DeepSeek V3 vs GLM-5.3

Updated September 28, 2026. Rank says GLM-5.3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

DeepSeek V3

DeepSeek

31.91/100

Supported · Public rank #149

90% interval 15.9–47.9

Model B

GLM-5.3

Z.AI

65.44/100

Estimated · Public rank #27

90% interval 59.7–71.2

Shared results: 0
DeepSeek V3 only: 6
GLM-5.3 only: 24
Like-for-like categories: 0 / 8

Supported: DeepSeek V3 · Estimated: GLM-5.3How the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Long documents
Prompts that approach the documented context limit
GLM-5.3
GLM-5.3 has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
DeepSeek V3 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
DeepSeek V3 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. DeepSeek V3 does not fit this workload in one request. GLM-5.3 has no comparable published API token rate.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

18.9DeepSeek V356.7GLM-5.3

Directional only · BenchAlign v5.7

GLM-5.3 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Directional only

DeepSeek V3: 11.3; Estimated · #107/111
GLM-5.3: 67.3; Supported · #9/111
Basis: BenchAlign v5.7 lane · 0 vs 9 public rows
Reading: Directional only

Coding

Directional only

DeepSeek V3: 18.9; Estimated · #124/136
GLM-5.3: 56.7; Supported · #22/136
Basis: BenchAlign v5.7 lane · 2 vs 13 public rows
Reading: Directional only

Knowledge

Directional only

DeepSeek V3: 30.3; Estimated · #132/160
GLM-5.3: 61.9; Supported · #33/160
Basis: BenchAlign v5.7 lane · 2 vs 2 public rows
Reading: Directional only

Reasoning

Not comparable

DeepSeek V3: 42.3; Unranked · 2 rankable rows
GLM-5.3: 77.1; #11/27
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

DeepSeek V3: Not ranked
GLM-5.3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

DeepSeek V3: Not ranked
GLM-5.3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

DeepSeek V3: 38.2; #103/124
GLM-5.3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

DeepSeek V3: 26.0; Unranked · 1 rankable row
GLM-5.3: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for DeepSeek V3 and GLM-5.3
Category	DeepSeek V3	GLM-5.3	Basis	Reading
Agentic	11.3Estimated · #107/111	67.3Supported · #9/111	Directional onlyBenchAlign v5.7 lane · 0 vs 9 public rows	Directional only
Coding	18.9Estimated · #124/136	56.7Supported · #22/136	Directional onlyBenchAlign v5.7 lane · 2 vs 13 public rows	Directional only
Knowledge	30.3Estimated · #132/160	61.9Supported · #33/160	Directional onlyBenchAlign v5.7 lane · 2 vs 2 public rows	Directional only
Reasoning	42.3Unranked · 2 rankable rows	77.1#11/27	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	38.2#103/124	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	26.0Unranked · 1 rankable row	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

DeepSeek V3: $0.00082; Fits in one request
GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request

GLM-5.3 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V3: $0.0168; Fits in one request
GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request

GLM-5.3 has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

DeepSeek V3: $0.0304; Does not fit in one request
GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

DeepSeek V3 does not fit this workload in one request. GLM-5.3 has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationDeepSeek V3GLM-5.3

Context window

Maximum documented context; output-token limits may be lower.

DeepSeek V3

128K

GLM-5.3

Z.AI GLM-5.3 model card

API model ID

DeepSeek V3

Not sourced

GLM-5.3

zai-org/GLM-5.3

Z.AI GLM-5.3 model card

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

DeepSeek V3

$0.07 per 1M cached input tokens

GLM-5.3

No comparable hosted API rate

Z.AI GLM-5.3 model card

Documented inputs

DeepSeek V3

Not sourced

GLM-5.3

Not sourced

Documented outputs

DeepSeek V3

Not sourced

GLM-5.3

Not sourced

Provider availability

DeepSeek V3

Not sourced

GLM-5.3

Not sourced

Reasoning profile

DeepSeek V3

Non-Reasoning

GLM-5.3

Reasoning

Weight access

DeepSeek V3

Open Weight

GLM-5.3

Open Weight

License

DeepSeek V3

Open Weight

GLM-5.3

Open Weight

Release date

DeepSeek V3

2024-12-26

GLM-5.3

2026-08-14

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GLM-5.3 has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, DeepSeek V3 or GLM-5.3?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, DeepSeek V3 or GLM-5.3?

GLM-5.3 scores higher for coding on the public lane, 56.7 to 18.9. DeepSeek V3 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, DeepSeek V3 or GLM-5.3?

GLM-5.3 scores higher for agentic tasks on the public lane, 67.3 to 11.3. DeepSeek V3 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, DeepSeek V3 or GLM-5.3?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, DeepSeek V3 or GLM-5.3?

GLM-5.3 has the larger documented context window: 1M, compared with 128K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

DeepSeek V3

API / mo$1,028

Self-host / mo$18,221

Break-even1.2B/day

GLM-5.3

API / mo$0

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence30 rows

Agentic

Terminal-Bench 2.1
DeepSeek V3—
GLM-5.388.2%
Source
Not directly comparable
terminalBench3
DeepSeek V3—
GLM-5.328.3%
Source
Not directly comparable
CyberGym
DeepSeek V3—
GLM-5.384.5%
Source
Not directly comparable
ExploitGym
DeepSeek V3—
GLM-5.315.0%
Source
Not directly comparable
Toolathlon-Verified
DeepSeek V3—
GLM-5.373.0%
Source
Not directly comparable
AutomationBench
DeepSeek V3—
GLM-5.348.2%
Source
Not directly comparable
Agents' Last Exam
DeepSeek V3—
GLM-5.328.5%
Source
Not directly comparable
HLE w/ tools
DeepSeek V3—
GLM-5.362.5%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
DeepSeek V3—
GLM-5.371.5%
Source
Not directly comparable

Coding

LiveCodeBench
DeepSeek V337.6%
Source
GLM-5.3—
Not directly comparable
SWE-bench Verified
DeepSeek V342%
Source
GLM-5.3—
Not directly comparable
Terminal-Bench 2.1
DeepSeek V3—
GLM-5.388.2%
Source
Not directly comparable
terminalBench3
DeepSeek V3—
GLM-5.328.3%
Source
Not directly comparable
DeepSWE
DeepSeek V3—
GLM-5.366.9%
Source
Not directly comparable
NL2Repo
DeepSeek V3—
GLM-5.358%
Source
Not directly comparable
ProgramBench
DeepSeek V3—
GLM-5.319.0%
Source
Not directly comparable
FrontierSWE
DeepSeek V3—
GLM-5.378.1%
Source
Not directly comparable
sweMarathon
DeepSeek V3—
GLM-5.342.5%
Source
Not directly comparable
PostTrain Bench
DeepSeek V3—
GLM-5.339.8%
Source
Not directly comparable
VulcanBench v3
DeepSeek V3—
GLM-5.378.3%
Source
Not directly comparable
OpenHarmony Bench
DeepSeek V3—
GLM-5.360.8%
Source
Not directly comparable
FrontierSWE v2
DeepSeek V3—
GLM-5.330.2%
Source
Not directly comparable
LiveCodeBench (Vals)
DeepSeek V3—
GLM-5.380.5%
Source
Not directly comparable
SWE-bench (Vals)
DeepSeek V3—
GLM-5.395.4%
Source
Not directly comparable

Knowledge

GPQA
DeepSeek V359.1%
Source
GLM-5.3—
Not directly comparable
MMLU-Pro
DeepSeek V375.9%
Source
GLM-5.3—
Not directly comparable
GPQA Diamond (Vals)
DeepSeek V3—
GLM-5.388.1%
Source
Not directly comparable
MMLU-Pro (Vals)
DeepSeek V3—
GLM-5.386.8%
Source
Not directly comparable

Instruction following

IFEval
DeepSeek V386.1%
Source
GLM-5.3—
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
DeepSeek V31.724%
Source
GLM-5.3—
Not directly comparable

30 public results · 0 shared

Related comparisons

Compare API pricing Read the methodology Open the model selector

Watch DeepSeek V3 vs GLM-5.3

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.

Last updated September 28, 2026

DeepSeek V3 vs GLM-5.3

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, DeepSeek V3 or GLM-5.3?

Which is better for coding, DeepSeek V3 or GLM-5.3?

Which is better for agentic tasks, DeepSeek V3 or GLM-5.3?

Which costs less, DeepSeek V3 or GLM-5.3?

Which has the larger context window, DeepSeek V3 or GLM-5.3?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Knowledge

Instruction following

Math

Related comparisons

Watch DeepSeek V3 vs GLM-5.3