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DeepSeek V3.2 vs GLM 5.3

Updated October 21, 2026. 56 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 5.3
DeepSeek V3.2DeepSeek
Coding
54.4
Overall
21.5
Output $/M
$0.42
1
GLM 5.3Z.ai
Coding
73.1
Overall
44.8
Output $/M
$1.40
55
vs boards won of 56 shared

Decision reading

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 54.4 for DeepSeek V3.2.

Pick DeepSeek V3.2 when output tokens cost 3.3x less, or when the benchmark that matters is LiveCodeBench (Vals AI), where it leads.

Model ADeepSeek V3.2DeepSeek54.4/1002 independent benchmarks$0.99 per solved taskModel BGLM 5.3Z.ai73.1/1002 independent benchmarks$0.36 per solved task
Shared results
56
DeepSeek V3.2 only
19
GLM 5.3 only
44
Like-for-like categories
8 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 54.4 from 2 benchmarks, GLM 5.3 73.1 from 2 benchmarks.
Widest category
Agentic work, 39.9 points apart on average over 2 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): DeepSeek V3.2 5.1% against GLM 5.3 78.1%, 73.0 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.42 against GLM 5.3 $1.40 per million output tokens, 3.3x apart.
Context window
DeepSeek V3.2 takes 164K tokens, GLM 5.3 1.05M.

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 5.3Ties
Coding18
Intelligence02
Agentic work02
Reasoning06
Math02
Knowledge07
Web design08
General020
All shared boards155
CodingLike-for-likeFour of these boards feed the Vibe Coding Index18
Coding benchmarks for DeepSeek V3.2 and GLM 5.3
BenchmarkDeepSeek V3.2GLM 5.3Leader
SWE-bench Verified70.0%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LiveBench codingNot measured82.8%Only GLM 5.3 measured
LMArena WebDev13621621GLM 5.3
AA Coding Index44.274.8GLM 5.3
LiveCodeBench (Vals AI)80.7%80.5%DeepSeek V3.2
SWE-bench Verified (Vals AI)67.6%95.4%GLM 5.3
Terminal-Bench 4 (Vals AI)Not measured38.9%Only GLM 5.3 measured
Vibe Code Bench (Vals AI)5.1%78.1%GLM 5.3
Code migration (Vals AI)Not measured44.2%Only GLM 5.3 measured
ProgramBench (Vals AI)Not measured1.5%Only GLM 5.3 measured
IOI (Vals AI)Not measured68.4%Only GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)Not measured71.5%Only GLM 5.3 measured
Vibe Code Bench 1-100 (Vals AI)Not measured20.0%Only GLM 5.3 measured
SkillsBench (Vals AI)Not measured47.5%Only GLM 5.3 measured
CyberBench (Vals AI)Not measured72.1%Only GLM 5.3 measured
LiveCodeBench (Artificial Analysis run)86.2%Not measuredOnly DeepSeek V3.2 measured
SciCodeNot measured59.0%Only GLM 5.3 measured
Terminal-Bench Hard (Artificial Analysis run)35.6%Not measuredOnly DeepSeek V3.2 measured
Terminal-Bench39.6%Not measuredOnly DeepSeek V3.2 measured
FrontierCodeNot measured40.1%Only GLM 5.3 measured
FrontierSWENot measured30.2%Only GLM 5.3 measured
WeirdMLNot measured75.4%Only GLM 5.3 measured
ALE-BenchNot measured1317.4Only GLM 5.3 measured
LMArena Text: coding14531497GLM 5.3
LMArena Text: software and IT14491493GLM 5.3
LMArena WebDev: React13501641GLM 5.3
LMArena WebDev: HTML13711595GLM 5.3
Neither model has a result on the other 10 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for DeepSeek V3.2 and GLM 5.3
BenchmarkDeepSeek V3.2GLM 5.3Leader
AA Intelligence Index21.544.8GLM 5.3
Epoch Capabilities Index146.1156.0GLM 5.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for DeepSeek V3.2 and GLM 5.3
BenchmarkDeepSeek V3.2GLM 5.3Leader
AA Agentic IndexNot measured53.1Only GLM 5.3 measured
Design Arena: agents building mobile appsNot measured1194Only GLM 5.3 measured
Design Arena: HTML slidesNot measured1189Only GLM 5.3 measured
Design Arena: PowerPoint slidesNot measured1255Only GLM 5.3 measured
LiveBench researchNot measured73.2%Only GLM 5.3 measured
LiveBench browserNot measured0.0%Only GLM 5.3 measured
LiveBench long agenticNot measured35.9%Only GLM 5.3 measured
SREBench (Vals AI)Not measured9.2%Only GLM 5.3 measured
Finance Agent (Vals AI)Not measured55.8%Only GLM 5.3 measured
Tax Agent Bench (Vals AI)Not measured39.7%Only GLM 5.3 measured
Legal Research Bench (Vals AI)Not measured49.0%Only GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)Not measured14.6%Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)Not measured56.3%Only GLM 5.3 measured
Terminal-Bench-Science (Vals AI)Not measured5.7%Only GLM 5.3 measured
τ²-Bench Telecom90.6%Not measuredOnly DeepSeek V3.2 measured
APEX-Agents21.3%56.6%GLM 5.3
Vending-Bench 2$1,034$8,164GLM 5.3
Neither model has a result on the other 22 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for DeepSeek V3.2 and GLM 5.3
BenchmarkDeepSeek V3.2GLM 5.3Leader
GPQA Diamond80.3%88.1%GLM 5.3
LiveBench reasoningNot measured85.8%Only GLM 5.3 measured
MysteryMechanism (Vals AI)Not measured23.0%Only GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)84.0%91.7%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)24.6%42.3%GLM 5.3
GPQA Diamond (Epoch AI run)Not measured90.9%Only GLM 5.3 measured
ARC-AGI57.0%Not measuredOnly DeepSeek V3.2 measured
ARC-AGI-24.0%Not measuredOnly DeepSeek V3.2 measured
Chess puzzlesNot measured37.0%Only GLM 5.3 measured
Mystery game puzzlesNot measured33.0%Only GLM 5.3 measured
LMArena Text: hard prompts14341490GLM 5.3
LMArena Text: expert14361514GLM 5.3
LMArena Text: hard prompts in English14461496GLM 5.3
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for DeepSeek V3.2 and GLM 5.3
BenchmarkDeepSeek V3.2GLM 5.3Leader
AIME84.6%Not measuredOnly DeepSeek V3.2 measured
MGSM (Vals AI)86.0%Not measuredOnly DeepSeek V3.2 measured
AA Math Index92.0Not measuredOnly DeepSeek V3.2 measured
AIME 2025 (Artificial Analysis run)92.0%Not measuredOnly DeepSeek V3.2 measured
OTIS Mock AIMENot measured91.1%Only GLM 5.3 measured
