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DeepSeek V3.2 Exp vs GLM 4.6

Updated October 21, 2026. 48 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 4.6
DeepSeek V3.2 ExpDeepSeek
Coding
55.0
Overall
16.6
Output $/M
$0.41
15
GLM 4.6Z.ai
Coding
52.2
Overall
18.5
Output $/M
$2.20
33
vs boards won of 48 shared

Decision reading

Pick DeepSeek V3.2 Exp for coding: it scores 55.0 on the Vibe Coding Index against 52.2 for GLM 4.6.

Pick GLM 4.6 when the context window is larger, 205K against 164K tokens, or when what matters is LMArena WebDev or LMArena Text: coding, where it leads.

Model ADeepSeek V3.2 ExpDeepSeek55.0/1002 independent benchmarksNo cost per solved taskModel BGLM 4.6Z.ai52.2/1002 independent benchmarksNo cost per solved task
Shared results
48
DeepSeek V3.2 Exp only
9
GLM 4.6 only
18
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 Exp 55.0 from 2 benchmarks, GLM 4.6 52.2 from 2 benchmarks.
Widest category
Agentic work, 43.0 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: DeepSeek V3.2 Exp 33.9% against GLM 4.6 76.9%, 43.0 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against GLM 4.6 $2.20 per million output tokens, 5.4x apart.
Context window
DeepSeek V3.2 Exp 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 V3.2 ExpBarsGLM 4.6Ties
Coding24
Intelligence01
Agentic work01
Reasoning23
Math22
Knowledge23
Web design43
General316
All shared boards1533
CodingLike-for-likeFour of these boards feed the Vibe Coding Index24
Coding benchmarks for DeepSeek V3.2 Exp and GLM 4.6
BenchmarkDeepSeek V3.2 ExpGLM 4.6Leader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
SWE-bench VerifiedNot measured68.2%Only GLM 4.6 measured
LMArena WebDev12721341GLM 4.6
AA Coding IndexNot measured45.8Only GLM 4.6 measured
LiveCodeBench (Vals AI)Not measured81.0%Only GLM 4.6 measured
Vibe Code Bench (Vals AI)Not measured3.1%Only GLM 4.6 measured
LiveCodeBench (Artificial Analysis run)78.9%69.5%DeepSeek V3.2 Exp
Terminal-Bench Hard (Artificial Analysis run)31.1%28.8%DeepSeek V3.2 Exp
Terminal-BenchNot measured24.5%Only GLM 4.6 measured
WeirdML39.5%Not measuredOnly DeepSeek V3.2 Exp measured
ALE-BenchNot measured340.8Only GLM 4.6 measured
LMArena Text: coding14381449GLM 4.6
LMArena Text: software and IT14381455GLM 4.6
LMArena WebDev: HTML12811350GLM 4.6
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for DeepSeek V3.2 Exp and GLM 4.6
BenchmarkDeepSeek V3.2 ExpGLM 4.6Leader
AA Intelligence Index16.618.5GLM 4.6
Epoch Capabilities Index144.9Not measuredOnly DeepSeek V3.2 Exp measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V3.2 Exp and GLM 4.6
BenchmarkDeepSeek V3.2 ExpGLM 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 measured1090Only GLM 4.6 measured
Design Arena: agents building Godot gamesNot measured1180Only GLM 4.6 measured
τ²-Bench Telecom33.9%76.9%GLM 4.6
TheAgentCompany42.9%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index23
Reasoning benchmarks for DeepSeek V3.2 Exp and GLM 4.6
BenchmarkDeepSeek V3.2 ExpGLM 4.6Leader
GPQA DiamondNot measured74.5%Only GLM 4.6 measured
GPQA Diamond (Artificial Analysis run)79.7%78.0%DeepSeek V3.2 Exp
Humanity's Last Exam (Artificial Analysis run)14.9%14.5%DeepSeek V3.2 Exp
LMArena Text: hard prompts14291440GLM 4.6
LMArena Text: expert14221432GLM 4.6
LMArena Text: hard prompts in English14441445GLM 4.6
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index22
Math benchmarks for DeepSeek V3.2 Exp and GLM 4.6
BenchmarkDeepSeek V3.2 ExpGLM 4.6Leader
AIMENot measured92.7%Only GLM 4.6 measured
MGSM (Vals AI)Not measured89.8%Only GLM 4.6 measured
AA Math Index87.786.0DeepSeek V3.2 Exp
AIME 2025 (Artificial Analysis run)87.7%86.0%DeepSeek V3.2 Exp
LMArena Text: math14221432GLM 4.6
