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DeepSeek V3.1 Terminus vs GLM 5.2

GLM 5.2 leads DeepSeek V3.1 Terminus on the Artificial Analysis Intelligence Index, 33.7 to 14.8, and DeepSeek V3.1 Terminus costs 4.3x less per output token ($1.0142 vs $4.40 per million). Updated October 21, 2026; 41 benchmarks have a result for both.

Replace DeepSeek V3.1 Terminus Replace GLM 5.2
DeepSeek V3.1 TerminusDeepSeek
Coding
No data
Overall
No data
Output $/M
$1.0142
0
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
41
vs boards won of 41 shared

DeepSeek V3.1 Terminus or GLM 5.2: which to pick

Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; DeepSeek V3.1 Terminus has none, so the two cannot be ranked on coding.

Pick DeepSeek V3.1 Terminus for output tokens 4.3x cheaper ($1.0142 vs $4.40 per million).

Model ADeepSeek V3.1 TerminusDeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved task
Shared results
41
DeepSeek V3.1 Terminus only
4
GLM 5.2 only
57
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 V3.1 Terminus not benchmarked, GLM 5.2 61.1 from 4 benchmarks.
Widest category
Agentic work, 46.8 points apart on average over 2 shared benchmarks.
Widest single gap
τ²-Bench Telecom: DeepSeek V3.1 Terminus 37.1% against GLM 5.2 99.1%, 62.0 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.1 Terminus $1.0142 against GLM 5.2 $4.40 per million output tokens, 4.3x apart.
Context window
DeepSeek V3.1 Terminus takes 164K tokens, GLM 5.2 1.05M.

