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DeepSeek V3.1 Terminus vs GLM 4.7

GLM 4.7 leads DeepSeek V3.1 Terminus on the Artificial Analysis Intelligence Index, 22.2 to 14.8, and DeepSeek V3.1 Terminus costs 2.2x less per output token ($1.0142 vs $2.20 per million). Updated October 21, 2026; 40 benchmarks have a result for both.

Replace DeepSeek V3.1 Terminus Replace GLM 4.7
DeepSeek V3.1 TerminusDeepSeek
Coding
No data
Overall
No data
Output $/M
$1.0142
4
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
35
vs boards won of 40 shared, 1 tied

DeepSeek V3.1 Terminus or GLM 4.7: which to pick

Only GLM 4.7 has a coding score, 55.1 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 2.2x cheaper ($1.0142 vs $2.20 per million) and work measured by ALE-Bench or LMArena Text: legal and government, where it leads.

Model ADeepSeek V3.1 TerminusDeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved task
Shared results
40
DeepSeek V3.1 Terminus only
5
GLM 4.7 only
41
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 4.7 55.1 from 2 benchmarks.
Widest category
Agentic work, 58.8 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: DeepSeek V3.1 Terminus 37.1% against GLM 4.7 95.9%, 58.8 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.1 Terminus $1.0142 against GLM 4.7 $2.20 per million output tokens, 2.2x apart.
Context window
DeepSeek V3.1 Terminus takes 164K tokens, GLM 4.7 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 V3.1 TerminusBarsGLM 4.7Ties
Coding141 tie
Intelligence01
Agentic work01
Reasoning04
Math03
Knowledge14
Web design07
General211
All shared boards4351 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index14
Coding benchmarks for DeepSeek V3.1 Terminus and GLM 4.7
BenchmarkDeepSeek V3.1 TerminusGLM 4.7Leader
LMArena WebDevNot measured1435Only GLM 4.7 measured
AA Coding Index43.545.3GLM 4.7
LiveCodeBench (Vals AI)Not measured82.2%Only GLM 4.7 measured
SWE-bench Verified (Vals AI)Not measured69.4%Only GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)79.8%89.4%GLM 4.7
SciCode38.0%Not measuredOnly DeepSeek V3.1 Terminus measured
Terminal-Bench Hard (Artificial Analysis run)31.8%31.8%Tie
Terminal-BenchNot measured33.4%Only GLM 4.7 measured
ALE-Bench745.2399.5DeepSeek V3.1 Terminus
LMArena Text: coding14251454GLM 4.7
LMArena Text: software and IT14261453GLM 4.7
LMArena WebDev: ReactNot measured1420Only GLM 4.7 measured
LMArena WebDev: HTMLNot measured1447Only GLM 4.7 measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for DeepSeek V3.1 Terminus and GLM 4.7
BenchmarkDeepSeek V3.1 TerminusGLM 4.7Leader
AA Intelligence Index14.822.2GLM 4.7
Epoch Capabilities IndexNot measured143.6Only GLM 4.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V3.1 Terminus and GLM 4.7
BenchmarkDeepSeek V3.1 TerminusGLM 4.7Leader
AA Agentic Index6.8Not measuredOnly DeepSeek V3.1 Terminus measured
Design Arena: agents building full-stack appsNot measured1050Only GLM 4.7 measured
Design Arena: agents building mobile appsNot measured1105Only GLM 4.7 measured
Design Arena: agents building Android appsNot measured1112Only GLM 4.7 measured
Design Arena: agents building Godot gamesNot measured1058Only GLM 4.7 measured
τ²-Bench Telecom37.1%95.9%GLM 4.7
Vending-Bench 2Not measured$2,377Only GLM 4.7 measured
BrowseComp-PlusNot measured64.7%Only GLM 4.7 measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for DeepSeek V3.1 Terminus and GLM 4.7
BenchmarkDeepSeek V3.1 TerminusGLM 4.7Leader
GPQA DiamondNot measured80.0%Only GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)79.2%85.9%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)16.4%27.4%GLM 4.7
GPQA Diamond (Epoch AI run)Not measured83.3%Only GLM 4.7 measured
SimpleBenchNot measured47.7%Only GLM 4.7 measured
Chess puzzlesNot measured21.0%Only GLM 4.7 measured
LMArena Text: hard prompts14261443GLM 4.7
LMArena Text: expertNot measured1424Only GLM 4.7 measured
LMArena Text: hard prompts in English14321452GLM 4.7
Kagi LLM Benchmark57.4%Not measuredOnly DeepSeek V3.1 Terminus measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for DeepSeek V3.1 Terminus and GLM 4.7
BenchmarkDeepSeek V3.1 TerminusGLM 4.7Leader
AIMENot measured93.3%Only GLM 4.7 measured
MGSM (Vals AI)Not measured88.2%Only GLM 4.7 measured
AA Math Index89.795.0GLM 4.7
AIME 2025 (Artificial Analysis run)89.7%95.0%GLM 4.7
OTIS Mock AIMENot measured83.3%Only GLM 4.7 measured
ProofBenchNot measured6.0%Only GLM 4.7 measured
