Pairwise benchmark snapshot · Aug 16, 2026

DeepSeek R1 vs GLM-5.2 benchmark

Name: DeepSeek R1 vs GLM-5.2 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares DeepSeek R1 (DeepSeek) and GLM-5.2 (Zhipu) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, GLM-5.2 is ahead by 134 points (1,492 vs 1,358; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, GLM-5.2 resolves 69.3% versus 65.2% (seed-bootstrap (Aug 1, 2026)). GLM-5.2 streams faster (84 tok/s) while GLM-5.2 is the cheaper output token ($0.97/1M). GLM-5.2 has the larger context window (1M 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-5.2

Zhipu flagship. Strong Chinese/English coding and agents.

Elo 1,492$0.9680000000000001/1M out

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Model A

Model B

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 (1)vsGLM-5.2 (9)

DeepSeek R1: 1W (10%)Overall: GLM-5.2GLM-5.2: 9W (90%)

← DeepSeek R1GLM-5.2 →

GLM-5.2 (4/5)

Intelligence & Reasoning

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

Preference Elo

1,358vs1,492

Coding Elo

—vs1,504

SWE-bench

65.2%vs69.3%

LiveBench

59%vs65.2%

GPQA Diamond

79.8%vs79.5%

DeepSeek R1: 1WGLM-5.2: 4W

GLM-5.2 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

62 tok/svs84 tok/s

Time to first token

420 msvs270 ms

DeepSeek R1: 0WGLM-5.2: 2W

GLM-5.2 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.5/1Mvs$0.97/1M

Input price

$0.7/1Mvs$0.31/1M

Context window

64kvs1M

DeepSeek R1: 0WGLM-5.2: 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-5.2 6/6

Preference Elo

GLM-5.2

134 pts advantage

Throughput Speed

GLM-5.2

22 tok/s faster

Price Efficiency

GLM-5.2

$1.53/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,358

EloHigh is better

1,492+10%

Code Elo

High is better

—

Code EloHigh is better

1,504

LiveBench

High is better

59%

LiveBenchHigh is better

65.2%+11%

SWE-bench

High is better

65.2%

SWE-benchHigh is better

69.3%+6%

GPQA

High is better

79.8%

GPQAHigh is better

79.5%

TTFT

Low is better

420 ms

TTFTLow is better

270 ms+56%

Speed

High is better

62 tok/s

SpeedHigh is better

84 tok/s+35%

In $

Low is better

$0.7/1M

In $Low is better

$0.31/1M+127%

Out $

Low is better

$2.5/1M

Out $Low is better

$0.97/1M+158%

Context

High is better

64k

ContextHigh is better

1M+1538%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 59% with GLM-5.2

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-5.2$25.30 / mo

In: $10.78Out: $14.52

Estimated Cost Delta

GLM-5.2 is estimated to save $36.70/month ($440/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
GLM-5.2:Pick GLM-5.2 when repo-level coding accuracy is the constraint.
2
GLM-5.2:Pick GLM-5.2 when time-to-first-token matters more than peak Elo.
3
GLM-5.2:Pick GLM-5.2 for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsGLM-5.2

Higher SWE-bench (69.3%).

High-volume chatGLM-5.2

Lower output list price ($0.97/1M).

Voice / low-latency UIGLM-5.2

Lower TTFT (270 ms).

Long-document RAGGLM-5.2

Larger window (1M).

Screenshots / visionGLM-5.2

GLM-5.2 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GLM-5.2	Higher SWE-bench (69.3%).
High-volume chat	GLM-5.2	Lower output list price ($0.97/1M).
Voice / low-latency UI	GLM-5.2	Lower TTFT (270 ms).
Long-document RAG	GLM-5.2	Larger window (1M).
Screenshots / vision	GLM-5.2	GLM-5.2 is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

newsAug 16, 2026

GLM-5.2 vs GLM 5V Turbo Price and SWE-bench 2026

GLM-5.2 undercuts GLM 5V Turbo by 74.2% on input at $0.31/1M, with 1,492 Elo and 69.3% SWE-bench in dated 2026 snapshots.

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-5.2

Elo 1,492

LiveBench65.2%

SWE-bench69.3%

Speed84 tok/s

Output cost$0.97/1M

Frequently Asked Questions

Which is better overall, DeepSeek R1 or GLM-5.2?: GLM-5.2 has the higher preference Elo in our latest snapshot (1,492). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, DeepSeek R1 or GLM-5.2?: GLM-5.2 leads SWE-bench at 69.3% vs 65.2%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: GLM-5.2 output tokens are $0.97/1M versus $2.5/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek R1 or GLM-5.2?: GLM-5.2 has the lower time-to-first-token (270 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: GLM-5.2 accepts 1M tokens versus 64k.
Can I self-host either model?: Both DeepSeek R1 and GLM-5.2 are marked open-weights. Hosting cost is not in this table.
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-5.2 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-5.2 matchups →

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

DeepSeek R1 vs GLM-5.2 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek R1 (1)vsGLM-5.2 (9)

DeepSeek R1: 1W (10%)Overall: GLM-5.2GLM-5.2: 9W (90%)

← DeepSeek R1GLM-5.2 →

GLM-5.2 (4/5)

Intelligence & Reasoning

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

Preference Elo

1,358vs1,492

Coding Elo

—vs1,504

SWE-bench

65.2%vs69.3%

LiveBench

59%vs65.2%

GPQA Diamond

79.8%vs79.5%

DeepSeek R1: 1WGLM-5.2: 4W

GLM-5.2 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

62 tok/svs84 tok/s

Time to first token

420 msvs270 ms

DeepSeek R1: 0WGLM-5.2: 2W

GLM-5.2 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.5/1Mvs$0.97/1M

Input price

$0.7/1Mvs$0.31/1M

Context window

64kvs1M

DeepSeek R1: 0WGLM-5.2: 3W

Recommended Workload Routing

Repo / coding agentsGLM-5.2

Higher SWE-bench (69.3%).

High-volume chatGLM-5.2

Lower output list price ($0.97/1M).

Voice / low-latency UIGLM-5.2

Lower TTFT (270 ms).

Long-document RAGGLM-5.2

Larger window (1M).

Screenshots / visionGLM-5.2

GLM-5.2 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GLM-5.2	Higher SWE-bench (69.3%).
High-volume chat	GLM-5.2	Lower output list price ($0.97/1M).
Voice / low-latency UI	GLM-5.2	Lower TTFT (270 ms).
Long-document RAG	GLM-5.2	Larger window (1M).
Screenshots / vision	GLM-5.2	GLM-5.2 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, DeepSeek R1 or GLM-5.2?

GLM-5.2 has the higher preference Elo in our latest snapshot (1,492). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, DeepSeek R1 or GLM-5.2?

GLM-5.2 leads SWE-bench at 69.3% vs 65.2%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

GLM-5.2 output tokens are $0.97/1M versus $2.5/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek R1 or GLM-5.2?

GLM-5.2 has the lower time-to-first-token (270 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

GLM-5.2 accepts 1M tokens versus 64k.

Can I self-host either model?

Both DeepSeek R1 and GLM-5.2 are marked open-weights. Hosting cost is not in this table.

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-5.2 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-5.2

GLM-5.2

GLM-5.2

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GLM-5.2 vs GLM 5V Turbo Price and SWE-bench 2026

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-5.2

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-5.2

GLM-5.2

GLM-5.2

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GLM-5.2 vs GLM 5V Turbo Price and SWE-bench 2026

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-5.2

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons