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DeepSeek V4 Flash 0423 vs GLM 4.7

Updated October 21, 2026. 44 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 4.7
DeepSeek V4 Flash 0423DeepSeek
Coding
54.6
Overall
24.4
Output $/M
$0.182
23
GLM 4.7Z.ai
Coding
33.6
Overall
22.2
Output $/M
$1.98
21
vs boards won of 44 shared

Decision reading

Pick DeepSeek V4 Flash 0423 for coding: it scores 54.6 on the Vibe Coding Index against 33.6 for GLM 4.7.

Pick GLM 4.7 when what matters is LMArena Text: software and IT or SimpleBench, where it leads.

Model ADeepSeek V4 Flash 0423DeepSeek54.6/1001 independent benchmarkNo cost per solved taskModel BGLM 4.7Z.ai33.6/1001 independent benchmark$0.29 per solved task
Shared results
44
DeepSeek V4 Flash 0423 only
4
GLM 4.7 only
36
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.

At a glance

Coding index
DeepSeek V4 Flash 0423 54.6 from 1 benchmark, GLM 4.7 33.6 from 1 benchmark.
Widest category
Coding, 11.9 points apart on average over 7 shared benchmarks.
Widest single gap
LMArena WebDev: React: DeepSeek V4 Flash 0423 1585 against GLM 4.7 1420, 27.5 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.182 against GLM 4.7 $1.98 per million output tokens, 10.9x apart.
Context window
DeepSeek V4 Flash 0423 takes 1.05M tokens, GLM 4.7 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 4.7Ties
Coding61
Intelligence20
Reasoning22
Math20
Knowledge04
Web design26
General98
All shared boards2321
CodingLike-for-likeFour of these boards feed the Vibe Coding Index61
Coding benchmarks for DeepSeek V4 Flash 0423 and GLM 4.7
BenchmarkDeepSeek V4 Flash 0423GLM 4.7Leader
LMArena WebDev15821435DeepSeek V4 Flash 0423
AA Coding Index52.045.3DeepSeek V4 Flash 0423
LiveCodeBench (Vals AI)Not measured82.2%Only GLM 4.7 measured
SWE-bench Verified (Vals AI)Not measured69.4%Only GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)Not measured89.4%Only GLM 4.7 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured31.8%Only GLM 4.7 measured
Terminal-BenchNot measured33.4%Only GLM 4.7 measured
WeirdML45.6%Not measuredOnly DeepSeek V4 Flash 0423 measured
ALE-Bench678.2399.5DeepSeek V4 Flash 0423
LMArena Text: coding14571454DeepSeek V4 Flash 0423
LMArena Text: software and IT14531453GLM 4.7
LMArena WebDev: React15851420DeepSeek V4 Flash 0423
LMArena WebDev: HTML15581447DeepSeek V4 Flash 0423
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for DeepSeek V4 Flash 0423 and GLM 4.7
BenchmarkDeepSeek V4 Flash 0423GLM 4.7Leader
AA Intelligence Index24.422.2DeepSeek V4 Flash 0423
Epoch Capabilities Index146.1143.6DeepSeek V4 Flash 0423
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Agentic work benchmarks for DeepSeek V4 Flash 0423 and GLM 4.7
BenchmarkDeepSeek V4 Flash 0423GLM 4.7Leader
AA Agentic Index26.3Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: agents building full-stack appsNot measured1050Only GLM 4.7 measured
Design Arena: agents building mobile appsNot measured1105Only GLM 4.7 measured
Design Arena: agents building Android appsNot measured1118Only GLM 4.7 measured
Design Arena: agents building Godot gamesNot measured1058Only GLM 4.7 measured
τ²-Bench TelecomNot measured95.9%Only GLM 4.7 measured
Vending-Bench 2Not measured$2,377Only GLM 4.7 measured
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index22
Reasoning benchmarks for DeepSeek V4 Flash 0423 and GLM 4.7
BenchmarkDeepSeek V4 Flash 0423GLM 4.7Leader
GPQA DiamondNot measured80.0%Only GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)Not measured85.9%Only GLM 4.7 measured
Humanity's Last Exam (Artificial Analysis run)Not measured27.4%Only GLM 4.7 measured
GPQA Diamond (Epoch AI run)Not measured83.3%Only GLM 4.7 measured
SimpleBench46.3%47.7%GLM 4.7
Chess puzzlesNot measured21.0%Only GLM 4.7 measured
LMArena Text: hard prompts14441443DeepSeek V4 Flash 0423
LMArena Text: expert14411424DeepSeek V4 Flash 0423
LMArena Text: hard prompts in English14491452GLM 4.7
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for DeepSeek V4 Flash 0423 and GLM 4.7
BenchmarkDeepSeek V4 Flash 0423GLM 4.7Leader
AIMENot measured93.3%Only GLM 4.7 measured
MGSM (Vals AI)Not measured88.2%Only GLM 4.7 measured
AA Math IndexNot measured95.0Only GLM 4.7 measured
AIME 2025 (Artificial Analysis run)Not measured95.0%Only GLM 4.7 measured
OTIS Mock AIMENot measured83.3%Only GLM 4.7 measured
ProofBenchNot measured6.0%Only GLM 4.7 measured
LMArena Text: math14261424DeepSeek V4 Flash 0423
LMArena Text: mathematical work14321411DeepSeek V4 Flash 0423
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for DeepSeek V4 Flash 0423 and GLM 4.7
BenchmarkDeepSeek V4 Flash 0423GLM 4.7Leader
MMLU ProNot measured82.7%Only GLM 4.7 measured
LegalBench (Vals AI)Not measured83.4%Only GLM 4.7 measured
CaseLaw (Vals AI)Not measured54.9%Only GLM 4.7 measured
CorpFin (Vals AI)Not measured46.4%Only GLM 4.7 measured
TaxEval (Vals AI)Not measured68.8%Only GLM 4.7 measured
MedQA (Vals AI)Not measured93.7%Only GLM 4.7 measured
MedCode (Vals AI)Not measured32.8%Only GLM 4.7 measured
MMLU Pro (Artificial Analysis run)Not measured85.6%Only GLM 4.7 measured
SimpleQA VerifiedNot measured32.2%Only GLM 4.7 measured
LMArena Text: science14461457GLM 4.7
LMArena Text: medicine and healthcare14481469GLM 4.7
LMArena Text: legal and government14391444GLM 4.7
LMArena Text: business and finance14251429GLM 4.7
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index26
Web design benchmarks for DeepSeek V4 Flash 0423 and GLM 4.7
BenchmarkDeepSeek V4 Flash 0423GLM 4.7Leader
Design Arena: websites12081226GLM 4.7
Design Arena: UI components11641199GLM 4.7
Design Arena: game dev11971189DeepSeek V4 Flash 0423
Design Arena: data viz11271195GLM 4.7
Design Arena: 3D11891191GLM 4.7
Design Arena: all code categories12051217GLM 4.7
Design Arena: SVG11551142DeepSeek V4 Flash 0423
Design Arena: ASCII art11091165GLM 4.7
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index98
General benchmarks for DeepSeek V4 Flash 0423 and GLM 4.7
BenchmarkDeepSeek V4 Flash 0423GLM 4.7Leader
MedScribe (Vals AI)Not measured68.6%Only GLM 4.7 measured
IFBenchNot measured67.9%Only GLM 4.7 measured
Long context reasoning (AA-LCR)Not measured71.0%Only GLM 4.7 measured
DTBench86.4%Not measuredOnly DeepSeek V4 Flash 0423 measured
LMCA35.9%Not measuredOnly DeepSeek V4 Flash 0423 measured
CL-benchNot measured15.9%Only GLM 4.7 measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena Text14321435GLM 4.7
LMArena Text: instruction following14211412DeepSeek V4 Flash 0423
LMArena Text: creative writing14031401DeepSeek V4 Flash 0423
LMArena Text: English14411453GLM 4.7
LMArena Text: non-English14191417DeepSeek V4 Flash 0423
LMArena Text: Chinese14631495GLM 4.7
LMArena Text: German14191426GLM 4.7
LMArena Text: Japanese13891439GLM 4.7
LMArena Text: Korean13831399GLM 4.7
LMArena Text: French14391432DeepSeek V4 Flash 0423
LMArena Text: Spanish14361434DeepSeek V4 Flash 0423
LMArena Text: Russian14271423DeepSeek V4 Flash 0423
LMArena Text: Polish14361435DeepSeek V4 Flash 0423
LMArena Text: multi-turn14491446DeepSeek V4 Flash 0423
LMArena Text: longer queries14341432DeepSeek V4 Flash 0423
LMArena Text: writing and language14101411GLM 4.7
LMArena Text: entertainment and media14001400GLM 4.7
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 4.7
BenchmarkDeepSeek V4 Flash 0423GLM 4.7Leader
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 4.7$0.0015Fits 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 4.7$0.033Fits 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 4.7$0.128Exceeds 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 4.7
SpecificationDeepSeek V4 Flash 0423Z.ai GLM 4.7
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedApril 24, 2026December 22, 2025
Context window1.05M205K
Input $/Mtok$0.091$0.54
Output $/Mtok$0.182$1.98
Coding index54.633.6
Independent measurements behind it11
Cost per solved taskNot computable$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Flash 0423 better than GLM 4.7 for coding?

Pick DeepSeek V4 Flash 0423 for coding: it scores 54.6 on the Vibe Coding Index against 33.6 for GLM 4.7. Pick GLM 4.7 when what matters is LMArena Text: software and IT or SimpleBench, where it leads.

Which is cheaper, DeepSeek V4 Flash 0423 or GLM 4.7?

DeepSeek V4 Flash 0423 costs $0.091 per million input tokens and $0.182 per million output tokens; GLM 4.7 costs $0.54 and $1.98. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, DeepSeek V4 Flash 0423 or GLM 4.7?

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

How many benchmarks is the DeepSeek V4 Flash 0423 vs GLM 4.7 comparison based on?

44 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 4.7's on 1. Where only one model has a result, the page says so instead of naming a winner.

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