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DeepSeek V3.2 Exp vs GLM 4.5

DeepSeek V3.2 Exp leads GLM 4.5 on the Artificial Analysis Intelligence Index, 16.6 to 12.8, and DeepSeek V3.2 Exp costs 5.4x less per output token ($0.41 vs $2.20 per million). Updated October 22, 2026; 48 benchmarks have a result for both.

Replace DeepSeek V3.2 Exp Replace GLM 4.5
DeepSeek V3.2 ExpDeepSeek
Coding
No data
Overall
No data
Output $/M
$0.41
26
GLM 4.5Z.ai
Coding
No data
Overall
No data
Output $/M
$2.20
21
vs boards won of 48 shared, 1 tied

DeepSeek V3.2 Exp or GLM 4.5: which to pick

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

Pick DeepSeek V3.2 Exp for output tokens 5.4x cheaper ($0.41 vs $2.20 per million) and a larger context window (164K against 131K tokens).

Model ADeepSeek V3.2 ExpDeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BGLM 4.5Z.aiNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
48
DeepSeek V3.2 Exp only
11
GLM 4.5 only
13
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 Exp not benchmarked, GLM 4.5 not benchmarked.
Widest category
Agentic work, 9.1 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): DeepSeek V3.2 Exp 72.3% against GLM 4.5 52.7%, 19.7 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against GLM 4.5 $2.20 per million output tokens, 5.4x apart.
Context window
DeepSeek V3.2 Exp takes 164K tokens, GLM 4.5 131K.

