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GLM 4.5V vs Qwen3.5 Plus 2026-04-20

GLM 4.5V and Qwen3.5 Plus 2026-04-20 cost about the same per output token ($1.80 vs $1.80 per million). Updated October 22, 2026; no benchmark has tested both models yet.

GLM 4.5VZ.ai
Coding
No data
Overall
No data
Output $/M
$1.80
Qwen3.5 Plus 2026-04-20Qwen
Coding
39.5
Overall
No data
Output $/M
$1.80
vs $1.80 vs $1.80 per 1M output tokens. About the same price.

GLM 4.5V or Qwen3.5 Plus 2026-04-20: which to pick

Only Qwen3.5 Plus 2026-04-20 has a coding score, 39.5 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

No benchmark has tested both yet. GLM 4.5V has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and LMArena Text: coding; Qwen3.5 Plus 2026-04-20 on LiveCodeBench (Vals AI), SWE-bench Verified (Vals AI) and Vibe Code Bench (Vals AI). Compare them on price and context instead.

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, Qwen3.5 Plus 2026-04-20 39.5 from 2 benchmarks.
Widest category
None yet: no category has been tested on both models.
Widest single gap
None yet: no benchmark has tested both models.
Output price
Both cost $1.80 per million output tokens.
Context window
GLM 4.5V takes 66K tokens, Qwen3.5 Plus 2026-04-20 1M.

Benchmark by benchmark

Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.

CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards measured
Coding benchmarks for GLM 4.5V and Qwen3.5 Plus 2026-04-20
BenchmarkGLM 4.5VQwen3.5 Plus 2026-04-20Leader
LiveCodeBench (Vals AI)Not measured85.3%Only Qwen3.5 Plus 2026-04-20 measured
SWE-bench Verified (Vals AI)Not measured71.2%Only Qwen3.5 Plus 2026-04-20 measured
Vibe Code Bench (Vals AI)Not measured15.7%Only Qwen3.5 Plus 2026-04-20 measured
LiveCodeBench (Artificial Analysis run)60.4%Not measuredOnly GLM 4.5V measured
Terminal-Bench Hard (Artificial Analysis run)6.8%Not measuredOnly GLM 4.5V measured
ALE-BenchNot measured608.9Only Qwen3.5 Plus 2026-04-20 measured
LMArena Text: coding1347Not measuredOnly GLM 4.5V measured
LMArena Text: software and IT1353Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 31 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
Intelligence benchmarks for GLM 4.5V and Qwen3.5 Plus 2026-04-20
BenchmarkGLM 4.5VQwen3.5 Plus 2026-04-20Leader
AA Intelligence Index7.6Not measuredOnly GLM 4.5V measured
Epoch Capabilities IndexNot measured146.2Only Qwen3.5 Plus 2026-04-20 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
Agentic work benchmarks for GLM 4.5V and Qwen3.5 Plus 2026-04-20
BenchmarkGLM 4.5VQwen3.5 Plus 2026-04-20Leader
τ²-Bench Telecom22.5%Not measuredOnly GLM 4.5V measured
Vending-Bench 2Not measured$1Only Qwen3.5 Plus 2026-04-20 measured
Neither model has a result on the other 39 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index13 boards measured
Reasoning benchmarks for GLM 4.5V and Qwen3.5 Plus 2026-04-20
BenchmarkGLM 4.5VQwen3.5 Plus 2026-04-20Leader
GPQA DiamondNot measured87.4%Only Qwen3.5 Plus 2026-04-20 measured
MMMU Pro (Vals AI)Not measured22.8%Only Qwen3.5 Plus 2026-04-20 measured
GPQA Diamond (Artificial Analysis run)68.4%Not measuredOnly GLM 4.5V measured
Humanity's Last Exam (Artificial Analysis run)6.3%Not measuredOnly GLM 4.5V measured
GPQA Diamond (Epoch AI run)Not measured84.8%Only Qwen3.5 Plus 2026-04-20 measured
Chess puzzlesNot measured8.0%Only Qwen3.5 Plus 2026-04-20 measured
Mystery game puzzlesNot measured17.0%Only Qwen3.5 Plus 2026-04-20 measured
LMArena Text: hard prompts1335Not measuredOnly GLM 4.5V measured
LMArena Text: expert1353Not measuredOnly GLM 4.5V measured
LMArena Text: hard prompts in English1357Not measuredOnly GLM 4.5V measured
LMArena Vision: diagrams1151Not measuredOnly GLM 4.5V measured
LMArena Vision: homework1163Not measuredOnly GLM 4.5V measured
Kagi LLM Benchmark59.8%Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 9 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
Math benchmarks for GLM 4.5V and Qwen3.5 Plus 2026-04-20
BenchmarkGLM 4.5VQwen3.5 Plus 2026-04-20Leader
AIMENot measured86.0%Only Qwen3.5 Plus 2026-04-20 measured
AA Math Index73.0Not measuredOnly GLM 4.5V measured
AIME 2025 (Artificial Analysis run)73.0%Not measuredOnly GLM 4.5V measured
OTIS Mock AIMENot measured86.7%Only Qwen3.5 Plus 2026-04-20 measured
LMArena Text: math1354Not measuredOnly GLM 4.5V measured
LMArena Text: mathematical work1336Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 7 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards measured
Knowledge benchmarks for GLM 4.5V and Qwen3.5 Plus 2026-04-20
BenchmarkGLM 4.5VQwen3.5 Plus 2026-04-20Leader
MMLU ProNot measured87.2%Only Qwen3.5 Plus 2026-04-20 measured
LegalBench (Vals AI)Not measured85.1%Only Qwen3.5 Plus 2026-04-20 measured
CaseLaw (Vals AI)Not measured59.7%Only Qwen3.5 Plus 2026-04-20 measured
CorpFin (Vals AI)Not measured65.3%Only Qwen3.5 Plus 2026-04-20 measured
MortgageTax (Vals AI)Not measured60.8%Only Qwen3.5 Plus 2026-04-20 measured
MedQA (Vals AI)Not measured95.2%Only Qwen3.5 Plus 2026-04-20 measured
MMLU Pro (Artificial Analysis run)78.8%Not measuredOnly GLM 4.5V measured
SimpleQA VerifiedNot measured25.4%Only Qwen3.5 Plus 2026-04-20 measured
LMArena Text: science1330Not measuredOnly GLM 4.5V measured
LMArena Text: medicine and healthcare1349Not measuredOnly GLM 4.5V measured
LMArena Text: legal and government1338Not measuredOnly GLM 4.5V measured
LMArena Text: business and finance1309Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 6 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index23 boards measured
General benchmarks for GLM 4.5V and Qwen3.5 Plus 2026-04-20
BenchmarkGLM 4.5VQwen3.5 Plus 2026-04-20Leader
SAGE (Vals AI)Not measured30.4%Only Qwen3.5 Plus 2026-04-20 measured
IFBench34.2%Not measuredOnly GLM 4.5V measured
Long context reasoning (AA-LCR)0.0%Not measuredOnly GLM 4.5V measured
DTBenchNot measured80.5%Only Qwen3.5 Plus 2026-04-20 measured
LMCANot measured36.4%Only Qwen3.5 Plus 2026-04-20 measured
CL-benchNot measured19.8%Only Qwen3.5 Plus 2026-04-20 measured
CL-bench LifeNot measured12.4%Only Qwen3.5 Plus 2026-04-20 measured
LMArena Text1333Not measuredOnly GLM 4.5V measured
LMArena Text: instruction following1311Not measuredOnly GLM 4.5V measured
LMArena Text: creative writing1294Not measuredOnly GLM 4.5V measured
LMArena Vision1154Not measuredOnly GLM 4.5V measured
LMArena Text: English1362Not measuredOnly GLM 4.5V measured
LMArena Text: non-English1303Not measuredOnly GLM 4.5V measured
LMArena Text: Chinese1337Not measuredOnly GLM 4.5V measured
LMArena Text: Spanish1336Not measuredOnly GLM 4.5V measured
LMArena Text: Russian1298Not measuredOnly GLM 4.5V measured
LMArena Text: Polish1302Not measuredOnly GLM 4.5V measured
LMArena Text: multi-turn1333Not measuredOnly GLM 4.5V measured
LMArena Text: longer queries1305Not measuredOnly GLM 4.5V measured
LMArena Text: writing and language1302Not measuredOnly GLM 4.5V measured
LMArena Text: entertainment and media1303Not measuredOnly GLM 4.5V measured
LMArena Vision: English1183Not measuredOnly GLM 4.5V measured
LMArena Vision: reading text1151Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 23 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
Qwen3.5 Plus 2026-04-20$0.0012Fits in one request

