Pairwise benchmark snapshot · Aug 16, 2026

DeepSeek R1 vs GLM-5.3 benchmark

Name: DeepSeek R1 vs GLM-5.3 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares DeepSeek R1 (DeepSeek) and GLM-5.3 (Zhipu) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, GLM-5.3 is ahead by 200 points (1,558 vs 1,358; seed-bootstrap (Aug 16, 2026)). On SWE-bench coding, GLM-5.3 resolves 76.4% versus 65.2% (seed-bootstrap (Aug 16, 2026)). GLM-5.3 streams faster (90 tok/s) while GLM-5.3 is the cheaper output token ($1.8/1M). GLM-5.3 has the larger context window (200k tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

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DeepSeek

DeepSeek R1

Open-weights reasoning model trained with large-scale RL.

Elo 1,358$2.5/1M out

Zhipu

GLM-5.3

Z.ai Aug 14 2026 post-train of the GLM-5.2 744B base. Coding-plan live; open weights promised after a two-week safety review (z.ai/blog/glm-5.3).

Elo 1,558$1.8/1M out

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Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek R1 (0)vsGLM-5.3 (10)

DeepSeek R1: 0W (0%)Overall: GLM-5.3GLM-5.3: 10W (100%)

← DeepSeek R1GLM-5.3 →

GLM-5.3 (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,358vs1,558

Coding Elo

—vs1,586

SWE-bench

65.2%vs76.4%

LiveBench

59%vs70.8%

GPQA Diamond

79.8%vs83.2%

DeepSeek R1: 0WGLM-5.3: 5W

GLM-5.3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

62 tok/svs90 tok/s

Time to first token

420 msvs255 ms

DeepSeek R1: 0WGLM-5.3: 2W

GLM-5.3 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.5/1Mvs$1.8/1M

Input price

$0.7/1Mvs$0.5/1M

Context window

64kvs200k

DeepSeek R1: 0WGLM-5.3: 3W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageGLM-5.3 6/6

Preference Elo

GLM-5.3

200 pts advantage

Throughput Speed

GLM-5.3

28 tok/s faster

Price Efficiency

GLM-5.3

$0.70/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,358

EloHigh is better

1,558+15%

Code Elo

High is better

—

Code EloHigh is better

1,586

LiveBench

High is better

59%

LiveBenchHigh is better

70.8%+20%

SWE-bench

High is better

65.2%

SWE-benchHigh is better

76.4%+17%

GPQA

High is better

79.8%

GPQAHigh is better

83.2%+4%

TTFT

Low is better

420 ms

TTFTLow is better

255 ms+65%

Speed

High is better

62 tok/s

SpeedHigh is better

90 tok/s+45%

In $

Low is better

$0.7/1M

In $Low is better

$0.5/1M+40%

Out $

Low is better

$2.5/1M

Out $Low is better

$1.8/1M+39%

Context

High is better

64k

ContextHigh is better

200k+213%

Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 28% with GLM-5.3

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

DeepSeek R1$62.00 / mo

In: $24.50Out: $37.50

GLM-5.3$44.50 / mo

In: $17.50Out: $27.00

Estimated Cost Delta

GLM-5.3 is estimated to save $17.50/month ($210/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
GLM-5.3:Pick GLM-5.3 when repo-level coding accuracy is the constraint.
2
GLM-5.3:Pick GLM-5.3 when time-to-first-token matters more than peak Elo.
3
GLM-5.3:Pick GLM-5.3 as the default general assistant.

Recommended Workload Routing

Repo / coding agentsGLM-5.3

Higher SWE-bench (76.4%).

High-volume chatGLM-5.3

Lower output list price ($1.8/1M).

Voice / low-latency UIGLM-5.3

Lower TTFT (255 ms).

Long-document RAGGLM-5.3

Larger window (200k).

Screenshots / visionGLM-5.3

GLM-5.3 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GLM-5.3	Higher SWE-bench (76.4%).
High-volume chat	GLM-5.3	Lower output list price ($1.8/1M).
Voice / low-latency UI	GLM-5.3	Lower TTFT (255 ms).
Long-document RAG	GLM-5.3	Larger window (200k).
Screenshots / vision	GLM-5.3	GLM-5.3 is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchAug 14, 2026

GLM-5.3 (Aug 14): Z.ai post-train of the 5.2 744B base

Coding-plan live; open weights promised after a two-week safety review. Newest Zhipu row.

launchJun 18, 2026

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

Strong Chinese/English coding. 5.3 is the new row; 5.2 stays so the delta is visible.

launchJan 20, 2025

DeepSeek R1 stays as the open-weights RL baseline

Jan 2025 reasoning model. Still searched. V4 Pro is the newer DeepSeek buy.

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LIVE

DeepSeek

DeepSeek R1

Elo 1,358

LiveBench59%

SWE-bench65.2%

Speed62 tok/s

Output cost$2.5/1M

Zhipu

GLM-5.3

Elo 1,558

LiveBench70.8%

SWE-bench76.4%

Speed90 tok/s

Output cost$1.8/1M

Frequently Asked Questions

Which is better overall, DeepSeek R1 or GLM-5.3?: GLM-5.3 has the higher preference Elo in our latest snapshot (1,558). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, DeepSeek R1 or GLM-5.3?: GLM-5.3 leads SWE-bench at 76.4% vs 65.2%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: GLM-5.3 output tokens are $1.8/1M versus $2.5/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek R1 or GLM-5.3?: GLM-5.3 has the lower time-to-first-token (255 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: GLM-5.3 accepts 200k tokens versus 64k.
Can I self-host either model?: DeepSeek R1 is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. DeepSeek R1 and GLM-5.3 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All DeepSeek R1 matchups →|All GLM-5.3 matchups →

DeepSeek R1 vs DeepSeek V3 (previous deepseek)DeepSeek R1 vs DeepSeek V4 Pro (next deepseek)GLM-5.3 vs GLM-5.2 (previous glm)DeepSeek V4 Flash vs GLM-5.3 DeepSeek V4 Pro vs GLM-5.3 DeepSeek Coder V2 vs DeepSeek R1

DeepSeek R1 vs GLM-5.3 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek R1 (0)vsGLM-5.3 (10)

DeepSeek R1: 0W (0%)Overall: GLM-5.3GLM-5.3: 10W (100%)

← DeepSeek R1GLM-5.3 →

GLM-5.3 (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,358vs1,558

Coding Elo

—vs1,586

SWE-bench

65.2%vs76.4%

LiveBench

59%vs70.8%

GPQA Diamond

79.8%vs83.2%

DeepSeek R1: 0WGLM-5.3: 5W

GLM-5.3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

62 tok/svs90 tok/s

Time to first token

420 msvs255 ms

DeepSeek R1: 0WGLM-5.3: 2W

GLM-5.3 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.5/1Mvs$1.8/1M

Input price

$0.7/1Mvs$0.5/1M

Context window

64kvs200k

DeepSeek R1: 0WGLM-5.3: 3W

Recommended Workload Routing

Repo / coding agentsGLM-5.3

Higher SWE-bench (76.4%).

High-volume chatGLM-5.3

Lower output list price ($1.8/1M).

Voice / low-latency UIGLM-5.3

Lower TTFT (255 ms).

Long-document RAGGLM-5.3

Larger window (200k).

Screenshots / visionGLM-5.3

GLM-5.3 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GLM-5.3	Higher SWE-bench (76.4%).
High-volume chat	GLM-5.3	Lower output list price ($1.8/1M).
Voice / low-latency UI	GLM-5.3	Lower TTFT (255 ms).
Long-document RAG	GLM-5.3	Larger window (200k).
Screenshots / vision	GLM-5.3	GLM-5.3 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, DeepSeek R1 or GLM-5.3?

GLM-5.3 has the higher preference Elo in our latest snapshot (1,558). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, DeepSeek R1 or GLM-5.3?

GLM-5.3 leads SWE-bench at 76.4% vs 65.2%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

GLM-5.3 output tokens are $1.8/1M versus $2.5/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek R1 or GLM-5.3?

GLM-5.3 has the lower time-to-first-token (255 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

GLM-5.3 accepts 200k tokens versus 64k.

Can I self-host either model?

DeepSeek R1 is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. DeepSeek R1 and GLM-5.3 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

GLM-5.3

GLM-5.3

GLM-5.3

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GLM-5.3 (Aug 14): Z.ai post-train of the 5.2 744B base

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

DeepSeek R1 stays as the open-weights RL baseline

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

DeepSeek R1

GLM-5.3

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

GLM-5.3

GLM-5.3

GLM-5.3

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GLM-5.3 (Aug 14): Z.ai post-train of the 5.2 744B base

GLM-5.2 remains the prior Zhipu flagship for upgrade pairs

DeepSeek R1 stays as the open-weights RL baseline

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

DeepSeek R1

GLM-5.3

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons