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DeepSeek V3.2 Exp vs GLM 4.7 Flash

DeepSeek V3.2 Exp leads GLM 4.7 Flash on the Artificial Analysis Intelligence Index, 16.6 to 14.9, at about the same output price ($0.41 vs $0.40 per million). Updated October 22, 2026; 41 benchmarks have a result for both.

Replace DeepSeek V3.2 Exp Replace GLM 4.7 Flash
DeepSeek V3.2 ExpDeepSeek
Coding
No data
Overall
No data
Output $/M
$0.41
35
GLM 4.7 FlashZ.ai
Coding
No data
Overall
42.1
Output $/M
$0.40
6
vs boards won of 41 shared

DeepSeek V3.2 Exp or GLM 4.7 Flash: which to pick

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

Pick DeepSeek V3.2 Exp for work measured by τ²-Bench Telecom or Design Arena: websites, where it leads.

Model ADeepSeek V3.2 ExpDeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
41
DeepSeek V3.2 Exp only
18
GLM 4.7 Flash only
2
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 4.7 Flash not benchmarked.
Widest category
Agentic work, 64.9 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: DeepSeek V3.2 Exp 33.9% against GLM 4.7 Flash 98.8%, 64.9 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against GLM 4.7 Flash $0.40 per million output tokens, 1.0x apart.
Context window
DeepSeek V3.2 Exp takes 164K tokens, GLM 4.7 Flash 200K.

