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DeepSeek V4.1 Flash vs GLM 5.3

GLM 5.3 leads DeepSeek V4.1 Flash on coding, 70.6 to 62.5 on the Vibe Coding Index, and DeepSeek V4.1 Flash costs 5.2x less per output token ($0.84 vs $4.40 per million). Updated October 22, 2026; 84 benchmarks have a result for both.

Replace DeepSeek V4.1 Flash Replace GLM 5.3
DeepSeek V4.1 FlashDeepSeek
Coding
62.5
Overall
62.4
Output $/M
$0.84
32
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
52
vs boards won of 84 shared

DeepSeek V4.1 Flash or GLM 5.3: which to pick

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 62.5 for DeepSeek V4.1 Flash.

Pick DeepSeek V4.1 Flash for output tokens 5.2x cheaper ($0.84 vs $4.40 per million) and work measured by LiveBench coding or Vibe Code Bench (Vals AI), where it leads.

Model ADeepSeek V4.1 FlashDeepSeek62.5/1005 independent benchmarksNo cost per solved taskModel BGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task
Shared results
84
DeepSeek V4.1 Flash only
17
GLM 5.3 only
16
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 V4.1 Flash 62.5 from 5 benchmarks, GLM 5.3 70.6 from 6 benchmarks.
Widest category
Agentic work, 15.7 points apart on average over 10 shared benchmarks.
Widest single gap
LiveBench browser: DeepSeek V4.1 Flash 84.0% against GLM 5.3 0.0%, 84.0 points apart on a 0 to 100 scale.
Output price
DeepSeek V4.1 Flash $0.84 against GLM 5.3 $4.40 per million output tokens, 5.2x 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 V4.1 FlashBarsGLM 5.3Ties
Coding108
Intelligence02
Agentic work46
Reasoning36
Math24
Knowledge34
Web design72
General320
All shared boards3252
CodingLike-for-likeSix of these boards feed the Vibe Coding Index108
Coding benchmarks for DeepSeek V4.1 Flash and GLM 5.3
BenchmarkDeepSeek V4.1 FlashGLM 5.3Leader
LiveBench coding90.2%82.8%DeepSeek V4.1 Flash
LMArena WebDev16181621GLM 5.3
AA Coding IndexNot measured74.8Only GLM 5.3 measured
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)19.7%38.9%GLM 5.3
Vibe Code Bench (Vals AI)84.7%78.1%DeepSeek V4.1 Flash
Code migration (Vals AI)45.6%44.2%DeepSeek V4.1 Flash
ProgramBench (Vals AI)0.5%1.5%GLM 5.3
IOI (Vals AI)40.3%68.4%GLM 5.3
Terminal-Bench 2.1 (Vals AI)74.5%71.5%DeepSeek V4.1 Flash
SWE-bench Multimodal (Vals AI)46.5%Not measuredOnly DeepSeek V4.1 Flash measured
Vibe Code Bench 1-100 (Vals AI)16.4%20.0%GLM 5.3
SkillsBench (Vals AI)69.8%47.5%DeepSeek V4.1 Flash
CyberBench (Vals AI)73.7%72.1%DeepSeek V4.1 Flash
SciCode51.9%59.0%GLM 5.3
FrontierCode43.9%40.1%DeepSeek V4.1 Flash
FrontierSWENot measured30.2%Only GLM 5.3 measured
WeirdMLNot measured75.4%Only GLM 5.3 measured
ALE-Bench1092.31317.4GLM 5.3
LMArena Text: coding15021497DeepSeek V4.1 Flash
LMArena Text: software and IT14961493DeepSeek V4.1 Flash
LMArena WebDev: React16281641GLM 5.3
LMArena WebDev: HTML16121595DeepSeek V4.1 Flash
LMArena WebDev: from an image1606Not measuredOnly DeepSeek V4.1 Flash measured
Neither model has a result on the other 14 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for DeepSeek V4.1 Flash and GLM 5.3
BenchmarkDeepSeek V4.1 FlashGLM 5.3Leader
AA Intelligence Index24.744.8GLM 5.3
Epoch Capabilities Index155.9156.0GLM 5.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index46
Agentic work benchmarks for DeepSeek V4.1 Flash and GLM 5.3
BenchmarkDeepSeek V4.1 FlashGLM 5.3Leader
AA Agentic IndexNot measured53.1Only GLM 5.3 measured
Design Arena: agents building mobile appsNot measured1194Only GLM 5.3 measured
Design Arena: HTML slidesNot measured1189Only GLM 5.3 measured
Design Arena: PowerPoint slidesNot measured1230Only GLM 5.3 measured
LiveBench research86.8%73.2%DeepSeek V4.1 Flash
LiveBench browser84.0%0.0%DeepSeek V4.1 Flash
LiveBench long agentic40.1%35.9%DeepSeek V4.1 Flash
SREBench (Vals AI)0.8%9.2%GLM 5.3
Finance Agent (Vals AI)53.5%55.8%GLM 5.3
Tax Agent Bench (Vals AI)26.6%39.7%GLM 5.3
Legal Research Bench (Vals AI)41.4%49.0%GLM 5.3
Harvey's Legal Agent Benchmark (Vals AI)Not measured14.6%Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)57.2%56.3%DeepSeek V4.1 Flash
BioMysteryBench (Vals AI)67.8%Not measuredOnly DeepSeek V4.1 Flash measured
Terminal-Bench-Science (Vals AI)0.0%5.7%GLM 5.3
Vals Science Index29.6%Not measuredOnly DeepSeek V4.1 Flash measured
APEX-Agents39.5%56.6%GLM 5.3
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 index36
Reasoning benchmarks for DeepSeek V4.1 Flash and GLM 5.3
BenchmarkDeepSeek V4.1 FlashGLM 5.3Leader
GPQA DiamondNot measured88.1%Only GLM 5.3 measured
