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DeepSeek V4 Flash 0423 vs GLM 5.1

GLM 5.1 leads DeepSeek V4 Flash 0423 on the Artificial Analysis Intelligence Index, 26.1 to 24.4, and DeepSeek V4 Flash 0423 costs 15.7x less per output token ($0.28 vs $4.40 per million). Updated October 22, 2026; 46 benchmarks have a result for both.

Replace DeepSeek V4 Flash 0423 Replace GLM 5.1
DeepSeek V4 Flash 0423DeepSeek
Coding
No data
Overall
No data
Output $/M
$0.28
4
GLM 5.1Z.ai
Coding
49.2
Overall
57.5
Output $/M
$4.40
42
vs boards won of 46 shared

DeepSeek V4 Flash 0423 or GLM 5.1: which to pick

Only GLM 5.1 has a coding score, 49.2 on the Vibe Coding Index; DeepSeek V4 Flash 0423 has none, so the two cannot be ranked on coding.

Pick DeepSeek V4 Flash 0423 for output tokens 15.7x cheaper ($0.28 vs $4.40 per million) and a larger context window (1.05M against 205K tokens).

Model ADeepSeek V4 Flash 0423DeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BGLM 5.1Z.ai49.2/1003 independent benchmarks$0.60 per solved task
Shared results
46
DeepSeek V4 Flash 0423 only
3
GLM 5.1 only
43
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 Flash 0423 not benchmarked, GLM 5.1 49.2 from 3 benchmarks.
Widest category
Web design, 12.9 points apart on average over 8 shared benchmarks.
Widest single gap
Design Arena: data viz: DeepSeek V4 Flash 0423 1127 against GLM 5.1 1366, 29.9 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.28 against GLM 5.1 $4.40 per million output tokens, 15.7x apart.
Context window
DeepSeek V4 Flash 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 Flash 0423BarsGLM 5.1Ties
Coding35
Intelligence02
Agentic work10
Reasoning04
Math02
Knowledge04
Web design08
General017
All shared boards442
CodingLike-for-likeSix of these boards feed the Vibe Coding Index35
Coding benchmarks for DeepSeek V4 Flash 0423 and GLM 5.1
BenchmarkDeepSeek V4 Flash 0423GLM 5.1Leader
LMArena WebDev15821508DeepSeek V4 Flash 0423
AA Coding Index52.055.8GLM 5.1
LiveCodeBench (Vals AI)Not measured81.4%Only GLM 5.1 measured
SWE-bench Verified (Vals AI)Not measured76.4%Only GLM 5.1 measured
Vibe Code Bench (Vals AI)Not measured31.5%Only GLM 5.1 measured
Code migration (Vals AI)Not measured25.8%Only GLM 5.1 measured
ProgramBench (Vals AI)Not measured0.0%Only GLM 5.1 measured
Terminal-Bench 2.1 (Vals AI)Not measured56.9%Only GLM 5.1 measured
SciCodeNot measured44.8%Only GLM 5.1 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured43.2%Only GLM 5.1 measured
WeirdML45.6%57.1%GLM 5.1
ALE-Bench678.2887.1GLM 5.1
LMArena Text: coding14571485GLM 5.1
LMArena Text: software and IT14531484GLM 5.1
LMArena WebDev: React15851505DeepSeek V4 Flash 0423
LMArena WebDev: HTML15581508DeepSeek V4 Flash 0423
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for DeepSeek V4 Flash 0423 and GLM 5.1
BenchmarkDeepSeek V4 Flash 0423GLM 5.1Leader
AA Intelligence Index24.426.1GLM 5.1
Epoch Capabilities Index146.1149.8GLM 5.1
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V4 Flash 0423 and GLM 5.1
BenchmarkDeepSeek V4 Flash 0423GLM 5.1Leader
AA Agentic Index26.323.9DeepSeek V4 Flash 0423
Design Arena: agents building web appsNot measured1175Only GLM 5.1 measured
Design Arena: agents building full-stack appsNot measured1167Only GLM 5.1 measured
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 gamesNot measured1053Only GLM 5.1 measured
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)Not measured44.8%Only GLM 5.1 measured
Legal Research Bench (Vals AI)Not measured27.9%Only GLM 5.1 measured
τ²-Bench TelecomNot measured97.7%Only GLM 5.1 measured
APEX-AgentsNot measured40.9%Only GLM 5.1 measured
Vending-Bench 2Not measured$5,634Only GLM 5.1 measured
Neither model has a result on the other 26 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for DeepSeek V4 Flash 0423 and GLM 5.1
BenchmarkDeepSeek V4 Flash 0423GLM 5.1Leader
GPQA DiamondNot measured84.5%Only GLM 5.1 measured
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)Not measured89.9%Only GLM 5.1 measured
SimpleBench46.3%55.1%GLM 5.1
Chess puzzlesNot measured20.0%Only GLM 5.1 measured
LMArena Text: hard prompts14441473GLM 5.1
LMArena Text: expert14411478GLM 5.1
LMArena Text: hard prompts in English14491482GLM 5.1
Kagi LLM Benchmark52.2%Not measuredOnly DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for DeepSeek V4 Flash 0423 and GLM 5.1
BenchmarkDeepSeek V4 Flash 0423GLM 5.1Leader
AIMENot measured91.9%Only GLM 5.1 measured
OTIS Mock AIMENot measured93.3%Only GLM 5.1 measured
FrontierMath, tiers 1 to 3Not measured36.8%Only GLM 5.1 measured
ProofBenchNot measured22.2%Only GLM 5.1 measured
LMArena Text: math14261473GLM 5.1
LMArena Text: mathematical work14321470GLM 5.1
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for DeepSeek V4 Flash 0423 and GLM 5.1
BenchmarkDeepSeek V4 Flash 0423GLM 5.1Leader
MMLU ProNot measured86.9%Only GLM 5.1 measured
LegalBench (Vals AI)Not measured84.4%Only GLM 5.1 measured
CaseLaw (Vals AI)Not measured51.5%Only GLM 5.1 measured
CorpFin (Vals AI)Not measured64.5%Only GLM 5.1 measured
TaxEval (Vals AI)Not measured71.2%Only GLM 5.1 measured
MedCode (Vals AI)Not measured41.6%Only GLM 5.1 measured
SimpleQA VerifiedNot measured34.0%Only GLM 5.1 measured
LMArena Text: science14461478GLM 5.1
LMArena Text: medicine and healthcare14481474GLM 5.1
LMArena Text: legal and government14391465GLM 5.1
LMArena Text: business and finance14251452GLM 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 index08
Web design benchmarks for DeepSeek V4 Flash 0423 and GLM 5.1
BenchmarkDeepSeek V4 Flash 0423GLM 5.1Leader
Design Arena: websites12071290GLM 5.1
Design Arena: UI components11621292GLM 5.1
Design Arena: game dev11961265GLM 5.1
Design Arena: data viz11271366GLM 5.1
Design Arena: 3D11881336GLM 5.1
Design Arena: all code categories12051268GLM 5.1
Design Arena: SVG11551218GLM 5.1
Design Arena: ASCII art11091141GLM 5.1
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
General benchmarks for DeepSeek V4 Flash 0423 and GLM 5.1
BenchmarkDeepSeek V4 Flash 0423GLM 5.1Leader
Public Benefits Bench (Vals AI)Not measured61.8%Only GLM 5.1 measured
MedScribe (Vals AI)Not measured72.3%Only GLM 5.1 measured
IFBenchNot measured76.3%Only GLM 5.1 measured
Long context reasoning (AA-LCR)Not measured73.7%Only GLM 5.1 measured
DTBench86.4%Not measuredOnly DeepSeek V4 Flash 0423 measured
LMCA35.9%Not measuredOnly DeepSeek V4 Flash 0423 measured
ExploitBenchNot measured18.1%Only GLM 5.1 measured
LMArena Text14321463GLM 5.1
LMArena Text: instruction following14211453GLM 5.1
LMArena Text: creative writing14031453GLM 5.1
LMArena Text: English14411476GLM 5.1
LMArena Text: non-English14191448GLM 5.1
LMArena Text: Chinese14631515GLM 5.1
LMArena Text: German14191467GLM 5.1
LMArena Text: Japanese13891435GLM 5.1
LMArena Text: Korean13831424GLM 5.1
LMArena Text: French14391470GLM 5.1
LMArena Text: Spanish14361468GLM 5.1
LMArena Text: Russian14271454GLM 5.1
LMArena Text: Polish14361449GLM 5.1
LMArena Text: multi-turn14491473GLM 5.1
LMArena Text: longer queries14341467GLM 5.1
LMArena Text: writing and language14101454GLM 5.1
LMArena Text: entertainment and media14001442GLM 5.1
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 V4 Flash 0423 and GLM 5.1
BenchmarkDeepSeek V4 Flash 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 Flash 0423$0.0003Fits in one request
GLM 5.1$0.0036Fits in one request

