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DeepSeek V3.2 vs GLM 4.6

DeepSeek V3.2 leads GLM 4.6 on coding, 30.0 to 8.8 on the Vibe Coding Index, and DeepSeek V3.2 costs 2.5x less per output token ($0.88 vs $2.20 per million). Updated October 22, 2026; 61 benchmarks have a result for both.

Replace DeepSeek V3.2 Replace GLM 4.6
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
37.9
Output $/M
$0.88
30
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
31
vs boards won of 61 shared

DeepSeek V3.2 or GLM 4.6: which to pick

Pick DeepSeek V3.2 for coding: it scores 30.0 on the Vibe Coding Index against 8.8 for GLM 4.6.

Pick GLM 4.6 for a larger context window (205K against 164K tokens) and work measured by AA Coding Index or LiveCodeBench (Vals AI), where it leads.

Model ADeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved taskModel BGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved task
Shared results
61
DeepSeek V3.2 only
14
GLM 4.6 only
8
Like-for-like categories
8 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
DeepSeek V3.2 30.0 from 3 benchmarks, GLM 4.6 8.8 from 2 benchmarks.
Widest category
Agentic work, 13.7 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): DeepSeek V3.2 73.3% against GLM 4.6 54.0%, 19.3 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.88 against GLM 4.6 $2.20 per million output tokens, 2.5x apart.
Context window
DeepSeek V3.2 takes 164K tokens, GLM 4.6 205K.