LMArena Text: math14021424GLM 4.7
LMArena Text: mathematical workNot measured1411Only GLM 4.7 measured
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index14
Knowledge benchmarks for DeepSeek V3.1 Terminus and GLM 4.7
BenchmarkDeepSeek V3.1 TerminusGLM 4.7Leader
MMLU ProNot measured82.7%Only GLM 4.7 measured
LegalBench (Vals AI)Not measured83.4%Only GLM 4.7 measured
CaseLaw (Vals AI)Not measured54.9%Only GLM 4.7 measured
CorpFin (Vals AI)Not measured46.4%Only GLM 4.7 measured
TaxEval (Vals AI)Not measured68.8%Only GLM 4.7 measured
MedQA (Vals AI)Not measured93.7%Only GLM 4.7 measured
MedCode (Vals AI)Not measured32.8%Only GLM 4.7 measured
MMLU Pro (Artificial Analysis run)85.1%85.6%GLM 4.7
SimpleQA VerifiedNot measured32.2%Only GLM 4.7 measured
LMArena Text: science14511457GLM 4.7
LMArena Text: medicine and healthcare14681469GLM 4.7
LMArena Text: legal and government14571444DeepSeek V3.1 Terminus
LMArena Text: business and finance14141429GLM 4.7
Neither model has a result on the other 5 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 4.7
BenchmarkDeepSeek V3.1 TerminusGLM 4.7Leader
Design Arena: websites11871226GLM 4.7
Design Arena: UI components11821199GLM 4.7
Design Arena: game dev11311189GLM 4.7
Design Arena: data viz11671195GLM 4.7
Design Arena: 3D11411191GLM 4.7
Design Arena: all code categories11751217GLM 4.7
Design Arena: SVG10651143GLM 4.7
Design Arena: ASCII artNot measured1165Only GLM 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index211
General benchmarks for DeepSeek V3.1 Terminus and GLM 4.7
BenchmarkDeepSeek V3.1 TerminusGLM 4.7Leader
MedScribe (Vals AI)Not measured68.6%Only GLM 4.7 measured
IFBench57.0%67.9%GLM 4.7
Long context reasoning (AA-LCR)69.3%71.0%GLM 4.7
DTBench81.3%Not measuredOnly DeepSeek V3.1 Terminus measured
LMCA28.6%Not measuredOnly DeepSeek V3.1 Terminus measured
CL-benchNot measured15.9%Only GLM 4.7 measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena Text14191435GLM 4.7
LMArena Text: instruction following14041412GLM 4.7
LMArena Text: creative writing14031401DeepSeek V3.1 Terminus
LMArena Text: English14291453GLM 4.7
LMArena Text: non-English14081417GLM 4.7
LMArena Text: ChineseNot measured1495Only GLM 4.7 measured
LMArena Text: GermanNot measured1426Only GLM 4.7 measured
LMArena Text: JapaneseNot measured1439Only GLM 4.7 measured
LMArena Text: KoreanNot measured1399Only GLM 4.7 measured
LMArena Text: FrenchNot measured1432Only GLM 4.7 measured
LMArena Text: SpanishNot measured1434Only GLM 4.7 measured
LMArena Text: Russian14351423DeepSeek V3.1 Terminus
LMArena Text: Polish14271435GLM 4.7
LMArena Text: multi-turn14111446GLM 4.7
LMArena Text: longer queries14221432GLM 4.7
LMArena Text: writing and language14091411GLM 4.7
LMArena Text: entertainment and media13911400GLM 4.7
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 V3.1 Terminus and GLM 4.7
BenchmarkDeepSeek V3.1 TerminusGLM 4.7Leader
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 4.7$0.0017Fits 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 4.7$0.037Fits 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 4.7$0.142Exceeds the context window

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 4.7
SpecificationDeepSeek V3.1 TerminusZ.ai GLM 4.7
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedSeptember 22, 2025December 22, 2025
Context window164K205K
Input $/Mtok$0.3063$0.60
Output $/Mtok$1.0142$2.20
Coding indexNot benchmarked55.1
Independent measurements behind it02
Cost per solved taskNot computable$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.1 Terminus better than GLM 4.7 for coding?

Only GLM 4.7 has a coding score, 55.1 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 2.2x cheaper ($1.0142 vs $2.20 per million) and work measured by ALE-Bench or LMArena Text: legal and government, where it leads.

Which is cheaper, DeepSeek V3.1 Terminus or GLM 4.7?

DeepSeek V3.1 Terminus costs $0.3063 per million input tokens and $1.0142 per million output tokens; GLM 4.7 costs $0.60 and $2.20. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, DeepSeek V3.1 Terminus or GLM 4.7?

GLM 4.7, with 205K tokens against 164K.

How many benchmarks is the DeepSeek V3.1 Terminus vs GLM 4.7 comparison based on?

40 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 4.7's on 2. Where only one model has a result, the page says so instead of naming a winner.

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