Qwen3.5 Plus 2026-04-20 costs less

Repository review

50K input + 3K output tokens

GLM 4.5V$0.035Fits in one request
Qwen3.5 Plus 2026-04-20$0.020Fits in one request

Qwen3.5 Plus 2026-04-20 costs less

Agent loop

200K input + 10K output tokens

GLM 4.5V$0.138Exceeds the context window
Qwen3.5 Plus 2026-04-20$0.078Fits in one request

Qwen3.5 Plus 2026-04-20 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 Qwen3.5 Plus 2026-04-20
SpecificationZ.ai GLM 4.5VQwen3.5 Plus 2026-04-20
MakerZ.aiQwen
WeightsOpen weightsNo public weights listed
ReleasedAugust 11, 2025April 27, 2026
Context window66K1M
Input $/Mtok$0.60$0.30
Output $/Mtok$1.80$1.80
Coding indexNot benchmarked39.5
Independent measurements behind it02
Cost per solved taskNot computable$1.35
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5V better than Qwen3.5 Plus 2026-04-20 for coding?

Only Qwen3.5 Plus 2026-04-20 has a coding score, 39.5 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, GLM 4.5V or Qwen3.5 Plus 2026-04-20?

GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; Qwen3.5 Plus 2026-04-20 costs $0.30 and $1.80. Cost per solved task is known for Qwen3.5 Plus 2026-04-20 only.

Which has the bigger context window, GLM 4.5V or Qwen3.5 Plus 2026-04-20?

Qwen3.5 Plus 2026-04-20, with 1M tokens against 66K.

Has any benchmark tested both GLM 4.5V and Qwen3.5 Plus 2026-04-20?

No benchmark has tested both yet. GLM 4.5V has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and LMArena Text: coding; Qwen3.5 Plus 2026-04-20 on LiveCodeBench (Vals AI), SWE-bench Verified (Vals AI) and Vibe Code Bench (Vals AI). Compare them on price and context instead. Data as of October 22, 2026.

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