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 4.6Ties
Coding83
Intelligence10
Agentic work10
Reasoning51
Math33
Knowledge46
Web design34
General514
All shared boards3031
CodingLike-for-likeSix of these boards feed the Vibe Coding Index83
Coding benchmarks for DeepSeek V3.2 and GLM 4.6
BenchmarkDeepSeek V3.2GLM 4.6Leader
SWE-bench Verified70.0%68.2%DeepSeek V3.2
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev13621341DeepSeek V3.2
AA Coding Index44.245.8GLM 4.6
LiveCodeBench (Vals AI)80.7%81.0%GLM 4.6
SWE-bench Verified (Vals AI)67.6%Not measuredOnly DeepSeek V3.2 measured
Vibe Code Bench (Vals AI)5.1%3.1%DeepSeek V3.2
LiveCodeBench (Artificial Analysis run)86.2%69.5%DeepSeek V3.2
Terminal-Bench Hard (Artificial Analysis run)35.6%28.8%DeepSeek V3.2
Terminal-Bench39.6%24.5%DeepSeek V3.2
ALE-BenchNot measured340.8Only GLM 4.6 measured
LMArena Text: coding14531449DeepSeek V3.2
LMArena Text: software and IT14491455GLM 4.6
LMArena WebDev: React1350Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev: HTML13711350DeepSeek V3.2
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 4.6
BenchmarkDeepSeek V3.2GLM 4.6Leader
AA Intelligence Index21.518.5DeepSeek 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 4.6
BenchmarkDeepSeek V3.2GLM 4.6Leader
Design Arena: agents building full-stack appsNot measured1029Only GLM 4.6 measured
Design Arena: agents building mobile appsNot measured1096Only GLM 4.6 measured
Design Arena: agents building Android appsNot measured1085Only GLM 4.6 measured
Design Arena: agents building Godot gamesNot measured1180Only GLM 4.6 measured
τ²-Bench Telecom90.6%76.9%DeepSeek V3.2
APEX-Agents21.3%Not measuredOnly DeepSeek V3.2 measured
Vending-Bench 2$1,034Not measuredOnly DeepSeek V3.2 measured
Berkeley Function Calling Leaderboard (BFCL)Not measured72.4%Only GLM 4.6 measured
BrowseComp-PlusNot measured48.2%Only GLM 4.6 measured
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
Reasoning benchmarks for DeepSeek V3.2 and GLM 4.6
BenchmarkDeepSeek V3.2GLM 4.6Leader
GPQA Diamond80.3%74.5%DeepSeek V3.2
GPQA Diamond (Artificial Analysis run)84.0%78.0%DeepSeek V3.2
Humanity's Last Exam (Artificial Analysis run)24.6%14.5%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 prompts14341440GLM 4.6
LMArena Text: expert14361432DeepSeek V3.2
LMArena Text: hard prompts in English14461445DeepSeek V3.2
Kagi LLM BenchmarkNot measured45.7%Only GLM 4.6 measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index33
Math benchmarks for DeepSeek V3.2 and GLM 4.6
BenchmarkDeepSeek V3.2GLM 4.6Leader
AIME84.6%92.7%GLM 4.6
MGSM (Vals AI)86.0%89.8%GLM 4.6
AA Math Index92.086.0DeepSeek V3.2
AIME 2025 (Artificial Analysis run)92.0%86.0%DeepSeek V3.2
LMArena Text: math14351432DeepSeek V3.2
LMArena Text: mathematical work14401447GLM 4.6
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index46
Knowledge benchmarks for DeepSeek V3.2 and GLM 4.6
BenchmarkDeepSeek V3.2GLM 4.6Leader
MMLU Pro84.9%82.2%DeepSeek V3.2
LegalBench (Vals AI)76.1%79.6%GLM 4.6
CaseLaw (Vals AI)55.4%Not measuredOnly DeepSeek V3.2 measured
CorpFin (Vals AI)51.0%56.8%GLM 4.6
TaxEval (Vals AI)68.2%66.2%DeepSeek V3.2
MedQA (Vals AI)93.9%92.2%DeepSeek V3.2
MMLU Pro (Artificial Analysis run)86.2%82.9%DeepSeek V3.2
LMArena Text: science14461456GLM 4.6
LMArena Text: medicine and healthcare14401455GLM 4.6
LMArena Text: legal and government14251448GLM 4.6
LMArena Text: business and finance14101416GLM 4.6
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index34
Web design benchmarks for DeepSeek V3.2 and GLM 4.6
BenchmarkDeepSeek V3.2GLM 4.6Leader
Design Arena: websites11751174DeepSeek V3.2
Design Arena: UI components11461145DeepSeek V3.2
Design Arena: game dev11311150GLM 4.6
Design Arena: data viz11591161GLM 4.6
Design Arena: 3D11321128DeepSeek V3.2
Design Arena: all code categories11631165GLM 4.6
Design Arena: SVG10311108GLM 4.6
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 index514
General benchmarks for DeepSeek V3.2 and GLM 4.6
BenchmarkDeepSeek V3.2GLM 4.6Leader
IFBench60.7%43.4%DeepSeek V3.2
Long context reasoning (AA-LCR)73.3%54.0%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 Text14251440GLM 4.6
LMArena Text: instruction following14131410DeepSeek V3.2
LMArena Text: creative writing14001412GLM 4.6
LMArena Text: English14391449GLM 4.6
LMArena Text: non-English14091426GLM 4.6
LMArena Text: Chinese14551499GLM 4.6
LMArena Text: German14131447GLM 4.6
LMArena Text: Japanese13741393GLM 4.6
LMArena Text: Korean13701400GLM 4.6
LMArena Text: French14291459GLM 4.6
LMArena Text: Spanish14241436GLM 4.6
LMArena Text: Russian14241418DeepSeek V3.2
LMArena Text: Polish14191447GLM 4.6
LMArena Text: multi-turn14271427GLM 4.6
LMArena Text: longer queries14281422DeepSeek V3.2
LMArena Text: writing and language14071415GLM 4.6
LMArena Text: entertainment and media13951409GLM 4.6
Neither model has a result on the other 23 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 4.6
BenchmarkDeepSeek V3.2GLM 4.6Leader
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 4.6$0.0017Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.017Fits in one request
GLM 4.6$0.037Fits in one request

DeepSeek V3.2 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.067Exceeds the context window
GLM 4.6$0.142Exceeds 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 4.6
SpecificationDeepSeek V3.2Z.ai GLM 4.6
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedDecember 1, 2025September 30, 2025
Context window164K205K
Input $/Mtok$0.29$0.60
Output $/Mtok$0.88$2.20
Coding index30.08.8
Independent measurements behind it32
Cost per solved task$0.99Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 better than GLM 4.6 for coding?

Pick DeepSeek V3.2 for coding: it scores 30.0 on the Vibe Coding Index against 8.8 for GLM 4.6. Pick GLM 4.6 for a larger context window (205K against 164K tokens) and work measured by AA Coding Index or LiveCodeBench (Vals AI), where it leads.

Which is cheaper, DeepSeek V3.2 or GLM 4.6?

DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; GLM 4.6 costs $0.60 and $2.20. Cost per solved task is known for DeepSeek V3.2 only.

Which has the bigger context window, DeepSeek V3.2 or GLM 4.6?

GLM 4.6, with 205K tokens against 164K.

How many benchmarks is the DeepSeek V3.2 vs GLM 4.6 comparison based on?

61 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks and GLM 4.6's on 2. Where only one model has a result, the page says so instead of naming a winner.

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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/deepseek-v3-2-vs-glm-4-6">LLMBenchmark.ai, DeepSeek V3.2 vs GLM 4.6</a></p>