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DeepSeek V4 Flash 0423 vs GLM 5.3

Updated October 21, 2026. 47 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V4 Flash 0423 Replace GLM 5.3
DeepSeek V4 Flash 0423DeepSeek
Coding
54.6
Overall
24.4
Output $/M
$0.182
0
GLM 5.3Z.ai
Coding
73.1
Overall
44.8
Output $/M
$1.40
47
vs boards won of 47 shared

Decision reading

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 54.6 for DeepSeek V4 Flash 0423.

Pick DeepSeek V4 Flash 0423 when output tokens cost 7.7x less.

Model ADeepSeek V4 Flash 0423DeepSeek54.6/1001 independent benchmarkNo cost per solved taskModel BGLM 5.3Z.ai73.1/1002 independent benchmarks$0.36 per solved task
Shared results
47
DeepSeek V4 Flash 0423 only
1
GLM 5.3 only
53
Like-for-like categories
8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 54.6 from 1 benchmark, GLM 5.3 73.1 from 2 benchmarks.
Widest category
Agentic work, 26.8 points apart on average over 1 shared benchmark.
Widest single gap
WeirdML: DeepSeek V4 Flash 0423 45.6% against GLM 5.3 75.4%, 29.8 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.182 against GLM 5.3 $1.40 per million output tokens, 7.7x apart.
Context window
Both take 1.05M tokens.

