Pairwise benchmark snapshot · Aug 16, 2026

GLM-5.3 vs Qwen3.8 27B benchmark

Name: GLM-5.3 vs Qwen3.8 27B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares GLM-5.3 (Zhipu) and Qwen3.8 27B (Alibaba) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, GLM-5.3 is ahead by 160 points (1,558 vs 1,398; seed-bootstrap (Aug 16, 2026)). On SWE-bench coding, GLM-5.3 resolves 76.4% versus 58.8% (seed-bootstrap (Aug 16, 2026)). Qwen3.8 27B streams faster (152 tok/s) while GLM-5.3 is the cheaper output token ($1.8/1M). Qwen3.8 27B has the larger context window (262k 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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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

Alibaba

Qwen3.8 27B

Alibaba open-weight 27B drop dated Aug 14 2026. Dense enough to self-host; not a frontier MoE.

Elo 1,398$3.1999999999999997/1M 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.

GLM-5.3 (6)vsQwen3.8 27B (4)

GLM-5.3: 6W (60%)Overall: GLM-5.3Qwen3.8 27B: 4W (40%)

← GLM-5.3Qwen3.8 27B →

GLM-5.3 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,558vs1,398

Coding Elo

1,586vs1,422

SWE-bench

76.4%vs58.8%

LiveBench

70.8%vs60.6%

GPQA Diamond

83.2%vs73.4%

GLM-5.3: 5WQwen3.8 27B: 0W

Qwen3.8 27B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

90 tok/svs152 tok/s

Time to first token

255 msvs140 ms

GLM-5.3: 0WQwen3.8 27B: 2W

Qwen3.8 27B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.8/1Mvs$3.2/1M

Input price

$0.5/1Mvs$0.45/1M

Context window

200kvs262k

GLM-5.3: 1WQwen3.8 27B: 2W

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 4/6

Preference Elo

GLM-5.3

160 pts advantage

Throughput Speed

Qwen3.8 27B

62 tok/s faster

Price Efficiency

GLM-5.3

$1.40/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+11%1,558

EloHigh is better

1,398

Code Elo

High is better

+12%1,586

Code EloHigh is better

1,422

LiveBench

High is better

+17%70.8%

LiveBenchHigh is better

60.6%

SWE-bench

High is better

+30%76.4%

SWE-benchHigh is better

58.8%

GPQA

High is better

+13%83.2%

GPQAHigh is better

73.4%

TTFT

Low is better

255 ms

TTFTLow is better

140 ms+82%

Speed

High is better

90 tok/s

SpeedHigh is better

152 tok/s+69%

In $

Low is better

$0.5/1M

In $Low is better

$0.45/1M+11%

Out $

Low is better

+78%$1.8/1M

Out $Low is better

$3.2/1M

Context

High is better

200k

ContextHigh is better

262k+31%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 30% 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)

GLM-5.3$44.50 / mo

In: $17.50Out: $27.00

Qwen3.8 27B$63.75 / mo

In: $15.75Out: $48.00

Estimated Cost Delta

GLM-5.3 is estimated to save $19.25/month ($231/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
Qwen3.8 27B:Pick Qwen3.8 27B 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 UIQwen3.8 27B

Lower TTFT (140 ms).

Long-document RAGQwen3.8 27B

Larger window (262k).

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	Qwen3.8 27B	Lower TTFT (140 ms).
Long-document RAG	Qwen3.8 27B	Larger window (262k).
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

Qwen3.8 27B (Aug 14): dense enough to self-host, not a frontier MoE

Alibaba open-weight 27B drop. Local/open-weight lists should see it. It will not win frontier-agents.

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.

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LIVE

Zhipu

GLM-5.3

Elo 1,558

LiveBench70.8%

SWE-bench76.4%

Speed90 tok/s

Output cost$1.8/1M

Alibaba

Qwen3.8 27B

Elo 1,398

LiveBench60.6%

SWE-bench58.8%

Speed152 tok/s

Output cost$3.2/1M

Frequently Asked Questions

Which is better overall, GLM-5.3 or Qwen3.8 27B?: 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, GLM-5.3 or Qwen3.8 27B?: GLM-5.3 leads SWE-bench at 76.4% vs 58.8%. 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 $3.2/1M. Input prices and retry rates still move the real bill.
Which is faster, GLM-5.3 or Qwen3.8 27B?: Qwen3.8 27B has the lower time-to-first-token (140 ms vs 255 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Qwen3.8 27B accepts 262k tokens versus 200k.
Can I self-host either model?: Qwen3.8 27B 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. GLM-5.3 and Qwen3.8 27B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All GLM-5.3 matchups →|All Qwen3.8 27B matchups →

GLM-5.3 vs GLM-5.2 (previous glm)Qwen3.8 27B vs Qwen 3 Max (previous qwen)Qwen 3 Max vs GLM-5.3 Qwen QwQ 32B vs GLM-5.3 GPT-5 vs GLM-5.3 Claude Sonnet 5 vs GLM-5.3

GLM-5.3 vs Qwen3.8 27B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GLM-5.3 (6)vsQwen3.8 27B (4)

GLM-5.3: 6W (60%)Overall: GLM-5.3Qwen3.8 27B: 4W (40%)

← GLM-5.3Qwen3.8 27B →

GLM-5.3 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,558vs1,398

Coding Elo

1,586vs1,422

SWE-bench

76.4%vs58.8%

LiveBench

70.8%vs60.6%

GPQA Diamond

83.2%vs73.4%

GLM-5.3: 5WQwen3.8 27B: 0W

Qwen3.8 27B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

90 tok/svs152 tok/s

Time to first token

255 msvs140 ms

GLM-5.3: 0WQwen3.8 27B: 2W

Qwen3.8 27B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.8/1Mvs$3.2/1M

Input price

$0.5/1Mvs$0.45/1M

Context window

200kvs262k

GLM-5.3: 1WQwen3.8 27B: 2W

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 UIQwen3.8 27B

Lower TTFT (140 ms).

Long-document RAGQwen3.8 27B

Larger window (262k).

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	Qwen3.8 27B	Lower TTFT (140 ms).
Long-document RAG	Qwen3.8 27B	Larger window (262k).
Screenshots / vision	GLM-5.3	GLM-5.3 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, GLM-5.3 or Qwen3.8 27B?

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, GLM-5.3 or Qwen3.8 27B?

GLM-5.3 leads SWE-bench at 76.4% vs 58.8%. 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 $3.2/1M. Input prices and retry rates still move the real bill.

Which is faster, GLM-5.3 or Qwen3.8 27B?

Qwen3.8 27B has the lower time-to-first-token (140 ms vs 255 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Qwen3.8 27B accepts 262k tokens versus 200k.

Can I self-host either model?

Qwen3.8 27B 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. GLM-5.3 and Qwen3.8 27B 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

Qwen3.8 27B

GLM-5.3

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Qwen3.8 27B (Aug 14): dense enough to self-host, not a frontier MoE

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

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CompareLLM Matrix

GLM-5.3

Qwen3.8 27B

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

Qwen3.8 27B

GLM-5.3

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Qwen3.8 27B (Aug 14): dense enough to self-host, not a frontier MoE

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

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

GLM-5.3

Qwen3.8 27B

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons