DeepSeek V4 Pro 0813 vs GLM 5.3 Flash

Updated October 21, 2026. 72 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V4 Pro 0813 Replace GLM 5.3 Flash

DeepSeek V4 Pro 0813DeepSeek

Coding: 54.6
Overall: 36.0
Output $/M: $1.98

GLM 5.3 FlashZ.ai

Coding: 71.0
Overall: 41.8
Output $/M: $0.50

vs boards won of 72 shared, 1 tied

Decision reading

Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 54.6 for DeepSeek V4 Pro 0813.

Pick DeepSeek V4 Pro 0813 when what matters is LiveCodeBench (Vals AI) or SWE-bench Verified (Vals AI), where it leads.

Model ADeepSeek V4 Pro 0813DeepSeek54.6/1001 independent benchmark$0.10 per solved task Model BGLM 5.3 FlashZ.ai71.0/1002 independent benchmarks$0.02 per solved task

72630

Shared results: 72
DeepSeek V4 Pro 0813 only: 6
GLM 5.3 Flash only: 30
Like-for-like categories: 7 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.

Coding
Vibe Coding Index, four public coding benchmarks
GLM 5.3 Flash
GLM 5.3 Flash scores 71.0 against 54.6.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 36.0.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 50.9 against 41.3.
Same board, both measured
Reasoning
GPQA Diamond
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 92.4% against 86.4%.
Same board, both measured
Knowledge
MMLU Pro
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 87.0% against 86.1%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5.3 Flash has a Design Arena: websites result, 1272.
Directional only
General
Vals Index
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 53.0% against 50.9%.
Same board, both measured
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Flash
DeepSeek V4 Pro 0813 $0.0016, GLM 5.3 Flash $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3 Flash
DeepSeek V4 Pro 0813 $0.10, GLM 5.3 Flash $0.02.
Published attempt cost and pass rate

At a glance

Coding index: DeepSeek V4 Pro 0813 54.6 from 1 benchmark, GLM 5.3 Flash 71.0 from 2 benchmarks.
Widest category: Math, 11.9 points apart on average over 6 shared benchmarks.
Widest single gap: Vibe Code Bench (Vals AI): DeepSeek V4 Pro 0813 82.3% against GLM 5.3 Flash 30.8%, 51.5 points apart on a 0 to 100 scale.
Output price: DeepSeek V4 Pro 0813 $1.98 against GLM 5.3 Flash $0.50 per million output tokens, 4.0x 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
Category	DeepSeek V4 Pro 0813	GLM 5.3 Flash	Ties
Coding	8	11	1 tie
Intelligence	1	1
Agentic work	3	6
Reasoning	6	5
Math	4	2
Knowledge	1	6
General	3	14
All shared boards	26	45	1 tie

CodingLike-for-likeFour of these boards feed the Vibe Coding Index811

Coding benchmarks for DeepSeek V4 Pro 0813 and GLM 5.3 Flash
Benchmark	DeepSeek V4 Pro 0813	GLM 5.3 Flash	Leader
LiveBench coding	Not measured	80.6%	Only GLM 5.3 Flash measured
LMArena WebDev	1582	1607	GLM 5.3 Flash
AA Coding Index	68.8	71.5	GLM 5.3 Flash
LiveCodeBench (Vals AI)	87.5%	80.5%	DeepSeek V4 Pro 0813
SWE-bench Verified (Vals AI)	96.4%	92.0%	DeepSeek V4 Pro 0813
Terminal-Bench 4 (Vals AI)	14.1%	37.4%	GLM 5.3 Flash
Vibe Code Bench (Vals AI)	82.3%	30.8%	DeepSeek V4 Pro 0813
Code migration (Vals AI)	41.5%	20.5%	DeepSeek V4 Pro 0813
ProgramBench (Vals AI)	0.0%	0.0%	Tie
IOI (Vals AI)	51.6%	52.5%	GLM 5.3 Flash
Terminal-Bench 2.1 (Vals AI)	54.7%	62.9%	GLM 5.3 Flash
Vibe Code Bench 1-100 (Vals AI)	17.5%	16.0%	DeepSeek V4 Pro 0813
SkillsBench (Vals AI)	53.8%	40.2%	DeepSeek V4 Pro 0813
CyberBench (Vals AI)	72.9%	67.9%	DeepSeek V4 Pro 0813
SciCode	51.0%	51.6%	GLM 5.3 Flash
FrontierCode	28.5%	31.8%	GLM 5.3 Flash
FrontierSWE	Not measured	18.1%	Only GLM 5.3 Flash measured
WeirdML	66.2%	Not measured	Only DeepSeek V4 Pro 0813 measured
ALE-Bench	1403.2	303.6	DeepSeek V4 Pro 0813
LMArena Text: coding	1469	1507	GLM 5.3 Flash
LMArena Text: software and IT	1462	1500	GLM 5.3 Flash
LMArena WebDev: React	1588	1624	GLM 5.3 Flash
LMArena WebDev: HTML	1566	1577	GLM 5.3 Flash
LMArena WebDev: from an image	Not measured	1578	Only GLM 5.3 Flash measured
Neither model has a result on the other 14 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11

