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

Z.ai: GLM 5 Turbo vs Llama 3.1 405B benchmark

Name: Z.ai: GLM 5 Turbo vs Llama 3.1 405B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Z.ai: GLM 5 Turbo (Zhipu) and Llama 3.1 405B (Meta) using the latest snapshots we have as of Aug 17, 2026. Llama 3.1 405B wins on output price at $3.5/1M. Z.ai: GLM 5 Turbo has the larger context window (203k 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 Turbo

Auto-discovered from OpenRouter (z-ai/glm-5-turbo). Preview until a second source matches.

$4/1M 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 Turbo (2)vsLlama 3.1 405B (8)

Z.ai: GLM 5 Turbo: 2W (20%)Overall: Llama 3.1 405BLlama 3.1 405B: 8W (80%)

← Z.ai: GLM 5 TurboLlama 3.1 405B →

Llama 3.1 405B (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,370

Coding Elo

—vs1,385

SWE-bench

—vs58.4%

LiveBench

—vs61.2%

GPQA Diamond

—vs75.2%

Z.ai: GLM 5 Turbo: 0WLlama 3.1 405B: 5W

Llama 3.1 405B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs42 tok/s

Time to first token

—vs360 ms

Z.ai: GLM 5 Turbo: 0WLlama 3.1 405B: 2W

Z.ai: GLM 5 Turbo (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4/1Mvs$3.5/1M

Input price

$1.2/1Mvs$1.25/1M

Context window

203kvs128k

Z.ai: GLM 5 Turbo: 2WLlama 3.1 405B: 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 AdvantageLlama 3.1 405B 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

Llama 3.1 405B

$0.50/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,370

Code Elo

High is better

—

Code EloHigh is better

1,385

LiveBench

High is better

—

LiveBenchHigh is better

61.2%

SWE-bench

High is better

—

SWE-benchHigh is better

58.4%

GPQA

High is better

—

GPQAHigh is better

75.2%

TTFT

Low is better

—

TTFTLow is better

360 ms

Speed

High is better

—

SpeedHigh is better

42 tok/s

In $

Low is better

+4%$1.2/1M

In $Low is better

$1.25/1M

Out $

Low is better

$4/1M

Out $Low is better

$3.5/1M+14%

Context

High is better

+58%203k

ContextHigh is better

128k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 6% with Llama 3.1 405B

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 Turbo$102.00 / mo

In: $42.00Out: $60.00

Llama 3.1 405B$96.25 / mo

In: $43.75Out: $52.50

Estimated Cost Delta

Llama 3.1 405B is estimated to save $5.75/month ($69/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Llama 3.1 405B:Pick Llama 3.1 405B when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatLlama 3.1 405B

Lower output list price ($3.5/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGZ.ai: GLM 5 Turbo

Larger window (203k).

Screenshots / visionLlama 3.1 405B

Llama 3.1 405B 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	Llama 3.1 405B	Lower output list price ($3.5/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Z.ai: GLM 5 Turbo	Larger window (203k).
Screenshots / vision	Llama 3.1 405B	Llama 3.1 405B is the side marked multimodal in the catalog.

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LIVE

Zhipu

Z.ai: GLM 5 Turbo

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$4/1M

Llama 3.1 405B

Elo 1,370

LiveBench61.2%

SWE-bench58.4%

Speed42 tok/s

Output cost$3.5/1M

Frequently Asked Questions

Which is better overall, Z.ai: GLM 5 Turbo or Llama 3.1 405B?: 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 Turbo or Llama 3.1 405B?: 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?: Llama 3.1 405B output tokens are $3.5/1M versus $4/1M. Input prices and retry rates still move the real bill.
Which is faster, Z.ai: GLM 5 Turbo or Llama 3.1 405B?: We do not have TTFT for both models.
Which has the larger context window?: Z.ai: GLM 5 Turbo accepts 203k tokens versus 128k.
Can I self-host either model?: Llama 3.1 405B 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 Turbo and Llama 3.1 405B 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 Turbo matchups →|All Llama 3.1 405B matchups →

Llama 3.1 405B vs Llama 3.1 70B (next llama)Llama 4 Maverick vs Llama 3.1 405B GLM-5.2 vs Llama 3.1 405B GLM-5.3 vs Llama 3.1 405B Llama 3.3 70B vs Llama 3.1 405B Z.ai: GLM 5V Turbo vs Llama 3.1 405B

Z.ai: GLM 5 Turbo vs Llama 3.1 405B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Z.ai: GLM 5 Turbo (2)vsLlama 3.1 405B (8)

Z.ai: GLM 5 Turbo: 2W (20%)Overall: Llama 3.1 405BLlama 3.1 405B: 8W (80%)

← Z.ai: GLM 5 TurboLlama 3.1 405B →

Llama 3.1 405B (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,370

Coding Elo

—vs1,385

SWE-bench

—vs58.4%

LiveBench

—vs61.2%

GPQA Diamond

—vs75.2%

Z.ai: GLM 5 Turbo: 0WLlama 3.1 405B: 5W

Llama 3.1 405B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs42 tok/s

Time to first token

—vs360 ms

Z.ai: GLM 5 Turbo: 0WLlama 3.1 405B: 2W

Z.ai: GLM 5 Turbo (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4/1Mvs$3.5/1M

Input price

$1.2/1Mvs$1.25/1M

Context window

203kvs128k

Z.ai: GLM 5 Turbo: 2WLlama 3.1 405B: 1W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatLlama 3.1 405B

Lower output list price ($3.5/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGZ.ai: GLM 5 Turbo

Larger window (203k).

Screenshots / visionLlama 3.1 405B

Llama 3.1 405B 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	Llama 3.1 405B	Lower output list price ($3.5/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Z.ai: GLM 5 Turbo	Larger window (203k).
Screenshots / vision	Llama 3.1 405B	Llama 3.1 405B is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Z.ai: GLM 5 Turbo or Llama 3.1 405B?

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 Turbo or Llama 3.1 405B?

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?

Llama 3.1 405B output tokens are $3.5/1M versus $4/1M. Input prices and retry rates still move the real bill.

Which is faster, Z.ai: GLM 5 Turbo or Llama 3.1 405B?

We do not have TTFT for both models.

Which has the larger context window?

Z.ai: GLM 5 Turbo accepts 203k tokens versus 128k.

Can I self-host either model?

Llama 3.1 405B 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 Turbo and Llama 3.1 405B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.