DeepSeek V4 Flash 0423 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Flash 0423$0.0078Fits in one request
GLM 5.1$0.083Fits in one request

DeepSeek V4 Flash 0423 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0423$0.031Fits in one request
GLM 5.1$0.324Exceeds the context window

DeepSeek V4 Flash 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 Flash 0423 and GLM 5.1
SpecificationDeepSeek V4 Flash 0423Z.ai GLM 5.1
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedApril 24, 2026April 7, 2026
Context window1.05M205K
Input $/Mtok$0.14$1.40
Output $/Mtok$0.28$4.40
Coding indexNot benchmarked49.2
Independent measurements behind it13
Cost per solved taskNot computable$0.60
SourceOpenRouter listingOpenRouter listing

Questions

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

Only GLM 5.1 has a coding score, 49.2 on the Vibe Coding Index; DeepSeek V4 Flash 0423 has none, so the two cannot be ranked on coding. Pick DeepSeek V4 Flash 0423 for output tokens 15.7x cheaper ($0.28 vs $4.40 per million) and a larger context window (1.05M against 205K tokens).

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

DeepSeek V4 Flash 0423 costs $0.14 per million input tokens and $0.28 per million output tokens; GLM 5.1 costs $1.40 and $4.40. Cost per solved task is known for GLM 5.1 only.

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

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

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

46 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark 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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