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DeepSeek V4 Pro 0423 vs GLM 5.1

GLM 5.1 leads DeepSeek V4 Pro 0423 on coding, 49.2 to 38.7 on the Vibe Coding Index, and DeepSeek V4 Pro 0423 costs 1.5x less per output token ($2.976 vs $4.40 per million). Updated October 22, 2026; 72 benchmarks have a result for both.

Replace DeepSeek V4 Pro 0423 Replace GLM 5.1
DeepSeek V4 Pro 0423DeepSeek
Coding
38.7
Overall
50.0
Output $/M
$2.976
23
GLM 5.1Z.ai
Coding
49.2
Overall
57.5
Output $/M
$4.40
48
vs boards won of 72 shared, 1 tied

DeepSeek V4 Pro 0423 or GLM 5.1: which to pick

Pick GLM 5.1 for coding: it scores 49.2 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423, though on 3 benchmarks against 5.

Pick DeepSeek V4 Pro 0423 for output tokens 1.5x cheaper ($2.976 vs $4.40 per million) and a lower cost per solved task ($0.57 against $0.60).

Model ADeepSeek V4 Pro 0423DeepSeek38.7/1005 independent benchmarks$0.57 per solved taskModel BGLM 5.1Z.ai49.2/1003 independent benchmarks$0.60 per solved task
Shared results
72
DeepSeek V4 Pro 0423 only
12
GLM 5.1 only
17
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 V4 Pro 0423 38.7 from 5 benchmarks, GLM 5.1 49.2 from 3 benchmarks.
Widest category
Agentic work, 10.4 points apart on average over 7 shared benchmarks.
Widest single gap
Design Arena: agents building full-stack apps: DeepSeek V4 Pro 0423 948 against GLM 5.1 1167, 27.4 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Pro 0423 $2.976 against GLM 5.1 $4.40 per million output tokens, 1.5x apart.
Context window
DeepSeek V4 Pro 0423 takes 1.05M tokens, GLM 5.1 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 V4 Pro 0423BarsGLM 5.1Ties
Coding671 tie
Intelligence11
Agentic work25
Reasoning24
Math23
Knowledge47
Web design17
General514
All shared boards23481 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index67
Coding benchmarks for DeepSeek V4 Pro 0423 and GLM 5.1
BenchmarkDeepSeek V4 Pro 0423GLM 5.1Leader
LMArena WebDev14471508GLM 5.1
AA Coding Index59.455.8DeepSeek V4 Pro 0423
LiveCodeBench (Vals AI)87.5%81.4%DeepSeek V4 Pro 0423
SWE-bench Verified (Vals AI)77.4%76.4%DeepSeek V4 Pro 0423
Terminal-Bench 4 (Vals AI)11.1%Not measuredOnly DeepSeek V4 Pro 0423 measured
Vibe Code Bench (Vals AI)49.9%31.5%DeepSeek V4 Pro 0423
Code migration (Vals AI)26.2%25.8%DeepSeek V4 Pro 0423
ProgramBench (Vals AI)0.0%0.0%Tie
Terminal-Bench 2.1 (Vals AI)50.2%56.9%GLM 5.1
SkillsBench (Vals AI)51.3%Not measuredOnly DeepSeek V4 Pro 0423 measured
SciCodeNot measured44.8%Only GLM 5.1 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured43.2%Only GLM 5.1 measured
FrontierCode17.6%Not measuredOnly DeepSeek V4 Pro 0423 measured
WeirdML48.9%57.1%GLM 5.1
ALE-Bench1006.1887.1DeepSeek V4 Pro 0423
LMArena Text: coding14701485GLM 5.1
LMArena Text: software and IT14691484GLM 5.1
LMArena WebDev: React14411505GLM 5.1
LMArena WebDev: HTML14351508GLM 5.1
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
Intelligence benchmarks for DeepSeek V4 Pro 0423 and GLM 5.1
BenchmarkDeepSeek V4 Pro 0423GLM 5.1Leader
AA Intelligence Index30.426.1DeepSeek V4 Pro 0423
Epoch Capabilities Index149.1149.8GLM 5.1
Agentic workLike-for-likePublic boards, shown as published, kept out of the index25
Agentic work benchmarks for DeepSeek V4 Pro 0423 and GLM 5.1
BenchmarkDeepSeek V4 Pro 0423GLM 5.1Leader
AA Agentic Index26.323.9DeepSeek V4 Pro 0423
Design Arena: agents building web apps10001175GLM 5.1
Design Arena: agents building full-stack apps9481167GLM 5.1
Design Arena: agents building mobile appsNot measured1162Only GLM 5.1 measured
Design Arena: agents building Android appsNot measured1182Only GLM 5.1 measured
Design Arena: agents building Godot games10591053DeepSeek V4 Pro 0423
Design Arena: agents building gamesNot measured1164Only GLM 5.1 measured
Design Arena: HTML slidesNot measured1120Only GLM 5.1 measured
Design Arena: PowerPoint slidesNot measured1163Only GLM 5.1 measured
Design Arena: spreadsheetsNot measured1315Only GLM 5.1 measured
Finance Agent (Vals AI)44.1%44.8%GLM 5.1
Tax Agent Bench (Vals AI)25.2%Not measuredOnly DeepSeek V4 Pro 0423 measured
Legal Research Bench (Vals AI)23.1%27.9%GLM 5.1
Excel Modeling Benchmark (Vals AI)51.6%Not measuredOnly DeepSeek V4 Pro 0423 measured