FrontierMath, tiers 1 to 3Not measured68.8%Only GLM 5.3 measured
FrontierMath, tier 4Not measured29.3%Only GLM 5.3 measured
ProofBenchNot measured49.0%Only GLM 5.3 measured
LMArena Text: math14351495GLM 5.3
LMArena Text: mathematical work14401497GLM 5.3
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index07
Knowledge benchmarks for DeepSeek V3.2 and GLM 5.3
BenchmarkDeepSeek V3.2GLM 5.3Leader
MMLU Pro84.9%86.8%GLM 5.3
LegalBench (Vals AI)76.1%84.8%GLM 5.3
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%72.4%GLM 5.3
MedQA (Vals AI)93.9%Not measuredOnly DeepSeek V3.2 measured
MedCode (Vals AI)Not measured42.9%Only GLM 5.3 measured
MMLU Pro (Artificial Analysis run)86.2%Not measuredOnly DeepSeek V3.2 measured
SimpleQA VerifiedNot measured41.0%Only GLM 5.3 measured
LMArena Text: science14461498GLM 5.3
LMArena Text: medicine and healthcare14401481GLM 5.3
LMArena Text: legal and government14251473GLM 5.3
LMArena Text: business and finance14101459GLM 5.3
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for DeepSeek V3.2 and GLM 5.3
BenchmarkDeepSeek V3.2GLM 5.3Leader
Design Arena: websites11751295GLM 5.3
Design Arena: UI components11491314GLM 5.3
Design Arena: game dev11321311GLM 5.3
Design Arena: data viz11581260GLM 5.3
Design Arena: 3D11331332GLM 5.3
Design Arena: all code categories11641304GLM 5.3
Design Arena: SVG10321292GLM 5.3
Design Arena: ASCII art10821213GLM 5.3
LiveBench designNot measured77.3%Only GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
General benchmarks for DeepSeek V3.2 and GLM 5.3
BenchmarkDeepSeek V3.2GLM 5.3Leader
Vals IndexNot measured59.7%Only GLM 5.3 measured
LiveBench data analysisNot measured70.2%Only GLM 5.3 measured
Public Benefits Bench (Vals AI)Not measured68.5%Only GLM 5.3 measured
MedScribe (Vals AI)Not measured88.8%Only GLM 5.3 measured
IFBench60.7%Not measuredOnly DeepSeek V3.2 measured
Long context reasoning (AA-LCR)73.3%79.7%GLM 5.3
DTBench85.6%87.7%GLM 5.3
LMCA28.8%55.5%GLM 5.3
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text14251474GLM 5.3
LMArena Text: instruction following14131479GLM 5.3
LMArena Text: creative writing14001458GLM 5.3
LMArena Text: English14391482GLM 5.3
LMArena Text: non-English14091459GLM 5.3
LMArena Text: Chinese14551532GLM 5.3
LMArena Text: German14131488GLM 5.3
LMArena Text: Japanese13741460GLM 5.3
LMArena Text: Korean13701460GLM 5.3
LMArena Text: French14291503GLM 5.3
LMArena Text: Spanish14241462GLM 5.3
LMArena Text: Russian14241469GLM 5.3
LMArena Text: Polish14191466GLM 5.3
LMArena Text: multi-turn14271472GLM 5.3
LMArena Text: longer queries14281483GLM 5.3
LMArena Text: writing and language14071469GLM 5.3
LMArena Text: entertainment and media13951447GLM 5.3
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 and GLM 5.3
BenchmarkDeepSeek V3.2GLM 5.3Leader
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$0.0005Fits in one request
GLM 5.3$0.0008Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.015Fits in one request
GLM 5.3$0.0081Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.060Exceeds the context window
GLM 5.3$0.030Fits in one request

GLM 5.3 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 5.3
SpecificationDeepSeek V3.2Z.ai GLM 5.3
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedDecember 1, 2025August 18, 2026
Context window164K1.05M
Input $/Mtok$0.28$0.079
Output $/Mtok$0.42$1.40
Coding index54.473.1
Independent measurements behind it22
Cost per solved task$0.99$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 better than GLM 5.3 for coding?

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 54.4 for DeepSeek V3.2. Pick DeepSeek V3.2 when output tokens cost 3.3x less, or when the benchmark that matters is LiveCodeBench (Vals AI), where it leads.

Which is cheaper, DeepSeek V3.2 or GLM 5.3?

DeepSeek V3.2 costs $0.28 per million input tokens and $0.42 per million output tokens; GLM 5.3 costs $0.079 and $1.40. Per solved SWE-bench Verified task, DeepSeek V3.2 costs $0.99 and GLM 5.3 $0.36.

Which has the bigger context window, DeepSeek V3.2 or GLM 5.3?

GLM 5.3, with 1.05M tokens against 164K.

How many benchmarks is the DeepSeek V3.2 vs GLM 5.3 comparison based on?

56 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 2 independent benchmarks and GLM 5.3's on 2. Where only one model has a result, the page says so instead of naming a winner.

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