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 5.3Ties
Coding08
Intelligence02
Agentic work01
Reasoning03
Math02
Knowledge04
Web design08
General019
All shared boards047
CodingLike-for-likeFour of these boards feed the Vibe Coding Index08
Coding benchmarks for DeepSeek V4 Flash 0423 and GLM 5.3
BenchmarkDeepSeek V4 Flash 0423GLM 5.3Leader
LiveBench codingNot measured82.8%Only GLM 5.3 measured
LMArena WebDev15821621GLM 5.3
AA Coding Index52.074.8GLM 5.3
LiveCodeBench (Vals AI)Not measured80.5%Only GLM 5.3 measured
SWE-bench Verified (Vals AI)Not measured95.4%Only GLM 5.3 measured
Terminal-Bench 4 (Vals AI)Not measured38.9%Only GLM 5.3 measured
Vibe Code Bench (Vals AI)Not measured78.1%Only GLM 5.3 measured
Code migration (Vals AI)Not measured44.2%Only GLM 5.3 measured
ProgramBench (Vals AI)Not measured1.5%Only GLM 5.3 measured
IOI (Vals AI)Not measured68.4%Only GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)Not measured71.5%Only GLM 5.3 measured
Vibe Code Bench 1-100 (Vals AI)Not measured20.0%Only GLM 5.3 measured
SkillsBench (Vals AI)Not measured47.5%Only GLM 5.3 measured
CyberBench (Vals AI)Not measured72.1%Only GLM 5.3 measured
SciCodeNot measured59.0%Only GLM 5.3 measured
FrontierCodeNot measured40.1%Only GLM 5.3 measured
FrontierSWENot measured30.2%Only GLM 5.3 measured
WeirdML45.6%75.4%GLM 5.3
ALE-Bench678.21317.4GLM 5.3
LMArena Text: coding14571497GLM 5.3
LMArena Text: software and IT14531493GLM 5.3
LMArena WebDev: React15851641GLM 5.3
LMArena WebDev: HTML15581595GLM 5.3
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for DeepSeek V4 Flash 0423 and GLM 5.3
BenchmarkDeepSeek V4 Flash 0423GLM 5.3Leader
AA Intelligence Index24.444.8GLM 5.3
Epoch Capabilities Index146.1156.0GLM 5.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V4 Flash 0423 and GLM 5.3
BenchmarkDeepSeek V4 Flash 0423GLM 5.3Leader
AA Agentic Index26.353.1GLM 5.3
Design Arena: agents building mobile appsNot measured1194Only GLM 5.3 measured
Design Arena: HTML slidesNot measured1189Only GLM 5.3 measured
Design Arena: PowerPoint slidesNot measured1255Only GLM 5.3 measured
LiveBench researchNot measured73.2%Only GLM 5.3 measured
LiveBench browserNot measured0.0%Only GLM 5.3 measured
LiveBench long agenticNot measured35.9%Only GLM 5.3 measured
SREBench (Vals AI)Not measured9.2%Only GLM 5.3 measured
Finance Agent (Vals AI)Not measured55.8%Only GLM 5.3 measured
Tax Agent Bench (Vals AI)Not measured39.7%Only GLM 5.3 measured
Legal Research Bench (Vals AI)Not measured49.0%Only GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)Not measured14.6%Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)Not measured56.3%Only GLM 5.3 measured
Terminal-Bench-Science (Vals AI)Not measured5.7%Only GLM 5.3 measured
APEX-AgentsNot measured56.6%Only GLM 5.3 measured
Vending-Bench 2Not measured$8,164Only GLM 5.3 measured
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for DeepSeek V4 Flash 0423 and GLM 5.3
BenchmarkDeepSeek V4 Flash 0423GLM 5.3Leader
GPQA DiamondNot measured88.1%Only GLM 5.3 measured
LiveBench reasoningNot measured85.8%Only GLM 5.3 measured
MysteryMechanism (Vals AI)Not measured23.0%Only GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)Not measured91.7%Only GLM 5.3 measured
Humanity's Last Exam (Artificial Analysis run)Not measured42.3%Only GLM 5.3 measured
GPQA Diamond (Epoch AI run)Not measured90.9%Only GLM 5.3 measured
SimpleBench46.3%Not measuredOnly DeepSeek V4 Flash 0423 measured
Chess puzzlesNot measured37.0%Only GLM 5.3 measured
Mystery game puzzlesNot measured33.0%Only GLM 5.3 measured
LMArena Text: hard prompts14441490GLM 5.3
LMArena Text: expert14411514GLM 5.3
LMArena Text: hard prompts in English14491496GLM 5.3
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for DeepSeek V4 Flash 0423 and GLM 5.3
BenchmarkDeepSeek V4 Flash 0423GLM 5.3Leader
OTIS Mock AIMENot measured91.1%Only GLM 5.3 measured
FrontierMath, tiers 1 to 3Not measured68.8%Only GLM 5.3 measured
FrontierMath, tier 4Not measured29.3%Only GLM 5.3 measured
ProofBenchNot measured49.0%Only GLM 5.3 measured
LMArena Text: math14261495GLM 5.3
LMArena Text: mathematical work14321497GLM 5.3
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 5.3
BenchmarkDeepSeek V4 Flash 0423GLM 5.3Leader
MMLU ProNot measured86.8%Only GLM 5.3 measured
LegalBench (Vals AI)Not measured84.8%Only GLM 5.3 measured
TaxEval (Vals AI)Not measured72.4%Only GLM 5.3 measured
MedCode (Vals AI)Not measured42.9%Only GLM 5.3 measured
SimpleQA VerifiedNot measured41.0%Only GLM 5.3 measured
LMArena Text: science14461498GLM 5.3
LMArena Text: medicine and healthcare14481481GLM 5.3
LMArena Text: legal and government14391473GLM 5.3
LMArena Text: business and finance14251459GLM 5.3
Neither model has a result on the other 8 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 5.3
BenchmarkDeepSeek V4 Flash 0423GLM 5.3Leader
Design Arena: websites12081295GLM 5.3
Design Arena: UI components11641314GLM 5.3
Design Arena: game dev11971311GLM 5.3
Design Arena: data viz11271260GLM 5.3
Design Arena: 3D11891332GLM 5.3
Design Arena: all code categories12051304GLM 5.3
Design Arena: SVG11551292GLM 5.3
Design Arena: ASCII art11091213GLM 5.3
LiveBench designNot measured77.3%Only GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for DeepSeek V4 Flash 0423 and GLM 5.3
BenchmarkDeepSeek V4 Flash 0423GLM 5.3Leader
Vals IndexNot measured59.7%Only GLM 5.3 measured
LiveBench data analysisNot measured70.2%Only GLM 5.3 measured
Public Benefits Bench (Vals AI)Not measured68.5%Only GLM 5.3 measured
MedScribe (Vals AI)Not measured88.8%Only GLM 5.3 measured
Long context reasoning (AA-LCR)Not measured79.7%Only GLM 5.3 measured
DTBench86.4%87.7%GLM 5.3
LMCA35.9%55.5%GLM 5.3
LMArena Text14321474GLM 5.3
LMArena Text: instruction following14211479GLM 5.3
LMArena Text: creative writing14031458GLM 5.3
LMArena Text: English14411482GLM 5.3
LMArena Text: non-English14191459GLM 5.3
LMArena Text: Chinese14631532GLM 5.3
LMArena Text: German14191488GLM 5.3
LMArena Text: Japanese13891460GLM 5.3
LMArena Text: Korean13831460GLM 5.3
LMArena Text: French14391503GLM 5.3
LMArena Text: Spanish14361462GLM 5.3
LMArena Text: Russian14271469GLM 5.3
LMArena Text: Polish14361466GLM 5.3
LMArena Text: multi-turn14491472GLM 5.3
LMArena Text: longer queries14341483GLM 5.3
LMArena Text: writing and language14101469GLM 5.3
LMArena Text: entertainment and media14001447GLM 5.3
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 5.3
BenchmarkDeepSeek V4 Flash 0423GLM 5.3Leader
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$0.0002Fits in one request
GLM 5.3$0.0008Fits in one request

DeepSeek V4 Flash 0423 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Flash 0423$0.0051Fits in one request
GLM 5.3$0.0081Fits in one request

DeepSeek V4 Flash 0423 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0423$0.020Fits in one request
GLM 5.3$0.030Fits in one request

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 5.3
SpecificationDeepSeek V4 Flash 0423Z.ai GLM 5.3
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedApril 24, 2026August 18, 2026
Context window1.05M1.05M
Input $/Mtok$0.091$0.079
Output $/Mtok$0.182$1.40
Coding index54.673.1
Independent measurements behind it12
Cost per solved taskNot computable$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Flash 0423 better than GLM 5.3 for coding?

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 54.6 for DeepSeek V4 Flash 0423. Pick DeepSeek V4 Flash 0423 when output tokens cost 7.7x less.

Which is cheaper, DeepSeek V4 Flash 0423 or GLM 5.3?

DeepSeek V4 Flash 0423 costs $0.091 per million input tokens and $0.182 per million output tokens; GLM 5.3 costs $0.079 and $1.40. Cost per solved task is known for GLM 5.3 only.

Which has the bigger context window, DeepSeek V4 Flash 0423 or GLM 5.3?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4 Flash 0423 vs GLM 5.3 comparison based on?

47 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark and GLM 5.3's on 2. Where only one model has a result, the page says so instead of naming a winner.

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