τ²-Bench TelecomNot measured97.7%Only GLM 5.1 measured
APEX-AgentsNot measured40.9%Only GLM 5.1 measured
Vending-Bench 2$3,285$5,634GLM 5.1
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index24
Reasoning benchmarks for DeepSeek V4 Pro 0423 and GLM 5.1
BenchmarkDeepSeek V4 Pro 0423GLM 5.1Leader
GPQA Diamond89.4%84.5%DeepSeek V4 Pro 0423
GPQA Diamond (Artificial Analysis run)Not measured86.8%Only GLM 5.1 measured
Humanity's Last Exam (Artificial Analysis run)Not measured30.1%Only GLM 5.1 measured
GPQA Diamond (Epoch AI run)90.9%89.9%DeepSeek V4 Pro 0423
SimpleBenchNot measured55.1%Only GLM 5.1 measured
Chess puzzles19.0%20.0%GLM 5.1
Mystery game puzzles17.0%Not measuredOnly DeepSeek V4 Pro 0423 measured
LMArena Text: hard prompts14611473GLM 5.1
LMArena Text: expert14631478GLM 5.1
LMArena Text: hard prompts in English14661482GLM 5.1
Kagi LLM Benchmark53.5%Not measuredOnly DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index23
Math benchmarks for DeepSeek V4 Pro 0423 and GLM 5.1
BenchmarkDeepSeek V4 Pro 0423GLM 5.1Leader
AIMENot measured91.9%Only GLM 5.1 measured
OTIS Mock AIME96.7%93.3%DeepSeek V4 Pro 0423
FrontierMath, tiers 1 to 345.3%36.8%DeepSeek V4 Pro 0423
FrontierMath, tier 42.4%Not measuredOnly DeepSeek V4 Pro 0423 measured
ProofBench16.0%22.2%GLM 5.1
LMArena Text: math14431473GLM 5.1
LMArena Text: mathematical work14551470GLM 5.1
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index47
Knowledge benchmarks for DeepSeek V4 Pro 0423 and GLM 5.1
BenchmarkDeepSeek V4 Pro 0423GLM 5.1Leader
MMLU Pro87.3%86.9%DeepSeek V4 Pro 0423
LegalBench (Vals AI)80.3%84.4%GLM 5.1
CaseLaw (Vals AI)59.4%51.5%DeepSeek V4 Pro 0423
CorpFin (Vals AI)61.4%64.5%GLM 5.1
TaxEval (Vals AI)72.1%71.2%DeepSeek V4 Pro 0423
MedCode (Vals AI)40.5%41.6%GLM 5.1
SimpleQA Verified47.0%34.0%DeepSeek V4 Pro 0423
LMArena Text: science14701478GLM 5.1
LMArena Text: medicine and healthcare14661474GLM 5.1
LMArena Text: legal and government14641465GLM 5.1
LMArena Text: business and finance14431452GLM 5.1
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index17
Web design benchmarks for DeepSeek V4 Pro 0423 and GLM 5.1
BenchmarkDeepSeek V4 Pro 0423GLM 5.1Leader
Design Arena: websites12341290GLM 5.1
Design Arena: UI components12191292GLM 5.1
Design Arena: game dev12281265GLM 5.1
Design Arena: data viz12031366GLM 5.1
Design Arena: 3D12531336GLM 5.1
Design Arena: all code categories12401268GLM 5.1
Design Arena: SVG11391218GLM 5.1
Design Arena: ASCII art11491141DeepSeek V4 Pro 0423
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index514
General benchmarks for DeepSeek V4 Pro 0423 and GLM 5.1
BenchmarkDeepSeek V4 Pro 0423GLM 5.1Leader
Public Benefits Bench (Vals AI)62.9%61.8%DeepSeek V4 Pro 0423
MedScribe (Vals AI)75.1%72.3%DeepSeek V4 Pro 0423
IFBenchNot measured76.3%Only GLM 5.1 measured
Long context reasoning (AA-LCR)Not measured73.7%Only GLM 5.1 measured
DTBench90.7%Not measuredOnly DeepSeek V4 Pro 0423 measured
LMCA41.2%Not measuredOnly DeepSeek V4 Pro 0423 measured
Surface Evolver Bench40.0%Not measuredOnly DeepSeek V4 Pro 0423 measured
ExploitBenchNot measured18.1%Only GLM 5.1 measured
CL-bench Life13.5%Not measuredOnly DeepSeek V4 Pro 0423 measured
LMArena Text14511463GLM 5.1
LMArena Text: instruction following14451453GLM 5.1
LMArena Text: creative writing14461453GLM 5.1
LMArena Text: English14591476GLM 5.1
LMArena Text: non-English14391448GLM 5.1
LMArena Text: Chinese14811515GLM 5.1
LMArena Text: German14581467GLM 5.1
LMArena Text: Japanese14461435DeepSeek V4 Pro 0423
LMArena Text: Korean14091424GLM 5.1
LMArena Text: French14731470DeepSeek V4 Pro 0423
LMArena Text: Spanish14571468GLM 5.1
LMArena Text: Russian14531454GLM 5.1
LMArena Text: Polish14611449DeepSeek V4 Pro 0423
LMArena Text: multi-turn14681473GLM 5.1
LMArena Text: longer queries14581467GLM 5.1
LMArena Text: writing and language14471454GLM 5.1
LMArena Text: entertainment and media14311442GLM 5.1
Neither model has a result on the other 20 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V4 Pro 0423 and GLM 5.1
BenchmarkDeepSeek V4 Pro 0423GLM 5.1Leader
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 V4 Pro 0423$0.0028Fits in one request
GLM 5.1$0.0036Fits in one request

