Enter opens the highlighted result. Esc closes.

DeepSeek V3.2 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 Replace GLM 4.5V
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
21.5
Output $/M
$0.88
35
GLM 4.5VZ.ai
Coding
No data
Overall
7.6
Output $/M
$1.80
0
vs boards won of 35 shared

Decision reading

Only DeepSeek V3.2 has a coding score, 30.0 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.

Model ADeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved taskModel BGLM 4.5VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
35
DeepSeek V3.2 only
40
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 30.0 from 3 benchmarks, GLM 4.5V not benchmarked.
Widest category
Agentic work, 68.1 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): DeepSeek V3.2 73.3% against GLM 4.5V 0.0%, 73.3 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.88 against GLM 4.5V $1.80 per million output tokens, 2.0x apart.
Context window
DeepSeek V3.2 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 V3.2BarsGLM 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 and GLM 4.5V
BenchmarkDeepSeek V3.2GLM 4.5VLeader
SWE-bench Verified70.0%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev1362Not measuredOnly DeepSeek V3.2 measured
AA Coding Index44.2Not measuredOnly DeepSeek V3.2 measured
LiveCodeBench (Vals AI)80.7%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Verified (Vals AI)67.6%Not measuredOnly DeepSeek V3.2 measured
Vibe Code Bench (Vals AI)5.1%Not measuredOnly DeepSeek V3.2 measured
LiveCodeBench (Artificial Analysis run)86.2%60.4%DeepSeek V3.2
Terminal-Bench Hard (Artificial Analysis run)35.6%6.8%DeepSeek V3.2
Terminal-Bench39.6%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: coding14531347DeepSeek V3.2
LMArena Text: software and IT14491353DeepSeek V3.2
LMArena WebDev: React1350Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev: HTML1371Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V3.2 and GLM 4.5V
BenchmarkDeepSeek V3.2GLM 4.5VLeader
AA Intelligence Index21.57.6DeepSeek V3.2
Epoch Capabilities Index146.1Not measuredOnly DeepSeek V3.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V3.2 and GLM 4.5V
BenchmarkDeepSeek V3.2GLM 4.5VLeader
τ²-Bench Telecom90.6%22.5%DeepSeek V3.2
APEX-Agents21.3%Not measuredOnly DeepSeek V3.2 measured
Vending-Bench 2$1,034Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for DeepSeek V3.2 and GLM 4.5V
BenchmarkDeepSeek V3.2GLM 4.5VLeader
GPQA Diamond80.3%Not measuredOnly DeepSeek V3.2 measured
GPQA Diamond (Artificial Analysis run)84.0%68.4%DeepSeek V3.2
Humanity's Last Exam (Artificial Analysis run)24.6%6.3%DeepSeek V3.2
ARC-AGI57.0%Not measuredOnly DeepSeek V3.2 measured
ARC-AGI-24.0%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: hard prompts14341335DeepSeek V3.2
LMArena Text: expert14361353DeepSeek V3.2
LMArena Text: hard prompts in English14461357DeepSeek V3.2
LMArena Vision: diagramsNot measured1151Only GLM 4.5V measured
LMArena Vision: homeworkNot measured1163Only GLM 4.5V measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for DeepSeek V3.2 and GLM 4.5V
BenchmarkDeepSeek V3.2GLM 4.5VLeader
AIME84.6%Not measuredOnly DeepSeek V3.2 measured
MGSM (Vals AI)86.0%Not measuredOnly DeepSeek V3.2 measured
AA Math Index92.073.0DeepSeek V3.2
AIME 2025 (Artificial Analysis run)92.0%73.0%DeepSeek V3.2
LMArena Text: math14351354DeepSeek V3.2
LMArena Text: mathematical work14401336DeepSeek V3.2
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for DeepSeek V3.2 and GLM 4.5V
BenchmarkDeepSeek V3.2GLM 4.5VLeader
MMLU Pro84.9%Not measuredOnly DeepSeek V3.2 measured
LegalBench (Vals AI)76.1%Not measuredOnly DeepSeek V3.2 measured
CaseLaw (Vals AI)55.4%Not measuredOnly DeepSeek V3.2 measured
CorpFin (Vals AI)51.0%Not measuredOnly DeepSeek V3.2 measured
TaxEval (Vals AI)68.2%Not measuredOnly DeepSeek V3.2 measured