LiveBench reasoning86.7%85.8%DeepSeek V4.1 Flash
MysteryMechanism (Vals AI)21.2%23.0%GLM 5.3
GPQA Diamond (Artificial Analysis run)Not measured91.7%Only GLM 5.3 measured
Humanity's Last Exam (Artificial Analysis run)39.2%42.3%GLM 5.3
GPQA Diamond (Epoch AI run)89.8%90.9%GLM 5.3
ARC-AGI94.5%Not measuredOnly DeepSeek V4.1 Flash measured
ARC-AGI-272.9%Not measuredOnly DeepSeek V4.1 Flash measured
Chess puzzles48.0%37.0%DeepSeek V4.1 Flash
Mystery game puzzles43.0%33.0%DeepSeek V4.1 Flash
LMArena Text: hard prompts14831490GLM 5.3
LMArena Text: expert15011514GLM 5.3
LMArena Text: hard prompts in English14881496GLM 5.3
LMArena Vision: diagrams1297Not measuredOnly DeepSeek V4.1 Flash measured
LMArena Vision: homework1307Not measuredOnly DeepSeek V4.1 Flash measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index24
Math benchmarks for DeepSeek V4.1 Flash and GLM 5.3
BenchmarkDeepSeek V4.1 FlashGLM 5.3Leader
OTIS Mock AIME98.3%91.1%DeepSeek V4.1 Flash
FrontierMath, tiers 1 to 367.4%68.8%GLM 5.3
FrontierMath, tier 426.8%29.3%GLM 5.3
ProofBench54.0%49.0%DeepSeek V4.1 Flash
LMArena Text: math14781495GLM 5.3
LMArena Text: mathematical work14851497GLM 5.3
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index34
Knowledge benchmarks for DeepSeek V4.1 Flash and GLM 5.3
BenchmarkDeepSeek V4.1 FlashGLM 5.3Leader
MMLU ProNot measured86.8%Only GLM 5.3 measured
LegalBench (Vals AI)83.3%84.8%GLM 5.3
TaxEval (Vals AI)Not measured72.4%Only GLM 5.3 measured
MedCode (Vals AI)41.2%42.9%GLM 5.3
SimpleQA Verified42.9%41.0%DeepSeek V4.1 Flash
LMArena Text: science14691498GLM 5.3
LMArena Text: medicine and healthcare14841481DeepSeek V4.1 Flash
LMArena Text: legal and government14431473GLM 5.3
LMArena Text: business and finance14611459DeepSeek V4.1 Flash
Neither model has a result on the other 9 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index72
Web design benchmarks for DeepSeek V4.1 Flash and GLM 5.3
BenchmarkDeepSeek V4.1 FlashGLM 5.3Leader
Design Arena: websites13081295DeepSeek V4.1 Flash
Design Arena: UI components13121314GLM 5.3
Design Arena: game dev13301311DeepSeek V4.1 Flash
Design Arena: data viz12911260DeepSeek V4.1 Flash
Design Arena: 3D13551332DeepSeek V4.1 Flash
Design Arena: all code categories13201304DeepSeek V4.1 Flash
Design Arena: SVG12451294GLM 5.3
Design Arena: ASCII art12651208DeepSeek V4.1 Flash
LiveBench design79.2%77.3%DeepSeek V4.1 Flash
GeneralLike-for-likePublic boards, shown as published, kept out of the index320
General benchmarks for DeepSeek V4.1 Flash and GLM 5.3
BenchmarkDeepSeek V4.1 FlashGLM 5.3Leader
Vals IndexNot measured59.7%Only GLM 5.3 measured
LiveBench data analysis79.3%70.2%DeepSeek V4.1 Flash
Public Benefits Bench (Vals AI)64.3%68.5%GLM 5.3
SAGE (Vals AI)47.9%Not measuredOnly DeepSeek V4.1 Flash measured
MedScribe (Vals AI)85.5%88.8%GLM 5.3
Long context reasoning (AA-LCR)84.0%79.7%DeepSeek V4.1 Flash
DTBench89.9%87.7%DeepSeek V4.1 Flash
LMCA47.0%55.5%GLM 5.3
Surface Evolver Bench46.3%Not measuredOnly DeepSeek V4.1 Flash measured
Furniture assembly34.2%Not measuredOnly DeepSeek V4.1 Flash measured
LMArena Text14641474GLM 5.3
LMArena Text: instruction following14731479GLM 5.3
LMArena Text: creative writing14371458GLM 5.3
LMArena Vision1278Not measuredOnly DeepSeek V4.1 Flash measured
LMArena Text: English14731482GLM 5.3
LMArena Text: non-English14491459GLM 5.3
LMArena Text: Chinese15011532GLM 5.3
LMArena Text: German14701488GLM 5.3
LMArena Text: Japanese14321460GLM 5.3
LMArena Text: Korean14361460GLM 5.3
LMArena Text: French14651503GLM 5.3
LMArena Text: Spanish14591462GLM 5.3
LMArena Text: Russian14671469GLM 5.3
LMArena Text: Polish14571466GLM 5.3
LMArena Text: multi-turn14611472GLM 5.3
LMArena Text: longer queries14731483GLM 5.3
LMArena Text: writing and language14501469GLM 5.3
LMArena Text: entertainment and media14451447GLM 5.3
LMArena Vision: English1288Not measuredOnly DeepSeek V4.1 Flash measured
LMArena Vision: Chinese1327Not measuredOnly DeepSeek V4.1 Flash measured
LMArena Vision: reading text1282Not measuredOnly DeepSeek V4.1 Flash measured
LMArena Vision: humor1260Not measuredOnly DeepSeek V4.1 Flash measured
LMArena Vision: creative writing1275Not measuredOnly DeepSeek V4.1 Flash measured
Neither model has a result on the other 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V4.1 Flash and GLM 5.3
BenchmarkDeepSeek V4.1 FlashGLM 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 V4.1 Flash$0.0006Fits in one request
GLM 5.3$0.0036Fits in one request