DeepSeek V4 Pro 0423 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Pro 0423$0.077Fits in one request
GLM 5.1$0.083Fits in one request

DeepSeek V4 Pro 0423 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Pro 0423$0.301Fits in one request
GLM 5.1$0.324Exceeds the context window

DeepSeek V4 Pro 0423 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 V4 Pro 0423 and GLM 5.1
SpecificationDeepSeek V4 Pro 0423Z.ai GLM 5.1
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedApril 24, 2026April 7, 2026
Context window1.05M205K
Input $/Mtok$1.358$1.40
Output $/Mtok$2.976$4.40
Coding index38.749.2
Independent measurements behind it53
Cost per solved task$0.57$0.60
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Pro 0423 better than GLM 5.1 for coding?

Pick GLM 5.1 for coding: it scores 49.2 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423, though on 3 benchmarks against 5. Pick DeepSeek V4 Pro 0423 for output tokens 1.5x cheaper ($2.976 vs $4.40 per million) and a lower cost per solved task ($0.57 against $0.60).

Which is cheaper, DeepSeek V4 Pro 0423 or GLM 5.1?

DeepSeek V4 Pro 0423 costs $1.358 per million input tokens and $2.976 per million output tokens; GLM 5.1 costs $1.40 and $4.40. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0423 costs $0.57 and GLM 5.1 $0.60.

Which has the bigger context window, DeepSeek V4 Pro 0423 or GLM 5.1?

DeepSeek V4 Pro 0423, with 1.05M tokens against 205K.

How many benchmarks is the DeepSeek V4 Pro 0423 vs GLM 5.1 comparison based on?

72 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0423's coding index rests on 5 independent benchmarks and GLM 5.1's on 3. Where only one model has a result, the page says so instead of naming a winner.

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