MedQA (Vals AI)93.9%Not measuredOnly DeepSeek V3.2 measured
MMLU Pro (Artificial Analysis run)86.2%78.8%DeepSeek V3.2
LMArena Text: science14461330DeepSeek V3.2
LMArena Text: medicine and healthcare14401349DeepSeek V3.2
LMArena Text: legal and government14251338DeepSeek V3.2
LMArena Text: business and finance14101309DeepSeek V3.2
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for DeepSeek V3.2 and GLM 4.5V
BenchmarkDeepSeek V3.2GLM 4.5VLeader
Design Arena: websites1175Not measuredOnly DeepSeek V3.2 measured
Design Arena: UI components1149Not measuredOnly DeepSeek V3.2 measured
Design Arena: game dev1131Not measuredOnly DeepSeek V3.2 measured
Design Arena: data viz1158Not measuredOnly DeepSeek V3.2 measured
Design Arena: 3D1133Not measuredOnly DeepSeek V3.2 measured
Design Arena: all code categories1164Not measuredOnly DeepSeek V3.2 measured
Design Arena: SVG1032Not measuredOnly DeepSeek V3.2 measured
Design Arena: ASCII art1082Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index150
General benchmarks for DeepSeek V3.2 and GLM 4.5V
BenchmarkDeepSeek V3.2GLM 4.5VLeader
IFBench60.7%34.2%DeepSeek V3.2
Long context reasoning (AA-LCR)73.3%0.0%DeepSeek V3.2
DTBench85.6%Not measuredOnly DeepSeek V3.2 measured
LMCA28.8%Not measuredOnly DeepSeek V3.2 measured
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text14251333DeepSeek V3.2
LMArena Text: instruction following14131311DeepSeek V3.2
LMArena Text: creative writing14001294DeepSeek V3.2
LMArena VisionNot measured1154Only GLM 4.5V measured
LMArena Text: English14391362DeepSeek V3.2
LMArena Text: non-English14091303DeepSeek V3.2
LMArena Text: Chinese14551337DeepSeek V3.2
LMArena Text: German1413Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Japanese1374Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Korean1370Not measuredOnly DeepSeek V3.2 measured
LMArena Text: French1429Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Spanish14241336DeepSeek V3.2
LMArena Text: Russian14241298DeepSeek V3.2
LMArena Text: Polish14191302DeepSeek V3.2
LMArena Text: multi-turn14271333DeepSeek V3.2
LMArena Text: longer queries14281305DeepSeek V3.2
LMArena Text: writing and language14071302DeepSeek V3.2
LMArena Text: entertainment and media13951303DeepSeek V3.2
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 20 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.2 and GLM 4.5V
BenchmarkDeepSeek V3.2GLM 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$0.0007Fits in one request
GLM 4.5V$0.0015Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.017Fits in one request
GLM 4.5V$0.035Fits in one request

DeepSeek V3.2 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.067Exceeds the context window
GLM 4.5V$0.138Exceeds the context window

DeepSeek V3.2 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 and GLM 4.5V
SpecificationDeepSeek V3.2Z.ai GLM 4.5V
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedDecember 1, 2025August 11, 2025
Context window164K66K
Input $/Mtok$0.29$0.60
Output $/Mtok$0.88$1.80
Coding index30.0Not benchmarked
Independent measurements behind it30
Cost per solved task$0.99Not computable
SourceOpenRouter listingOpenRouter listing

Questions

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

Only DeepSeek V3.2 has a coding score, 30.0 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, DeepSeek V3.2 or GLM 4.5V?

DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; GLM 4.5V costs $0.60 and $1.80. Cost per solved task is known for DeepSeek V3.2 only.

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

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

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

35 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks and GLM 4.5V's on 0. Where only one model has a result, the page says so instead of naming a winner.

Compare next