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DeepSeek V4 Pro 0813 vs GLM 4.7 Flash

DeepSeek V4 Pro 0813 leads GLM 4.7 Flash on the Artificial Analysis Intelligence Index, 36.0 to 14.9, and GLM 4.7 Flash costs 4.9x less per output token ($0.40 vs $1.98 per million). Updated October 21, 2026; 31 benchmarks have a result for both.

DeepSeek V4 Pro 0813DeepSeek
Coding
56.1
Overall
60.8
Output $/M
$1.98
31
GLM 4.7 FlashZ.ai
Coding
No data
Overall
42.1
Output $/M
$0.40
0
vs boards won of 31 shared

DeepSeek V4 Pro 0813 or GLM 4.7 Flash: which to pick

Only DeepSeek V4 Pro 0813 has a coding score, 56.1 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding.

Pick GLM 4.7 Flash for output tokens 4.9x cheaper ($0.40 vs $1.98 per million).

Shared results
31
DeepSeek V4 Pro 0813 only
47
GLM 4.7 Flash only
12
Like-for-like categories
6 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 0813 56.1 from 5 benchmarks, GLM 4.7 Flash not benchmarked.
Widest category
Reasoning, 24.8 points apart on average over 6 shared benchmarks.
Widest single gap
OTIS Mock AIME: DeepSeek V4 Pro 0813 98.6% against GLM 4.7 Flash 58.3%, 40.3 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Pro 0813 $1.98 against GLM 4.7 Flash $0.40 per million output tokens, 4.9x apart.
Context window
DeepSeek V4 Pro 0813 takes 1.05M tokens, GLM 4.7 Flash 200K.