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 4.7 FlashTies
Coding30
Intelligence10
Agentic work01
Reasoning50
Math20
Knowledge40
Web design34
General171
All shared boards356
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for DeepSeek V3.2 Exp and GLM 4.7 Flash
BenchmarkDeepSeek V3.2 ExpGLM 4.7 FlashLeader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena WebDev1272Not measuredOnly DeepSeek V3.2 Exp measured
LiveCodeBench (Artificial Analysis run)78.9%Not measuredOnly DeepSeek V3.2 Exp measured
Terminal-Bench Hard (Artificial Analysis run)31.1%22.0%DeepSeek V3.2 Exp
WeirdML39.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: coding14381382DeepSeek V3.2 Exp
LMArena Text: software and IT14381389DeepSeek V3.2 Exp
LMArena WebDev: HTML1281Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 31 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V3.2 Exp and GLM 4.7 Flash
BenchmarkDeepSeek V3.2 ExpGLM 4.7 FlashLeader
AA Intelligence Index16.614.9DeepSeek 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 4.7 Flash
BenchmarkDeepSeek V3.2 ExpGLM 4.7 FlashLeader
τ²-Bench Telecom33.9%98.8%GLM 4.7 Flash
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 index50
Reasoning benchmarks for DeepSeek V3.2 Exp and GLM 4.7 Flash
BenchmarkDeepSeek V3.2 ExpGLM 4.7 FlashLeader
GPQA Diamond (Artificial Analysis run)79.7%58.1%DeepSeek V3.2 Exp
Humanity's Last Exam (Artificial Analysis run)14.9%7.6%DeepSeek V3.2 Exp
GPQA Diamond (Epoch AI run)Not measured60.5%Only GLM 4.7 Flash measured
LMArena Text: hard prompts14291356DeepSeek V3.2 Exp
LMArena Text: expert14221357DeepSeek V3.2 Exp
LMArena Text: hard prompts in English14441369DeepSeek V3.2 Exp
Kagi LLM Benchmark52.2%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for DeepSeek V3.2 Exp and GLM 4.7 Flash
BenchmarkDeepSeek V3.2 ExpGLM 4.7 FlashLeader
AA Math Index87.7Not measuredOnly DeepSeek V3.2 Exp measured
AIME 2025 (Artificial Analysis run)87.7%Not measuredOnly DeepSeek V3.2 Exp measured
OTIS Mock AIMENot measured58.3%Only GLM 4.7 Flash measured
LMArena Text: math14221355DeepSeek V3.2 Exp
LMArena Text: mathematical work14331369DeepSeek V3.2 Exp
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for DeepSeek V3.2 Exp and GLM 4.7 Flash
BenchmarkDeepSeek V3.2 ExpGLM 4.7 FlashLeader
MMLU Pro (Artificial Analysis run)85.0%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: science14431358DeepSeek V3.2 Exp
LMArena Text: medicine and healthcare14681343DeepSeek V3.2 Exp
LMArena Text: legal and government14191357DeepSeek V3.2 Exp
LMArena Text: business and finance14121343DeepSeek 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 index34
Web design benchmarks for DeepSeek V3.2 Exp and GLM 4.7 Flash
BenchmarkDeepSeek V3.2 ExpGLM 4.7 FlashLeader
Design Arena: websites11781194GLM 4.7 Flash
Design Arena: UI components11641205GLM 4.7 Flash
Design Arena: game dev11441134DeepSeek V3.2 Exp
Design Arena: data viz11521123DeepSeek V3.2 Exp
Design Arena: 3D11481120DeepSeek V3.2 Exp
Design Arena: all code categories11701177GLM 4.7 Flash
Design Arena: SVG10311037GLM 4.7 Flash
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index171
General benchmarks for DeepSeek V3.2 Exp and GLM 4.7 Flash
BenchmarkDeepSeek V3.2 ExpGLM 4.7 FlashLeader
IFBench54.1%60.8%GLM 4.7 Flash
Long context reasoning (AA-LCR)72.3%41.7%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 Text14251351DeepSeek V3.2 Exp
LMArena Text: instruction following14011327DeepSeek V3.2 Exp
LMArena Text: creative writing14041296DeepSeek V3.2 Exp
LMArena Text: English14411373DeepSeek V3.2 Exp
LMArena Text: non-English14041330DeepSeek V3.2 Exp
LMArena Text: Chinese14611403DeepSeek V3.2 Exp
LMArena Text: German14401336DeepSeek V3.2 Exp
LMArena Text: Japanese1331Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Korean13721283DeepSeek V3.2 Exp
LMArena Text: French14341332DeepSeek V3.2 Exp
LMArena Text: Spanish14411350DeepSeek V3.2 Exp
LMArena Text: Russian14121333DeepSeek V3.2 Exp
LMArena Text: Polish14351291DeepSeek V3.2 Exp
LMArena Text: multi-turn14221342DeepSeek V3.2 Exp
LMArena Text: longer queries14181345DeepSeek V3.2 Exp
LMArena Text: writing and language14071320DeepSeek V3.2 Exp
LMArena Text: entertainment and media14071304DeepSeek 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 4.7 Flash
BenchmarkDeepSeek V3.2 ExpGLM 4.7 FlashLeader
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 4.7 Flash$0.0003Fits in one request

GLM 4.7 Flash costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2 Exp$0.015Fits in one request
GLM 4.7 Flash$0.0045Fits in one request

GLM 4.7 Flash costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2 Exp$0.058Exceeds the context window
GLM 4.7 Flash$0.017Exceeds the context window

GLM 4.7 Flash 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 4.7 Flash
SpecificationDeepSeek V3.2 ExpZ.ai GLM 4.7 Flash
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedSeptember 29, 2025January 19, 2026
Context window164K200K
Input $/Mtok$0.27$0.0653
Output $/Mtok$0.41$0.40
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 4.7 Flash for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for work measured by τ²-Bench Telecom or Design Arena: websites, where it leads.

Which is cheaper, DeepSeek V3.2 Exp or GLM 4.7 Flash?

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GLM 4.7 Flash costs $0.0653 and $0.40. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V3.2 Exp or GLM 4.7 Flash?

GLM 4.7 Flash, with 200K tokens against 164K.

How many benchmarks is the DeepSeek V3.2 Exp vs GLM 4.7 Flash comparison based on?

41 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 4.7 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.

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