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 4.5Ties
Coding32
Intelligence10
Agentic work01
Reasoning33
Math31
Knowledge23
Web design331 tie
General118
All shared boards26211 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index32
Coding benchmarks for DeepSeek V3.2 Exp and GLM 4.5
BenchmarkDeepSeek V3.2 ExpGLM 4.5Leader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
SWE-bench VerifiedNot measured64.2%Only GLM 4.5 measured
LMArena WebDev1272Not measuredOnly DeepSeek V3.2 Exp measured
LiveCodeBench (Vals AI)Not measured67.5%Only GLM 4.5 measured
LiveCodeBench (Artificial Analysis run)78.9%73.8%DeepSeek V3.2 Exp
Terminal-Bench Hard (Artificial Analysis run)31.1%22.0%DeepSeek V3.2 Exp
WeirdML39.5%40.6%GLM 4.5
ALE-BenchNot measured344.8Only GLM 4.5 measured
LMArena Text: coding14381434DeepSeek V3.2 Exp
LMArena Text: software and IT14381440GLM 4.5
LMArena WebDev: HTML1281Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V3.2 Exp and GLM 4.5
BenchmarkDeepSeek V3.2 ExpGLM 4.5Leader
AA Intelligence Index16.612.8DeepSeek 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 index01
Agentic work benchmarks for DeepSeek V3.2 Exp and GLM 4.5
BenchmarkDeepSeek V3.2 ExpGLM 4.5Leader
τ²-Bench Telecom33.9%43.0%GLM 4.5
TheAgentCompany42.9%Not measuredOnly DeepSeek V3.2 Exp measured
Berkeley Function Calling Leaderboard (BFCL)56.7%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index33
Reasoning benchmarks for DeepSeek V3.2 Exp and GLM 4.5
BenchmarkDeepSeek V3.2 ExpGLM 4.5Leader
GPQA DiamondNot measured72.2%Only GLM 4.5 measured
GPQA Diamond (Artificial Analysis run)79.7%78.2%DeepSeek V3.2 Exp
Humanity's Last Exam (Artificial Analysis run)14.9%13.0%DeepSeek V3.2 Exp
LMArena Text: hard prompts14291429GLM 4.5
LMArena Text: expert14221433GLM 4.5
LMArena Text: hard prompts in English14441429DeepSeek V3.2 Exp
Kagi LLM Benchmark52.2%57.9%GLM 4.5
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index31
Math benchmarks for DeepSeek V3.2 Exp and GLM 4.5
BenchmarkDeepSeek V3.2 ExpGLM 4.5Leader
AIMENot measured86.7%Only GLM 4.5 measured
MATH 500 (Vals AI)Not measured94.0%Only GLM 4.5 measured
MGSM (Vals AI)Not measured90.8%Only GLM 4.5 measured
AA Math Index87.773.7DeepSeek V3.2 Exp
AIME 2025 (Artificial Analysis run)87.7%73.7%DeepSeek V3.2 Exp
LMArena Text: math14221427GLM 4.5
LMArena Text: mathematical work14331430DeepSeek V3.2 Exp
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index23
Knowledge benchmarks for DeepSeek V3.2 Exp and GLM 4.5
BenchmarkDeepSeek V3.2 ExpGLM 4.5Leader
MMLU ProNot measured81.2%Only GLM 4.5 measured
LegalBench (Vals AI)Not measured75.6%Only GLM 4.5 measured
CorpFin (Vals AI)Not measured61.0%Only GLM 4.5 measured
TaxEval (Vals AI)Not measured72.4%Only GLM 4.5 measured
MedQA (Vals AI)Not measured90.0%Only GLM 4.5 measured
MMLU Pro (Artificial Analysis run)85.0%83.5%DeepSeek V3.2 Exp
LMArena Text: science14431444GLM 4.5
LMArena Text: medicine and healthcare14681455DeepSeek V3.2 Exp
LMArena Text: legal and government14191438GLM 4.5
LMArena Text: business and finance14121420GLM 4.5
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index33
Web design benchmarks for DeepSeek V3.2 Exp and GLM 4.5
BenchmarkDeepSeek V3.2 ExpGLM 4.5Leader
Design Arena: websites11781169DeepSeek V3.2 Exp
Design Arena: UI components11641141DeepSeek V3.2 Exp
Design Arena: game dev11441144Tie
Design Arena: data viz11521159GLM 4.5
Design Arena: 3D11481170GLM 4.5
Design Arena: all code categories11701165DeepSeek V3.2 Exp
Design Arena: SVG10311096GLM 4.5
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index118
General benchmarks for DeepSeek V3.2 Exp and GLM 4.5
BenchmarkDeepSeek V3.2 ExpGLM 4.5Leader
IFBench54.1%44.1%DeepSeek V3.2 Exp
Long context reasoning (AA-LCR)72.3%52.7%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
Creative writing (Lech Mazur)Not measured73.4%Only GLM 4.5 measured
CL-bench13.2%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench Life9.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text14251430GLM 4.5
LMArena Text: instruction following14011404GLM 4.5
LMArena Text: creative writing14041395DeepSeek V3.2 Exp
LMArena Text: English14411437DeepSeek V3.2 Exp
LMArena Text: non-English14041417GLM 4.5
LMArena Text: Chinese14611465GLM 4.5
LMArena Text: German14401407DeepSeek V3.2 Exp
LMArena Text: Japanese13311415GLM 4.5
LMArena Text: Korean13721380GLM 4.5
LMArena Text: French14341418DeepSeek V3.2 Exp
LMArena Text: Spanish14411454GLM 4.5
LMArena Text: Russian14121413GLM 4.5
LMArena Text: Polish14351422DeepSeek V3.2 Exp
LMArena Text: multi-turn14221415DeepSeek V3.2 Exp
LMArena Text: longer queries14181412DeepSeek V3.2 Exp
LMArena Text: writing and language14071403DeepSeek V3.2 Exp
LMArena Text: entertainment and media14071404DeepSeek V3.2 Exp
Neither model has a result on the other 21 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 4.5
BenchmarkDeepSeek V3.2 ExpGLM 4.5Leader
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 4.5$0.0017Fits 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 4.5$0.037Fits 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 4.5$0.142Exceeds 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 4.5
SpecificationDeepSeek V3.2 ExpZ.ai GLM 4.5
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedSeptember 29, 2025July 25, 2025
Context window164K131K
Input $/Mtok$0.27$0.60
Output $/Mtok$0.41$2.20
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 4.5 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 5.4x cheaper ($0.41 vs $2.20 per million) and a larger context window (164K against 131K tokens).

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

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GLM 4.5 costs $0.60 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 4.5?

DeepSeek V3.2 Exp, with 164K tokens against 131K.

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

48 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and GLM 4.5's on 0. Where only one model has a result, the page says so instead of naming a winner.

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