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 0813BarsGLM 4.7 FlashTies
Coding20
Intelligence10
Reasoning60
Math30
Knowledge40
General150
All shared boards310
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
Coding benchmarks for DeepSeek V4 Pro 0813 and GLM 4.7 Flash
BenchmarkDeepSeek V4 Pro 0813GLM 4.7 FlashLeader
LMArena WebDev1582Not measuredOnly DeepSeek V4 Pro 0813 measured
AA Coding Index68.8Not measuredOnly DeepSeek V4 Pro 0813 measured
LiveCodeBench (Vals AI)87.5%Not measuredOnly DeepSeek V4 Pro 0813 measured
SWE-bench Verified (Vals AI)96.4%Not measuredOnly DeepSeek V4 Pro 0813 measured
Terminal-Bench 4 (Vals AI)14.1%Not measuredOnly DeepSeek V4 Pro 0813 measured
Vibe Code Bench (Vals AI)82.3%Not measuredOnly DeepSeek V4 Pro 0813 measured
Code migration (Vals AI)41.5%Not measuredOnly DeepSeek V4 Pro 0813 measured
ProgramBench (Vals AI)0.0%Not measuredOnly DeepSeek V4 Pro 0813 measured
IOI (Vals AI)51.6%Not measuredOnly DeepSeek V4 Pro 0813 measured
Terminal-Bench 2.1 (Vals AI)54.7%Not measuredOnly DeepSeek V4 Pro 0813 measured
Vibe Code Bench 1-100 (Vals AI)17.5%Not measuredOnly DeepSeek V4 Pro 0813 measured
SkillsBench (Vals AI)53.8%Not measuredOnly DeepSeek V4 Pro 0813 measured
CyberBench (Vals AI)72.9%Not measuredOnly DeepSeek V4 Pro 0813 measured
SciCode51.0%Not measuredOnly DeepSeek V4 Pro 0813 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured22.0%Only GLM 4.7 Flash measured
FrontierCode28.5%Not measuredOnly DeepSeek V4 Pro 0813 measured
WeirdML66.2%Not measuredOnly DeepSeek V4 Pro 0813 measured
ALE-Bench1403.2Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: coding14691382DeepSeek V4 Pro 0813
LMArena Text: software and IT14621389DeepSeek V4 Pro 0813
LMArena WebDev: React1588Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena WebDev: HTML1566Not measuredOnly DeepSeek V4 Pro 0813 measured
Neither model has a result on the other 17 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V4 Pro 0813 and GLM 4.7 Flash
BenchmarkDeepSeek V4 Pro 0813GLM 4.7 FlashLeader
AA Intelligence Index36.014.9DeepSeek V4 Pro 0813
Epoch Capabilities Index155.6Not measuredOnly DeepSeek V4 Pro 0813 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Agentic work benchmarks for DeepSeek V4 Pro 0813 and GLM 4.7 Flash
BenchmarkDeepSeek V4 Pro 0813GLM 4.7 FlashLeader
AA Agentic Index41.3Not measuredOnly DeepSeek V4 Pro 0813 measured
SREBench (Vals AI)1.9%Not measuredOnly DeepSeek V4 Pro 0813 measured
Finance Agent (Vals AI)50.4%Not measuredOnly DeepSeek V4 Pro 0813 measured
Tax Agent Bench (Vals AI)26.8%Not measuredOnly DeepSeek V4 Pro 0813 measured
Legal Research Bench (Vals AI)40.9%Not measuredOnly DeepSeek V4 Pro 0813 measured
Harvey's Legal Agent Benchmark (Vals AI)10.8%Not measuredOnly DeepSeek V4 Pro 0813 measured
Excel Modeling Benchmark (Vals AI)52.8%Not measuredOnly DeepSeek V4 Pro 0813 measured
Terminal-Bench-Science (Vals AI)4.3%Not measuredOnly DeepSeek V4 Pro 0813 measured
τ²-Bench TelecomNot measured98.8%Only GLM 4.7 Flash measured
APEX-Agents47.3%Not measuredOnly DeepSeek V4 Pro 0813 measured
Neither model has a result on the other 31 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for DeepSeek V4 Pro 0813 and GLM 4.7 Flash
BenchmarkDeepSeek V4 Pro 0813GLM 4.7 FlashLeader
GPQA Diamond92.4%Not measuredOnly DeepSeek V4 Pro 0813 measured
GPQA Diamond (Artificial Analysis run)92.8%58.1%DeepSeek V4 Pro 0813
Humanity's Last Exam (Artificial Analysis run)41.0%7.6%DeepSeek V4 Pro 0813
GPQA Diamond (Epoch AI run)91.7%60.5%DeepSeek V4 Pro 0813
ARC-AGI90.5%Not measuredOnly DeepSeek V4 Pro 0813 measured
ARC-AGI-261.3%Not measuredOnly DeepSeek V4 Pro 0813 measured
Chess puzzles46.0%Not measuredOnly DeepSeek V4 Pro 0813 measured
Mystery game puzzles43.0%Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: hard prompts14581356DeepSeek V4 Pro 0813
LMArena Text: expert14591357DeepSeek V4 Pro 0813
LMArena Text: hard prompts in English14631369DeepSeek V4 Pro 0813
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for DeepSeek V4 Pro 0813 and GLM 4.7 Flash
BenchmarkDeepSeek V4 Pro 0813GLM 4.7 FlashLeader
OTIS Mock AIME98.6%58.3%DeepSeek V4 Pro 0813
FrontierMath, tiers 1 to 364.6%Not measuredOnly DeepSeek V4 Pro 0813 measured
FrontierMath, tier 426.8%Not measuredOnly DeepSeek V4 Pro 0813 measured
ProofBench50.0%Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: math14411355DeepSeek V4 Pro 0813
LMArena Text: mathematical work14431369DeepSeek V4 Pro 0813
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for DeepSeek V4 Pro 0813 and GLM 4.7 Flash
BenchmarkDeepSeek V4 Pro 0813GLM 4.7 FlashLeader
MMLU Pro87.0%Not measuredOnly DeepSeek V4 Pro 0813 measured
LegalBench (Vals AI)82.4%Not measuredOnly DeepSeek V4 Pro 0813 measured
CorpFin (Vals AI)65.4%Not measuredOnly DeepSeek V4 Pro 0813 measured
TaxEval (Vals AI)73.1%Not measuredOnly DeepSeek V4 Pro 0813 measured
MedCode (Vals AI)42.5%Not measuredOnly DeepSeek V4 Pro 0813 measured
SimpleQA Verified52.9%Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text: science14571358DeepSeek V4 Pro 0813
LMArena Text: medicine and healthcare14461343DeepSeek V4 Pro 0813
LMArena Text: legal and government14591357DeepSeek V4 Pro 0813
LMArena Text: business and finance14421343DeepSeek V4 Pro 0813
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for DeepSeek V4 Pro 0813 and GLM 4.7 Flash
BenchmarkDeepSeek V4 Pro 0813GLM 4.7 FlashLeader
Design Arena: websitesNot measured1194Only GLM 4.7 Flash measured
Design Arena: UI componentsNot measured1208Only GLM 4.7 Flash measured
Design Arena: game devNot measured1135Only GLM 4.7 Flash measured
Design Arena: data vizNot measured1123Only GLM 4.7 Flash measured
Design Arena: 3DNot measured1121Only GLM 4.7 Flash measured
Design Arena: all code categoriesNot measured1178Only GLM 4.7 Flash measured
Design Arena: SVGNot measured1038Only GLM 4.7 Flash measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index150
General benchmarks for DeepSeek V4 Pro 0813 and GLM 4.7 Flash
BenchmarkDeepSeek V4 Pro 0813GLM 4.7 FlashLeader
Vals Index53.0%Not measuredOnly DeepSeek V4 Pro 0813 measured
MedScribe (Vals AI)80.2%Not measuredOnly DeepSeek V4 Pro 0813 measured
IFBenchNot measured60.8%Only GLM 4.7 Flash measured
Long context reasoning (AA-LCR)80.3%41.7%DeepSeek V4 Pro 0813
DTBench93.9%Not measuredOnly DeepSeek V4 Pro 0813 measured
LMCA45.5%Not measuredOnly DeepSeek V4 Pro 0813 measured
LMArena Text14461351DeepSeek V4 Pro 0813
LMArena Text: instruction following14481327DeepSeek V4 Pro 0813
LMArena Text: creative writing14391296DeepSeek V4 Pro 0813
LMArena Text: English14571373DeepSeek V4 Pro 0813
LMArena Text: non-English14311330DeepSeek V4 Pro 0813
LMArena Text: Chinese14811403DeepSeek V4 Pro 0813
LMArena Text: GermanNot measured1336Only GLM 4.7 Flash measured
LMArena Text: Korean14491283DeepSeek V4 Pro 0813
LMArena Text: French14461332DeepSeek V4 Pro 0813
LMArena Text: Spanish14351350DeepSeek V4 Pro 0813
LMArena Text: Russian14481333DeepSeek V4 Pro 0813
LMArena Text: PolishNot measured1291Only GLM 4.7 Flash measured
LMArena Text: multi-turn14541342DeepSeek V4 Pro 0813
LMArena Text: longer queries14581345DeepSeek V4 Pro 0813
LMArena Text: writing and language14431320DeepSeek V4 Pro 0813
LMArena Text: entertainment and media14241304DeepSeek V4 Pro 0813
Neither model has a result on the other 24 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 4.7 Flash
BenchmarkDeepSeek V4 Pro 0813GLM 4.7 FlashLeader
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 4.7 Flash$0.0003Fits in one request

