10B?

GLM 4.6V costs $0.30 per million input tokens and $0.90 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.6V or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B, with 262K tokens against 131K.

How many benchmarks is the GLM 4.6V vs Qwen3.5-122B-A10B comparison based on?

34 benchmarks have a result for both models as of October 22, 2026. GLM 4.6V's coding index rests on 0 independent benchmarks and Qwen3.5-122B-A10B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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