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 V3.1 TerminusBarsGLM 5.2Ties
Coding06
Intelligence01
Agentic work02
Reasoning05
Math01
Knowledge04
Web design07
General015
All shared boards041
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
Coding benchmarks for DeepSeek V3.1 Terminus and GLM 5.2
BenchmarkDeepSeek V3.1 TerminusGLM 5.2Leader
LMArena WebDevNot measured1604Only GLM 5.2 measured
AA Coding Index43.568.8GLM 5.2
LiveCodeBench (Vals AI)Not measured69.5%Only GLM 5.2 measured
SWE-bench Verified (Vals AI)Not measured82.8%Only GLM 5.2 measured
Vibe Code Bench (Vals AI)Not measured64.0%Only GLM 5.2 measured
Code migration (Vals AI)Not measured37.9%Only GLM 5.2 measured
ProgramBench (Vals AI)Not measured0.5%Only GLM 5.2 measured
Terminal-Bench 2.1 (Vals AI)Not measured67.8%Only GLM 5.2 measured
SkillsBench (Vals AI)Not measured45.1%Only GLM 5.2 measured
LiveCodeBench (Artificial Analysis run)79.8%Not measuredOnly DeepSeek V3.1 Terminus measured
SciCode38.0%51.2%GLM 5.2
Terminal-Bench Hard (Artificial Analysis run)31.8%50.8%GLM 5.2
FrontierCodeNot measured24.5%Only GLM 5.2 measured
WeirdMLNot measured70.1%Only GLM 5.2 measured
ALE-Bench745.21047.0GLM 5.2
LMArena Text: coding14251487GLM 5.2
LMArena Text: software and IT14261486GLM 5.2
LMArena WebDev: ReactNot measured1617Only GLM 5.2 measured
LMArena WebDev: HTMLNot measured1565Only GLM 5.2 measured
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for DeepSeek V3.1 Terminus and GLM 5.2
BenchmarkDeepSeek V3.1 TerminusGLM 5.2Leader
AA Intelligence Index14.833.7GLM 5.2
Epoch Capabilities IndexNot measured151.9Only GLM 5.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for DeepSeek V3.1 Terminus and GLM 5.2
BenchmarkDeepSeek V3.1 TerminusGLM 5.2Leader
AA Agentic Index6.838.4GLM 5.2
Design Arena: agents building web appsNot measured1222Only GLM 5.2 measured
Design Arena: agents building full-stack appsNot measured1241Only GLM 5.2 measured
Design Arena: agents building mobile appsNot measured1160Only GLM 5.2 measured
Design Arena: agents building Android appsNot measured1178Only GLM 5.2 measured
Design Arena: agents building Godot gamesNot measured1142Only GLM 5.2 measured
Design Arena: agents building gamesNot measured1206Only GLM 5.2 measured
Design Arena: HTML slidesNot measured1175Only GLM 5.2 measured
Design Arena: PowerPoint slidesNot measured1195Only GLM 5.2 measured
SREBench (Vals AI)Not measured0.0%Only GLM 5.2 measured
Finance Agent (Vals AI)Not measured49.7%Only GLM 5.2 measured
Legal Research Bench (Vals AI)Not measured31.3%Only GLM 5.2 measured
Excel Modeling Benchmark (Vals AI)Not measured61.5%Only GLM 5.2 measured
τ²-Bench Telecom37.1%99.1%GLM 5.2
APEX-AgentsNot measured45.2%Only GLM 5.2 measured
PostTrainBenchNot measured31.7%Only GLM 5.2 measured
Vending-Bench 2Not measured$8,314Only GLM 5.2 measured
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for DeepSeek V3.1 Terminus and GLM 5.2
BenchmarkDeepSeek V3.1 TerminusGLM 5.2Leader
GPQA DiamondNot measured85.6%Only GLM 5.2 measured
GPQA Diamond (Artificial Analysis run)79.2%89.5%GLM 5.2
Humanity's Last Exam (Artificial Analysis run)16.4%41.1%GLM 5.2
GPQA Diamond (Epoch AI run)Not measured91.9%Only GLM 5.2 measured
ARC-AGINot measured77.0%Only GLM 5.2 measured
ARC-AGI-2Not measured22.8%Only GLM 5.2 measured
SimpleBenchNot measured58.8%Only GLM 5.2 measured
Chess puzzlesNot measured22.0%Only GLM 5.2 measured
Mystery game puzzlesNot measured19.0%Only GLM 5.2 measured
EBR-benchNot measured9.5%Only GLM 5.2 measured
LMArena Text: hard prompts14261481GLM 5.2
LMArena Text: expertNot measured1487Only GLM 5.2 measured
LMArena Text: hard prompts in English14321481GLM 5.2
Kagi LLM Benchmark57.4%62.6%GLM 5.2
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index01
Math benchmarks for DeepSeek V3.1 Terminus and GLM 5.2
BenchmarkDeepSeek V3.1 TerminusGLM 5.2Leader
AA Math Index89.7Not measuredOnly DeepSeek V3.1 Terminus measured
AIME 2025 (Artificial Analysis run)89.7%Not measuredOnly DeepSeek V3.1 Terminus measured
OTIS Mock AIMENot measured86.4%Only GLM 5.2 measured
FrontierMath, tiers 1 to 3Not measured59.2%Only GLM 5.2 measured
FrontierMath, tier 4Not measured29.3%Only GLM 5.2 measured
ProofBenchNot measured35.0%Only GLM 5.2 measured
LMArena Text: math14021483GLM 5.2
LMArena Text: mathematical workNot measured1489Only GLM 5.2 measured
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for DeepSeek V3.1 Terminus and GLM 5.2
BenchmarkDeepSeek V3.1 TerminusGLM 5.2Leader
MMLU ProNot measured86.7%Only GLM 5.2 measured
LegalBench (Vals AI)Not measured84.1%Only GLM 5.2 measured
CorpFin (Vals AI)Not measured66.1%Only GLM 5.2 measured
TaxEval (Vals AI)Not measured73.3%Only GLM 5.2 measured
MedCode (Vals AI)Not measured40.8%Only GLM 5.2 measured
MMLU Pro (Artificial Analysis run)85.1%Not measuredOnly DeepSeek V3.1 Terminus measured
SimpleQA VerifiedNot measured34.2%Only GLM 5.2 measured
LMArena Text: science14511489GLM 5.2
LMArena Text: medicine and healthcare14681475GLM 5.2
LMArena Text: legal and government14571486GLM 5.2
LMArena Text: business and finance14141451GLM 5.2
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for DeepSeek V3.1 Terminus and GLM 5.2
BenchmarkDeepSeek V3.1 TerminusGLM 5.2Leader
Design Arena: websites11871285GLM 5.2
Design Arena: UI components11821296GLM 5.2
Design Arena: game dev11311268GLM 5.2
Design Arena: data viz11671285GLM 5.2
Design Arena: 3D11411288GLM 5.2
Design Arena: all code categories11751288GLM 5.2
Design Arena: SVG10651214GLM 5.2
Design Arena: ASCII artNot measured1190Only GLM 5.2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index015
General benchmarks for DeepSeek V3.1 Terminus and GLM 5.2
BenchmarkDeepSeek V3.1 TerminusGLM 5.2Leader
MedScribe (Vals AI)Not measured83.5%Only GLM 5.2 measured
IFBench57.0%73.3%GLM 5.2
Long context reasoning (AA-LCR)69.3%78.3%GLM 5.2
DTBench81.3%93.6%GLM 5.2
LMCA28.6%45.8%GLM 5.2
Surface Evolver BenchNot measured55.6%Only GLM 5.2 measured
LMArena Text14191470GLM 5.2
LMArena Text: instruction following14041466GLM 5.2
LMArena Text: creative writing14031463GLM 5.2
LMArena Text: English14291475GLM 5.2
LMArena Text: non-English14081459GLM 5.2
LMArena Text: ChineseNot measured1522Only GLM 5.2 measured
LMArena Text: GermanNot measured1470Only GLM 5.2 measured
LMArena Text: JapaneseNot measured1450Only GLM 5.2 measured
LMArena Text: KoreanNot measured1440Only GLM 5.2 measured
LMArena Text: FrenchNot measured1479Only GLM 5.2 measured
LMArena Text: SpanishNot measured1478Only GLM 5.2 measured
LMArena Text: Russian14351468GLM 5.2
LMArena Text: Polish14271456GLM 5.2
LMArena Text: multi-turn14111469GLM 5.2
LMArena Text: longer queries14221480GLM 5.2
LMArena Text: writing and language14091463GLM 5.2
LMArena Text: entertainment and media13911450GLM 5.2
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 V3.1 Terminus and GLM 5.2
BenchmarkDeepSeek V3.1 TerminusGLM 5.2Leader
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 V3.1 Terminus$0.0008Fits in one request
GLM 5.2$0.0036Fits in one request