GLM 4.7 Flash costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Pro 0813$0.039Fits in one request
GLM 4.7 Flash$0.0045Fits in one request

GLM 4.7 Flash costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Pro 0813$0.152Fits in one request
GLM 4.7 Flash$0.017Exceeds the context window

GLM 4.7 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 4.7 Flash
SpecificationDeepSeek V4 Pro 0813Z.ai GLM 4.7 Flash
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedAugust 12, 2026January 19, 2026
Context window1.05M200K
Input $/Mtok$0.66$0.0653
Output $/Mtok$1.98$0.40
Coding index56.1Not benchmarked
Independent measurements behind it50
Cost per solved task$0.10Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Pro 0813 better than GLM 4.7 Flash for coding?

Only DeepSeek V4 Pro 0813 has a coding score, 56.1 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 4.9x cheaper ($0.40 vs $1.98 per million).

Which is cheaper, DeepSeek V4 Pro 0813 or GLM 4.7 Flash?

DeepSeek V4 Pro 0813 costs $0.66 per million input tokens and $1.98 per million output tokens; GLM 4.7 Flash costs $0.0653 and $0.40. Cost per solved task is known for DeepSeek V4 Pro 0813 only.

Which has the bigger context window, DeepSeek V4 Pro 0813 or GLM 4.7 Flash?

DeepSeek V4 Pro 0813, with 1.05M tokens against 200K.

How many benchmarks is the DeepSeek V4 Pro 0813 vs GLM 4.7 Flash comparison based on?

31 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Pro 0813's coding index rests on 5 independent benchmarks and GLM 4.7 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.

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