Intelligence benchmarks for DeepSeek V4 Pro 0813 and GLM 5.3 Flash
Benchmark	DeepSeek V4 Pro 0813	GLM 5.3 Flash	Leader
AA Intelligence Index	36.0	41.8	GLM 5.3 Flash
Epoch Capabilities Index	155.6	153.1	DeepSeek V4 Pro 0813

Agentic workLike-for-likePublic boards, shown as published, kept out of the index36

Agentic work benchmarks for DeepSeek V4 Pro 0813 and GLM 5.3 Flash
Benchmark	DeepSeek V4 Pro 0813	GLM 5.3 Flash	Leader
AA Agentic Index	41.3	50.9	GLM 5.3 Flash
LiveBench research	Not measured	70.1%	Only GLM 5.3 Flash measured
LiveBench browser	Not measured	84.0%	Only GLM 5.3 Flash measured
LiveBench long agentic	Not measured	28.4%	Only GLM 5.3 Flash measured
SREBench (Vals AI)	1.9%	0.8%	DeepSeek V4 Pro 0813
Finance Agent (Vals AI)	50.4%	57.9%	GLM 5.3 Flash
Tax Agent Bench (Vals AI)	26.8%	31.6%	GLM 5.3 Flash
Legal Research Bench (Vals AI)	40.9%	45.2%	GLM 5.3 Flash
Harvey's Legal Agent Benchmark (Vals AI)	10.8%	10.4%	DeepSeek V4 Pro 0813
Excel Modeling Benchmark (Vals AI)	52.8%	55.9%	GLM 5.3 Flash
Terminal-Bench-Science (Vals AI)	4.3%	2.9%	DeepSeek V4 Pro 0813
APEX-Agents	47.3%	52.8%	GLM 5.3 Flash
Neither model has a result on the other 27 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index65

Reasoning benchmarks for DeepSeek V4 Pro 0813 and GLM 5.3 Flash
Benchmark	DeepSeek V4 Pro 0813	GLM 5.3 Flash	Leader
GPQA Diamond	92.4%	86.4%	DeepSeek V4 Pro 0813
LiveBench reasoning	Not measured	77.6%	Only GLM 5.3 Flash measured
MMMU Pro (Vals AI)	Not measured	86.0%	Only GLM 5.3 Flash measured
GPQA Diamond (Artificial Analysis run)	92.8%	91.2%	DeepSeek V4 Pro 0813
Humanity's Last Exam (Artificial Analysis run)	41.0%	39.9%	DeepSeek V4 Pro 0813
GPQA Diamond (Epoch AI run)	91.7%	90.2%	DeepSeek V4 Pro 0813
ARC-AGI	90.5%	91.0%	GLM 5.3 Flash
ARC-AGI-2	61.3%	65.8%	GLM 5.3 Flash
Chess puzzles	46.0%	21.0%	DeepSeek V4 Pro 0813
Mystery game puzzles	43.0%	8.0%	DeepSeek V4 Pro 0813
LMArena Text: hard prompts	1458	1490	GLM 5.3 Flash
LMArena Text: expert	1459	1511	GLM 5.3 Flash
LMArena Text: hard prompts in English	1463	1488	GLM 5.3 Flash
LMArena Vision: diagrams	Not measured	1317	Only GLM 5.3 Flash measured
LMArena Vision: homework	Not measured	1318	Only GLM 5.3 Flash measured
Neither model has a result on the other 6 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index42