DeepSeek V3.1 Terminus costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.1 Terminus$0.018Fits in one request
GLM 5.2$0.083Fits in one request

DeepSeek V3.1 Terminus costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.1 Terminus$0.071Exceeds the context window
GLM 5.2$0.324Fits in one request

DeepSeek V3.1 Terminus 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 V3.1 Terminus and GLM 5.2
SpecificationDeepSeek V3.1 TerminusZ.ai GLM 5.2
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedSeptember 22, 2025June 16, 2026
Context window164K1.05M
Input $/Mtok$0.3063$1.40
Output $/Mtok$1.0142$4.40
Coding indexNot benchmarked61.1
Independent measurements behind it04
Cost per solved taskNot computable$0.86
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.1 Terminus better than GLM 5.2 for coding?

Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; DeepSeek V3.1 Terminus has none, so the two cannot be ranked on coding. Pick DeepSeek V3.1 Terminus for output tokens 4.3x cheaper ($1.0142 vs $4.40 per million).

Which is cheaper, DeepSeek V3.1 Terminus or GLM 5.2?

DeepSeek V3.1 Terminus costs $0.3063 per million input tokens and $1.0142 per million output tokens; GLM 5.2 costs $1.40 and $4.40. Cost per solved task is known for GLM 5.2 only.

Which has the bigger context window, DeepSeek V3.1 Terminus or GLM 5.2?

GLM 5.2, with 1.05M tokens against 164K.

How many benchmarks is the DeepSeek V3.1 Terminus vs GLM 5.2 comparison based on?

41 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.1 Terminus's coding index rests on 0 independent benchmarks and GLM 5.2's on 4. Where only one model has a result, the page says so instead of naming a winner.

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