DeepSeek V4.1 Flash costs less

Repository review

50K input + 3K output tokens

DeepSeek V4.1 Flash$0.014Fits in one request
GLM 5.3$0.083Fits in one request

DeepSeek V4.1 Flash costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4.1 Flash$0.053Fits in one request
GLM 5.3$0.324Fits in one request

DeepSeek V4.1 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 V4.1 Flash and GLM 5.3
SpecificationDeepSeek V4.1 FlashZ.ai GLM 5.3
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedSeptember 10, 2026August 18, 2026
Context window1.05M1.05M
Input $/Mtok$0.2225$1.40
Output $/Mtok$0.84$4.40
Coding index62.570.6
Independent measurements behind it56
Cost per solved taskNot computable$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4.1 Flash better than GLM 5.3 for coding?

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 62.5 for DeepSeek V4.1 Flash. Pick DeepSeek V4.1 Flash for output tokens 5.2x cheaper ($0.84 vs $4.40 per million) and work measured by LiveBench coding or Vibe Code Bench (Vals AI), where it leads.

Which is cheaper, DeepSeek V4.1 Flash or GLM 5.3?

DeepSeek V4.1 Flash costs $0.2225 per million input tokens and $0.84 per million output tokens; GLM 5.3 costs $1.40 and $4.40. Cost per solved task is known for GLM 5.3 only.

Which has the bigger context window, DeepSeek V4.1 Flash or GLM 5.3?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4.1 Flash vs GLM 5.3 comparison based on?

84 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4.1 Flash's coding index rests on 5 independent benchmarks and GLM 5.3's on 6. Where only one model has a result, the page says so instead of naming a winner.

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