Math benchmarks for DeepSeek V4 Pro 0813 and GLM 5.3 Flash
Benchmark	DeepSeek V4 Pro 0813	GLM 5.3 Flash	Leader
OTIS Mock AIME	98.6%	93.9%	DeepSeek V4 Pro 0813
FrontierMath, tiers 1 to 3	64.6%	55.8%	DeepSeek V4 Pro 0813
FrontierMath, tier 4	26.8%	17.1%	DeepSeek V4 Pro 0813
ProofBench	50.0%	21.0%	DeepSeek V4 Pro 0813
LMArena Text: math	1441	1503	GLM 5.3 Flash
LMArena Text: mathematical work	1443	1497	GLM 5.3 Flash
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index16

Knowledge benchmarks for DeepSeek V4 Pro 0813 and GLM 5.3 Flash
Benchmark	DeepSeek V4 Pro 0813	GLM 5.3 Flash	Leader
MMLU Pro	87.0%	86.1%	DeepSeek V4 Pro 0813
LegalBench (Vals AI)	82.4%	83.9%	GLM 5.3 Flash
CorpFin (Vals AI)	65.4%	Not measured	Only DeepSeek V4 Pro 0813 measured
TaxEval (Vals AI)	73.1%	75.6%	GLM 5.3 Flash
MedCode (Vals AI)	42.5%	Not measured	Only DeepSeek V4 Pro 0813 measured
SimpleQA Verified	52.9%	Not measured	Only DeepSeek V4 Pro 0813 measured
LMArena Text: science	1457	1484	GLM 5.3 Flash
LMArena Text: medicine and healthcare	1446	1475	GLM 5.3 Flash
LMArena Text: legal and government	1459	1473	GLM 5.3 Flash
LMArena Text: business and finance	1442	1460	GLM 5.3 Flash
Neither model has a result on the other 7 boards here

Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared

Web design benchmarks for DeepSeek V4 Pro 0813 and GLM 5.3 Flash
Benchmark	DeepSeek V4 Pro 0813	GLM 5.3 Flash	Leader
Design Arena: websites	Not measured	1272	Only GLM 5.3 Flash measured
Design Arena: UI components	Not measured	1316	Only GLM 5.3 Flash measured
Design Arena: game dev	Not measured	1279	Only GLM 5.3 Flash measured
Design Arena: data viz	Not measured	1262	Only GLM 5.3 Flash measured
Design Arena: 3D	Not measured	1316	Only GLM 5.3 Flash measured
Design Arena: all code categories	Not measured	1282	Only GLM 5.3 Flash measured
Design Arena: SVG	Not measured	1284	Only GLM 5.3 Flash measured
Design Arena: ASCII art	Not measured	1269	Only GLM 5.3 Flash measured
LiveBench design	Not measured	77.2%	Only GLM 5.3 Flash measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index314