LMArena Text: mathematical work14331447GLM 4.6
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index23
Knowledge benchmarks for DeepSeek V3.2 Exp and GLM 4.6
BenchmarkDeepSeek V3.2 ExpGLM 4.6Leader
MMLU ProNot measured82.2%Only GLM 4.6 measured
LegalBench (Vals AI)Not measured79.6%Only GLM 4.6 measured
CorpFin (Vals AI)Not measured56.8%Only GLM 4.6 measured
TaxEval (Vals AI)Not measured66.2%Only GLM 4.6 measured
MedQA (Vals AI)Not measured92.2%Only GLM 4.6 measured
MMLU Pro (Artificial Analysis run)85.0%82.9%DeepSeek V3.2 Exp
LMArena Text: science14431456GLM 4.6
LMArena Text: medicine and healthcare14681455DeepSeek V3.2 Exp
LMArena Text: legal and government14191448GLM 4.6
LMArena Text: business and finance14121416GLM 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 index43
Web design benchmarks for DeepSeek V3.2 Exp and GLM 4.6
BenchmarkDeepSeek V3.2 ExpGLM 4.6Leader
Design Arena: websites11791174DeepSeek V3.2 Exp
Design Arena: UI components11661147DeepSeek V3.2 Exp
Design Arena: game dev11451151GLM 4.6
Design Arena: data viz11521161GLM 4.6
Design Arena: 3D11481129DeepSeek V3.2 Exp
Design Arena: all code categories11711166DeepSeek V3.2 Exp
Design Arena: SVG10311108GLM 4.6
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index316
General benchmarks for DeepSeek V3.2 Exp and GLM 4.6
BenchmarkDeepSeek V3.2 ExpGLM 4.6Leader
IFBench54.1%43.4%DeepSeek V3.2 Exp
Long context reasoning (AA-LCR)72.3%54.0%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 Text14251440GLM 4.6
LMArena Text: instruction following14011410GLM 4.6
LMArena Text: creative writing14041412GLM 4.6
LMArena Text: English14411449GLM 4.6
LMArena Text: non-English14041426GLM 4.6
LMArena Text: Chinese14611499GLM 4.6
LMArena Text: German14401447GLM 4.6
LMArena Text: Japanese13311393GLM 4.6
LMArena Text: Korean13721400GLM 4.6
LMArena Text: French14341459GLM 4.6
LMArena Text: Spanish14411436DeepSeek V3.2 Exp
LMArena Text: Russian14121418GLM 4.6
LMArena Text: Polish14351447GLM 4.6
LMArena Text: multi-turn14221427GLM 4.6
LMArena Text: longer queries14181422GLM 4.6
LMArena Text: writing and language14071415GLM 4.6
LMArena Text: entertainment and media14071409GLM 4.6
Neither model has a result on the other 22 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 4.6
BenchmarkDeepSeek V3.2 ExpGLM 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 Exp$0.0005Fits in one request
GLM 4.6$0.0016Fits 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 4.6$0.034Fits 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 4.6$0.132Exceeds 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 4.6
SpecificationDeepSeek V3.2 ExpZ.ai GLM 4.6
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedSeptember 29, 2025September 30, 2025
Context window164K205K
Input $/Mtok$0.27$0.55
Output $/Mtok$0.41$2.20
Coding index55.052.2
Independent measurements behind it22
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

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

Pick DeepSeek V3.2 Exp for coding: it scores 55.0 on the Vibe Coding Index against 52.2 for GLM 4.6. Pick GLM 4.6 when the context window is larger, 205K against 164K tokens, or when what matters is LMArena WebDev or LMArena Text: coding, where it leads.

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

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GLM 4.6 costs $0.55 and $2.20. Cost per solved task is known for neither of them.

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

GLM 4.6, with 205K tokens against 164K.

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

48 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 2 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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