Llama 4 Scout vs GLM 5.2

Meta

55#165

Zhipu AI

79#68

Signal-by-Signal Comparison

Signal	Llama 4 Scout	Delta	GLM 5.2
Capabilities	67	--	67
Benchmarks	52	-29	81
Pricing	100	+2	98
Context window size	97	+2	96
Recency	46	-54	100
Output Capacity	70	-15	85
Overall Result	2 wins	of 6	3 wins

GLM 5.2 wins 3 of 6 signals

Score History

Score History (24 data points)

Llama 4 ScoutGLM 5.2

Llama 4 Scout

54.7

current score

Leader

GLM 5.2

right now

GLM 5.2

78.5

current score

LMMarketCap.com

Interactive Price Comparison

Quick presets

Monthly API calls

100Kcalls/month

Avg. input tokens/call

1,000tokens (~1,333 chars)

Avg. output tokens/call

500tokens (~667 chars)

Llama 4 Scout

Meta

Best Value

Per request$0.000250

Daily$0.83

Monthly$25.00

Annual$300.00

GLM 5.2

Zhipu AI

Per request$0.001890

Daily$6.30

Monthly$189.00

Annual$2268.00

Llama 4 Scout saves you $164.00/month

That's $1968.00/year compared to GLM 5.2 at your current usage level of 100K calls/month.

87% cheaper

Choose Llama 4 Scout for cost optimization

Llama 4 Scout pricing:

Input:$0.10/M tokens

Output:$0.30/M tokens

GLM 5.2 pricing:

Input:$0.73/M tokens

Output:$2.31/M tokens

Llama 4 Scout

Meta

Composite Score

Winner

GLM 5.2

Zhipu AI

Composite Score

Signal-by-Signal Comparison

Metric	Llama 4 Scout	GLM 5.2	Winner
Overall Score	55	79	GLM 5.2
Rank	#165	#68	GLM 5.2
Quality Rank	#165	#68	GLM 5.2
Adoption Rank	#165	#68	GLM 5.2
Parameters	--	--	--
Context Window	1311K	1049K	Llama 4 Scout
Pricing	$0.10/$0.30/M	$0.73/$2.31/M	--
Signal Scores
Capabilities	67	67	Llama 4 Scout
Benchmarks	52	81	GLM 5.2
Pricing	100	98	Llama 4 Scout
Context window size	97	96	Llama 4 Scout
Recency	46	100	GLM 5.2
Output Capacity	70	85	GLM 5.2

Benchmark Head-to-Head(9 benchmarks)

Llama 4: 0GLM 5.2: 1

Llama 4

GLM 5.2

Normalized 0-100%

MMLU

79.6%-

MMLU-Pro

74.3%86%

GPQA Diamond

57.2%-

MATH-500

50.3%-

HumanEval

74.1%-

SWE-bench Verified

9.1%-

BBH

76%-

Arena Elo

-1457

BigCodeBench

16.9%-

Benchmark Interpretation

Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.

Llama 4 ScoutCompetitive

Scores 55/100 (rank #165), placing it in the top 43% of all 290 models tracked.

Raw Quality0/100

Cost Efficiency0/100

Speed0/100

GLM 5.2Strong Performer

Scores 79/100 (rank #68), placing it in the top 77% of all 290 models tracked.

Raw Quality0/100

Cost Efficiency0/100

Speed0/100

GLM 5.2 has a 24-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.

When to Use Each Model

Choose Llama 4 Scout when you need:

High-volume production workloads where API costs must be minimized
Multimodal workflows that require image understanding
Self-hosted deployments where you need full control over the model

Choose GLM 5.2 when you need:

Step-by-step reasoning and chain-of-thought problem solving
Self-hosted deployments where you need full control over the model

Cost-Performance Analysis

Llama 4 ScoutBest Value

Input cost$0.10/M tokens

Output cost$0.30/M tokens

Cost per quality point$0.007

Est. monthly (1M tokens/day)$6.00

GLM 5.2

Input cost$0.73/M tokens

Output cost$2.31/M tokens

Cost per quality point$0.039

Est. monthly (1M tokens/day)$45.67

Llama 4 Scout offers 87% better value per quality point. At 1M tokens/day, you'd spend $6.00/month with Llama 4 Scout vs $45.67/month with GLM 5.2 - a $39.67 monthly difference.

Latency & Speed

Llama 4 ScoutFaster

Speed score0/100

GLM 5.2

Speed score0/100

Both models have comparable response speeds. For most applications, the latency difference is negligible.

When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.

Example Use Cases

Code generation & review

Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring

Llama 4 Scout

Customer support chatbot

Suitable for user-facing chat with competitive response times. Llama 4 Scout also offers lower per-token costs for high-volume support

Llama 4 Scout

Long document analysis

Larger context window (1311K tokens) can process longer documents, contracts, and research papers in a single pass

Llama 4 Scout

Batch data extraction

Lower output pricing ($0.30/M) reduces costs when processing thousands of records daily

Llama 4 Scout

Creative writing & content

Higher overall composite score (79/100) correlates with better nuance, coherence, and style in long-form content

GLM 5.2

Image understanding & OCR

Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly

Llama 4 Scout

Which Should You Choose?

Our recommendation:

GLM 5.2

GLM 5.2 clearly outperforms Llama 4 Scout with a significant 23.799999999999997-point lead. For most general use cases, GLM 5.2 is the stronger choice. However, Llama 4 Scout may still excel in niche scenarios.

By Use Case

Best for Quality

Llama 4 Scout

Marginally better benchmark scores; both are excellent

Best for Cost

Llama 4 Scout

87% lower pricing; better value at scale

Best for Reliability

Llama 4 Scout

Higher uptime and faster response speeds

Best for Prototyping

Llama 4 Scout

Stronger community support and better developer experience

Best for Production

Llama 4 Scout

Wider enterprise adoption and proven at scale

Llama 4 Scout

by Meta

Choose for Quality - Marginally better benchmark scores; both are excellent
Choose for Cost - 87% lower pricing; better value at scale
Choose for Reliability - Higher uptime and faster response speeds
Choose for Prototyping - Stronger community support and better developer experience
Choose for Production - Wider enterprise adoption and proven at scale