Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)

Z.ai: GLM 5.2 (batch) vs Qwen3.8 27B benchmark

Name: Z.ai: GLM 5.2 (batch) vs Qwen3.8 27B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Z.ai: GLM 5.2 (batch) (Zhipu) and Qwen3.8 27B (Alibaba) using the latest snapshots we have as of Aug 17, 2026. Z.ai: GLM 5.2 (batch) wins on output price at $2.2/1M. Z.ai: GLM 5.2 (batch) has the larger context window (512k 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

Z.ai: GLM 5.2 (batch)

Auto-discovered from OpenRouter (z-ai/glm-5.2:batch). Preview until a second source matches.

$2.2/1M 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.

Z.ai: GLM 5.2 (batch) (2)vsQwen3.8 27B (8)

Z.ai: GLM 5.2 (batch): 2W (20%)Overall: Qwen3.8 27BQwen3.8 27B: 8W (80%)

← Z.ai: GLM 5.2 (batch)Qwen3.8 27B →

Qwen3.8 27B (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,398

Coding Elo

—vs1,422

SWE-bench

—vs58.8%

LiveBench

—vs60.6%

GPQA Diamond

—vs73.4%

Z.ai: GLM 5.2 (batch): 0WQwen3.8 27B: 5W

Qwen3.8 27B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs152 tok/s

Time to first token

—vs140 ms

Z.ai: GLM 5.2 (batch): 0WQwen3.8 27B: 2W

Z.ai: GLM 5.2 (batch) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.2/1Mvs$3.2/1M

Input price

$0.7/1Mvs$0.45/1M

Context window

512kvs262k

Z.ai: GLM 5.2 (batch): 2WQwen3.8 27B: 1W

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 AdvantageQwen3.8 27B 4/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

Z.ai: GLM 5.2 (batch)

$1.00/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,398

Code Elo

High is better

—

Code EloHigh is better

1,422

LiveBench

High is better

—

LiveBenchHigh is better

60.6%

SWE-bench

High is better

—

SWE-benchHigh is better

58.8%

GPQA

High is better

—

GPQAHigh is better

73.4%

TTFT

Low is better

—

TTFTLow is better

140 ms

Speed

High is better

—

SpeedHigh is better

152 tok/s

In $

Low is better

$0.7/1M

In $Low is better

$0.45/1M+56%

Out $

Low is better

+45%$2.2/1M

Out $Low is better

$3.2/1M

Context

High is better

+95%512k

ContextHigh is better

262k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 10% with Z.ai: GLM 5.2 (batch)

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Z.ai: GLM 5.2 (batch)$57.50 / mo

In: $24.50Out: $33.00

Qwen3.8 27B$63.75 / mo

In: $15.75Out: $48.00

Estimated Cost Delta

Z.ai: GLM 5.2 (batch) is estimated to save $6.25/month ($75/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Z.ai: GLM 5.2 (batch):Pick Z.ai: GLM 5.2 (batch) when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatZ.ai: GLM 5.2 (batch)

Lower output list price ($2.2/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGZ.ai: GLM 5.2 (batch)

Larger window (512k).

Screenshots / visionQwen3.8 27B

Qwen3.8 27B is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	Z.ai: GLM 5.2 (batch)	Lower output list price ($2.2/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Z.ai: GLM 5.2 (batch)	Larger window (512k).
Screenshots / vision	Qwen3.8 27B	Qwen3.8 27B 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.

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LIVE

Zhipu

Z.ai: GLM 5.2 (batch)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$2.2/1M

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, Z.ai: GLM 5.2 (batch) or Qwen3.8 27B?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, Z.ai: GLM 5.2 (batch) or Qwen3.8 27B?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: Z.ai: GLM 5.2 (batch) output tokens are $2.2/1M versus $3.2/1M. Input prices and retry rates still move the real bill.
Which is faster, Z.ai: GLM 5.2 (batch) or Qwen3.8 27B?: We do not have TTFT for both models.
Which has the larger context window?: Z.ai: GLM 5.2 (batch) accepts 512k tokens versus 262k.
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. Z.ai: GLM 5.2 (batch) 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 Z.ai: GLM 5.2 (batch) matchups →|All Qwen3.8 27B matchups →

Qwen 3 Max vs Qwen3.8 27B Qwen QwQ 32B vs Qwen3.8 27B GLM-5.2 vs Qwen3.8 27B GLM-5.3 vs Qwen3.8 27B Qwen3 235B vs Qwen3.8 27B Qwen 2.5 Plus vs Qwen3.8 27B

Z.ai: GLM 5.2 (batch) vs Qwen3.8 27B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Z.ai: GLM 5.2 (batch) (2)vsQwen3.8 27B (8)

Z.ai: GLM 5.2 (batch): 2W (20%)Overall: Qwen3.8 27BQwen3.8 27B: 8W (80%)

← Z.ai: GLM 5.2 (batch)Qwen3.8 27B →

Qwen3.8 27B (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,398

Coding Elo

—vs1,422

SWE-bench

—vs58.8%

LiveBench

—vs60.6%

GPQA Diamond

—vs73.4%

Z.ai: GLM 5.2 (batch): 0WQwen3.8 27B: 5W

Qwen3.8 27B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs152 tok/s

Time to first token

—vs140 ms

Z.ai: GLM 5.2 (batch): 0WQwen3.8 27B: 2W

Z.ai: GLM 5.2 (batch) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.2/1Mvs$3.2/1M

Input price

$0.7/1Mvs$0.45/1M

Context window

512kvs262k

Z.ai: GLM 5.2 (batch): 2WQwen3.8 27B: 1W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatZ.ai: GLM 5.2 (batch)

Lower output list price ($2.2/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGZ.ai: GLM 5.2 (batch)

Larger window (512k).

Screenshots / visionQwen3.8 27B

Qwen3.8 27B is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	Z.ai: GLM 5.2 (batch)	Lower output list price ($2.2/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Z.ai: GLM 5.2 (batch)	Larger window (512k).
Screenshots / vision	Qwen3.8 27B	Qwen3.8 27B is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Z.ai: GLM 5.2 (batch) or Qwen3.8 27B?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, Z.ai: GLM 5.2 (batch) or Qwen3.8 27B?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

Z.ai: GLM 5.2 (batch) output tokens are $2.2/1M versus $3.2/1M. Input prices and retry rates still move the real bill.

Which is faster, Z.ai: GLM 5.2 (batch) or Qwen3.8 27B?

We do not have TTFT for both models.

Which has the larger context window?

Z.ai: GLM 5.2 (batch) accepts 512k tokens versus 262k.

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. Z.ai: GLM 5.2 (batch) 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

No shared data

No shared data

Z.ai: GLM 5.2 (batch)

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

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Z.ai: GLM 5.2 (batch)

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

No shared data

No shared data

Z.ai: GLM 5.2 (batch)

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

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Z.ai: GLM 5.2 (batch)

Qwen3.8 27B

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons