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DeepSeek V3.2 Exp vs GLM 4.5V

Updated October 21, 2026. 35 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V3.2 Exp Replace GLM 4.5V
DeepSeek V3.2 ExpDeepSeek
Coding
No data
Overall
16.6
Output $/M
$0.41
35
GLM 4.5VZ.ai
Coding
No data
Overall
7.6
Output $/M
$1.80
0
vs boards won of 35 shared

Decision reading

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

DeepSeek V3.2 Exp has the edge where output tokens cost 4.4x less, and where the context window is larger, 164K against 66K tokens.

Model ADeepSeek V3.2 ExpDeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BGLM 4.5VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
35
DeepSeek V3.2 Exp only
22
GLM 4.5V only
5
Like-for-like categories
7 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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
DeepSeek V3.2 Exp not benchmarked, GLM 4.5V not benchmarked.
Widest category
General, 21.2 points apart on average over 15 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): DeepSeek V3.2 Exp 72.3% against GLM 4.5V 0.0%, 72.3 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against GLM 4.5V $1.80 per million output tokens, 4.4x apart.
Context window
DeepSeek V3.2 Exp takes 164K tokens, GLM 4.5V 66K.

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
CategoryDeepSeek V3.2 ExpBarsGLM 4.5VTies
Coding40
Intelligence10
Agentic work10
Reasoning50
Math40
Knowledge50
General150
All shared boards350
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for DeepSeek V3.2 Exp and GLM 4.5V
BenchmarkDeepSeek V3.2 ExpGLM 4.5VLeader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena WebDev1272Not measuredOnly DeepSeek V3.2 Exp measured
LiveCodeBench (Artificial Analysis run)78.9%60.4%DeepSeek V3.2 Exp
Terminal-Bench Hard (Artificial Analysis run)31.1%6.8%DeepSeek V3.2 Exp
WeirdML39.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: coding14381347DeepSeek V3.2 Exp
LMArena Text: software and IT14381353DeepSeek V3.2 Exp
LMArena WebDev: HTML1281Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V3.2 Exp and GLM 4.5V
BenchmarkDeepSeek V3.2 ExpGLM 4.5VLeader
AA Intelligence Index16.67.6DeepSeek V3.2 Exp
Epoch Capabilities Index144.9Not measuredOnly DeepSeek V3.2 Exp measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V3.2 Exp and GLM 4.5V
BenchmarkDeepSeek V3.2 ExpGLM 4.5VLeader
τ²-Bench Telecom33.9%22.5%DeepSeek V3.2 Exp
TheAgentCompany42.9%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for DeepSeek V3.2 Exp and GLM 4.5V
BenchmarkDeepSeek V3.2 ExpGLM 4.5VLeader
GPQA Diamond (Artificial Analysis run)79.7%68.4%DeepSeek V3.2 Exp
Humanity's Last Exam (Artificial Analysis run)14.9%6.3%DeepSeek V3.2 Exp
LMArena Text: hard prompts14291335DeepSeek V3.2 Exp
LMArena Text: expert14221353DeepSeek V3.2 Exp
LMArena Text: hard prompts in English14441357DeepSeek V3.2 Exp
LMArena Vision: diagramsNot measured1151Only GLM 4.5V measured
LMArena Vision: homeworkNot measured1163Only 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 index40
Math benchmarks for DeepSeek V3.2 Exp and GLM 4.5V
BenchmarkDeepSeek V3.2 ExpGLM 4.5VLeader
AA Math Index87.773.0DeepSeek V3.2 Exp
AIME 2025 (Artificial Analysis run)87.7%73.0%DeepSeek V3.2 Exp
LMArena Text: math14221354DeepSeek V3.2 Exp
LMArena Text: mathematical work14331336DeepSeek V3.2 Exp
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for DeepSeek V3.2 Exp and GLM 4.5V
BenchmarkDeepSeek V3.2 ExpGLM 4.5VLeader
MMLU Pro (Artificial Analysis run)85.0%78.8%DeepSeek V3.2 Exp
LMArena Text: science14431330DeepSeek V3.2 Exp
LMArena Text: medicine and healthcare14681349DeepSeek V3.2 Exp
LMArena Text: legal and government14191338DeepSeek V3.2 Exp
LMArena Text: business and finance14121309DeepSeek V3.2 Exp
Neither model has a result on the other 12 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for DeepSeek V3.2 Exp and GLM 4.5V
BenchmarkDeepSeek V3.2 ExpGLM 4.5VLeader
Design Arena: websites1179Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: UI components1167Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: game dev1144Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: data viz1152Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: 3D1148Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: all code categories1171Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: SVG1032Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index150
General benchmarks for DeepSeek V3.2 Exp and GLM 4.5V
BenchmarkDeepSeek V3.2 ExpGLM 4.5VLeader
IFBench54.1%34.2%DeepSeek V3.2 Exp
Long context reasoning (AA-LCR)72.3%0.0%DeepSeek V3.2 Exp
DTBench87.7%Not measuredOnly DeepSeek V3.2 Exp measured
LMCA29.1%Not measuredOnly DeepSeek V3.2 Exp measured
Fiction.LiveBench83.3%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench13.2%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench Life9.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text14251333DeepSeek V3.2 Exp
LMArena Text: instruction following14011311DeepSeek V3.2 Exp
LMArena Text: creative writing14041294DeepSeek V3.2 Exp
LMArena VisionNot measured1154Only GLM 4.5V measured
LMArena Text: English14411362DeepSeek V3.2 Exp
LMArena Text: non-English14041303DeepSeek V3.2 Exp
LMArena Text: Chinese14611337DeepSeek V3.2 Exp
LMArena Text: German1440Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Japanese1331Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Korean1372Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: French1434Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Spanish14411336DeepSeek V3.2 Exp
LMArena Text: Russian14121298DeepSeek V3.2 Exp
LMArena Text: Polish14351302DeepSeek V3.2 Exp
LMArena Text: multi-turn14221333DeepSeek V3.2 Exp
LMArena Text: longer queries14181305DeepSeek V3.2 Exp
LMArena Text: writing and language14071302DeepSeek V3.2 Exp
LMArena Text: entertainment and media14071303DeepSeek V3.2 Exp
LMArena Vision: EnglishNot measured1183Only GLM 4.5V measured
LMArena Vision: reading textNot measured1151Only GLM 4.5V 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 DeepSeek V3.2 Exp and GLM 4.5V
BenchmarkDeepSeek V3.2 ExpGLM 4.5VLeader
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

DeepSeek V3.2 Exp$0.0005Fits in one request
GLM 4.5V$0.0015Fits in one request

DeepSeek V3.2 Exp costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2 Exp$0.015Fits in one request
GLM 4.5V$0.035Fits in one request

DeepSeek V3.2 Exp costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2 Exp$0.058Exceeds the context window
GLM 4.5V$0.138Exceeds the context window

DeepSeek V3.2 Exp 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 DeepSeek V3.2 Exp and GLM 4.5V
SpecificationDeepSeek V3.2 ExpZ.ai GLM 4.5V
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedSeptember 29, 2025August 11, 2025
Context window164K66K
Input $/Mtok$0.27$0.60
Output $/Mtok$0.41$1.80
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 Exp better than GLM 4.5V for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. DeepSeek V3.2 Exp has the edge where output tokens cost 4.4x less, and where the context window is larger, 164K against 66K tokens.

Which is cheaper, DeepSeek V3.2 Exp or GLM 4.5V?

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GLM 4.5V costs $0.60 and $1.80. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V3.2 Exp or GLM 4.5V?

DeepSeek V3.2 Exp, with 164K tokens against 66K.

How many benchmarks is the DeepSeek V3.2 Exp vs GLM 4.5V comparison based on?

35 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and GLM 4.5V's on 0. Where only one model has a result, the page says so instead of naming a winner.

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