General benchmarks for DeepSeek V4 Pro 0813 and GLM 5.3 Flash
Benchmark	DeepSeek V4 Pro 0813	GLM 5.3 Flash	Leader
Vals Index	53.0%	50.9%	DeepSeek V4 Pro 0813
LiveBench data analysis	Not measured	76.4%	Only GLM 5.3 Flash measured
MedScribe (Vals AI)	80.2%	88.9%	GLM 5.3 Flash
Long context reasoning (AA-LCR)	80.3%	80.0%	DeepSeek V4 Pro 0813
DTBench	93.9%	Not measured	Only DeepSeek V4 Pro 0813 measured
LMCA	45.5%	Not measured	Only DeepSeek V4 Pro 0813 measured
Surface Evolver Bench	Not measured	52.5%	Only GLM 5.3 Flash measured
LMArena Text	1446	1471	GLM 5.3 Flash
LMArena Text: instruction following	1448	1478	GLM 5.3 Flash
LMArena Text: creative writing	1439	1441	GLM 5.3 Flash
LMArena Vision	Not measured	1294	Only GLM 5.3 Flash measured
LMArena Text: English	1457	1474	GLM 5.3 Flash
LMArena Text: non-English	1431	1462	GLM 5.3 Flash
LMArena Text: Chinese	1481	1531	GLM 5.3 Flash
LMArena Text: German	Not measured	1462	Only GLM 5.3 Flash measured
LMArena Text: Japanese	Not measured	1444	Only GLM 5.3 Flash measured
LMArena Text: Korean	1449	1436	DeepSeek V4 Pro 0813
LMArena Text: French	1446	1491	GLM 5.3 Flash
LMArena Text: Spanish	1435	1475	GLM 5.3 Flash
LMArena Text: Russian	1448	1465	GLM 5.3 Flash
LMArena Text: Polish	Not measured	1450	Only GLM 5.3 Flash measured
LMArena Text: multi-turn	1454	1469	GLM 5.3 Flash
LMArena Text: longer queries	1458	1481	GLM 5.3 Flash
LMArena Text: writing and language	1443	1455	GLM 5.3 Flash
LMArena Text: entertainment and media	1424	1440	GLM 5.3 Flash
LMArena Vision: English	Not measured	1295	Only GLM 5.3 Flash measured
LMArena Vision: Chinese	Not measured	1363	Only GLM 5.3 Flash measured
LMArena Vision: reading text	Not measured	1299	Only GLM 5.3 Flash measured
LMArena Vision: humor	Not measured	1279	Only GLM 5.3 Flash measured
LMArena Vision: creative writing	Not measured	1270	Only GLM 5.3 Flash measured
Neither model has a result on the other 16 boards here

Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared

Image generation benchmarks for DeepSeek V4 Pro 0813 and GLM 5.3 Flash
Benchmark	DeepSeek V4 Pro 0813	GLM 5.3 Flash	Leader
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 0813$0.0016Fits in one request

GLM 5.3 Flash$0.0004Fits in one request

GLM 5.3 Flash costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Pro 0813$0.039Fits in one request

GLM 5.3 Flash$0.0090Fits in one request

GLM 5.3 Flash costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Pro 0813$0.152Fits in one request

GLM 5.3 Flash$0.035Fits in one request

GLM 5.3 Flash 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 0813 and GLM 5.3 Flash
Specification	DeepSeek V4 Pro 0813	Z.ai GLM 5.3 Flash
Maker	DeepSeek	Z.ai
Weights	Open weights	Open weights
Released	August 12, 2026	August 26, 2026
Context window	1.05M	1.05M
Input $/Mtok	$0.66	$0.15
Output $/Mtok	$1.98	$0.50
Coding index	54.6	71.0
Independent measurements behind it	1	2
Cost per solved task	$0.10	$0.02
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4 Pro 0813 better than GLM 5.3 Flash for coding?

Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 54.6 for DeepSeek V4 Pro 0813. Pick DeepSeek V4 Pro 0813 when what matters is LiveCodeBench (Vals AI) or SWE-bench Verified (Vals AI), where it leads.

Which is cheaper, DeepSeek V4 Pro 0813 or GLM 5.3 Flash?

DeepSeek V4 Pro 0813 costs $0.66 per million input tokens and $1.98 per million output tokens; GLM 5.3 Flash costs $0.15 and $0.50. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0813 costs $0.10 and GLM 5.3 Flash $0.02.

Which has the bigger context window, DeepSeek V4 Pro 0813 or GLM 5.3 Flash?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4 Pro 0813 vs GLM 5.3 Flash comparison based on?

72 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Pro 0813's coding index rests on 1 independent benchmark and GLM 5.3 Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.

Decision reading

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Knowledge

Web design

General

Long documents

Everyday cost

Cost per solved task

At a glance

Benchmark by benchmark

What each workload costs

Chat turn

Repository review

Agent loop

Specification differences

Questions

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