Enter opens the highlighted result. Esc closes.

DeepSeek V3.2 Exp vs GLM 4.5 Air

DeepSeek V3.2 Exp leads GLM 4.5 Air on the Artificial Analysis Intelligence Index, 16.6 to 11.1, and DeepSeek V3.2 Exp costs 2.7x less per output token ($0.41 vs $1.10 per million). Updated October 21, 2026; 47 benchmarks have a result for both.

Replace DeepSeek V3.2 Exp Replace GLM 4.5 Air
DeepSeek V3.2 ExpDeepSeek
Coding
No data
Overall
No data
Output $/M
$0.41
43
GLM 4.5 AirZ.ai
Coding
No data
Overall
No data
Output $/M
$1.10
4
vs boards won of 47 shared

DeepSeek V3.2 Exp or GLM 4.5 Air: 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 2.7x cheaper ($0.41 vs $1.10 per million) and a larger context window (164K against 131K tokens).

Model ADeepSeek V3.2 ExpDeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BGLM 4.5 AirZ.aiNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
47
DeepSeek V3.2 Exp only
12
GLM 4.5 Air only
1
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 Air not benchmarked.
Widest category
Agentic work, 12.6 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 Air 46.7%, 25.7 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against GLM 4.5 Air $1.10 per million output tokens, 2.7x apart.
Context window
DeepSeek V3.2 Exp takes 164K tokens, GLM 4.5 Air 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 4.5 AirTies
Coding40
Intelligence10
Agentic work01
Reasoning60
Math40
Knowledge50
Web design52
General181
All shared boards434
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for DeepSeek V3.2 Exp and GLM 4.5 Air
BenchmarkDeepSeek V3.2 ExpGLM 4.5 AirLeader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena WebDev1272Not measuredOnly DeepSeek V3.2 Exp measured
LiveCodeBench (Artificial Analysis run)78.9%68.4%DeepSeek V3.2 Exp
Terminal-Bench Hard (Artificial Analysis run)31.1%20.5%DeepSeek V3.2 Exp
GSO-BenchNot measured2.9%Only GLM 4.5 Air measured
WeirdML39.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: coding14381397DeepSeek V3.2 Exp
LMArena Text: software and IT14381401DeepSeek V3.2 Exp
LMArena WebDev: HTML1281Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 30 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 Air
BenchmarkDeepSeek V3.2 ExpGLM 4.5 AirLeader
AA Intelligence Index16.611.1DeepSeek 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 Air
BenchmarkDeepSeek V3.2 ExpGLM 4.5 AirLeader
τ²-Bench Telecom33.9%46.5%GLM 4.5 Air
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 index60
Reasoning benchmarks for DeepSeek V3.2 Exp and GLM 4.5 Air
BenchmarkDeepSeek V3.2 ExpGLM 4.5 AirLeader
GPQA Diamond (Artificial Analysis run)79.7%73.3%DeepSeek V3.2 Exp
Humanity's Last Exam (Artificial Analysis run)14.9%7.0%DeepSeek V3.2 Exp
LMArena Text: hard prompts14291379DeepSeek V3.2 Exp
LMArena Text: expert14221370DeepSeek V3.2 Exp
LMArena Text: hard prompts in English14441388DeepSeek V3.2 Exp
Kagi LLM Benchmark52.2%43.0%DeepSeek V3.2 Exp
Neither model has a result on the other 16 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for DeepSeek V3.2 Exp and GLM 4.5 Air
BenchmarkDeepSeek V3.2 ExpGLM 4.5 AirLeader
AA Math Index87.780.7DeepSeek V3.2 Exp
AIME 2025 (Artificial Analysis run)87.7%80.7%DeepSeek V3.2 Exp
LMArena Text: math14221396DeepSeek V3.2 Exp
LMArena Text: mathematical work14331396DeepSeek V3.2 Exp
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for DeepSeek V3.2 Exp and GLM 4.5 Air
BenchmarkDeepSeek V3.2 ExpGLM 4.5 AirLeader
MMLU Pro (Artificial Analysis run)85.0%81.5%DeepSeek V3.2 Exp
LMArena Text: science14431407DeepSeek V3.2 Exp
LMArena Text: medicine and healthcare14681402DeepSeek V3.2 Exp
LMArena Text: legal and government14191382DeepSeek V3.2 Exp
LMArena Text: business and finance14121371DeepSeek V3.2 Exp
Neither model has a result on the other 13 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index52
Web design benchmarks for DeepSeek V3.2 Exp and GLM 4.5 Air
BenchmarkDeepSeek V3.2 ExpGLM 4.5 AirLeader
Design Arena: websites11791146DeepSeek V3.2 Exp
Design Arena: UI components11671123DeepSeek V3.2 Exp
Design Arena: game dev11441094DeepSeek V3.2 Exp
Design Arena: data viz11521186GLM 4.5 Air
Design Arena: 3D11481123DeepSeek V3.2 Exp
Design Arena: all code categories11711137DeepSeek V3.2 Exp
Design Arena: SVG10321071GLM 4.5 Air
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index181
General benchmarks for DeepSeek V3.2 Exp and GLM 4.5 Air
BenchmarkDeepSeek V3.2 ExpGLM 4.5 AirLeader
IFBench54.1%37.6%DeepSeek V3.2 Exp
Long context reasoning (AA-LCR)72.3%46.7%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
CL-bench13.2%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench Life9.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text14251384DeepSeek V3.2 Exp
LMArena Text: instruction following14011354DeepSeek V3.2 Exp
LMArena Text: creative writing14041343DeepSeek V3.2 Exp
LMArena Text: English14411397DeepSeek V3.2 Exp
LMArena Text: non-English14041366DeepSeek V3.2 Exp
LMArena Text: Chinese14611426DeepSeek V3.2 Exp
LMArena Text: German14401377DeepSeek V3.2 Exp
LMArena Text: Japanese13311348GLM 4.5 Air
LMArena Text: Korean13721308DeepSeek V3.2 Exp
LMArena Text: French14341400DeepSeek V3.2 Exp
LMArena Text: Spanish14411385DeepSeek V3.2 Exp
LMArena Text: Russian14121373DeepSeek V3.2 Exp
LMArena Text: Polish14351368DeepSeek V3.2 Exp
LMArena Text: multi-turn14221371DeepSeek V3.2 Exp
LMArena Text: longer queries14181366DeepSeek V3.2 Exp
LMArena Text: writing and language14071354DeepSeek V3.2 Exp
LMArena Text: entertainment and media14071339DeepSeek V3.2 Exp
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 4.5 Air
BenchmarkDeepSeek V3.2 ExpGLM 4.5 AirLeader
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 Air$0.0008Fits 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 Air$0.013Fits in one request

GLM 4.5 Air costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2 Exp$0.058Exceeds the context window
GLM 4.5 Air$0.051Exceeds the context window

GLM 4.5 Air 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 Air
SpecificationDeepSeek V3.2 ExpZ.ai GLM 4.5 Air
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedSeptember 29, 2025July 25, 2025
Context window164K131K
Input $/Mtok$0.27$0.20
Output $/Mtok$0.41$1.10
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 Air 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 2.7x cheaper ($0.41 vs $1.10 per million) and a larger context window (164K against 131K tokens).

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

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GLM 4.5 Air costs $0.20 and $1.10. Cost per solved task is known for neither of them.

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

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

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

47 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and GLM 4.5 Air's on 0. Where only one model has a result, the page says so instead of naming a winner.

Compare next