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GPT-4.1 vs GLM 5.2

GPT-4.1

OpenAI

68#127
vs
GLM 5.2

Zhipu AI

79#68
Signal-by-Signal Comparison
SignalGPT-4.1DeltaGLM 5.2
Capabilities
83
+17
67
Benchmarks
70
-10
81
Pricing
92
-6
98
Context window size
96
--
96
Recency
47
-53
100
Output Capacity
75
-10
85
Overall Result
1 wins
of 6
4 wins
GLM 5.2 wins 4 of 6 signals

Score History

Score History (24 data points)
GPT-4.1GLM 5.2
GPT-4.1

67.7

current score

Leader

GLM 5.2

right now

GLM 5.2

78.5

current score

LMMarketCap.com
Interactive Price Comparison
100Kcalls/month
1,000tokens (~1,333 chars)
500tokens (~667 chars)

GPT-4.1

OpenAI

Per request$0.006000
Daily$20.00
Monthly$600.00
Annual$7200.00

GLM 5.2

Zhipu AI

Best Value
Per request$0.001890
Daily$6.30
Monthly$189.00
Annual$2268.00

GLM 5.2 saves you $411.00/month

That's $4932.00/year compared to GPT-4.1 at your current usage level of 100K calls/month.

69% cheaper
Choose GLM 5.2 for cost optimization

GPT-4.1 pricing:
Input:$2.00/M tokens
Output:$8.00/M tokens
GLM 5.2 pricing:
Input:$0.73/M tokens
Output:$2.31/M tokens
GPT-4.1

OpenAI

68

Composite Score

Winner
GLM 5.2

Zhipu AI

79

Composite Score

Signal-by-Signal Comparison
MetricGPT-4.1GLM 5.2Winner
Overall Score
68
79
GLM 5.2
Rank#127#68
GLM 5.2
Quality Rank#127#68
GLM 5.2
Adoption Rank#127#68
GLM 5.2
Parameters------
Context Window1048K1049K
GLM 5.2
Pricing$2.00/$8.00/M$0.73/$2.31/M--
Signal Scores
Capabilities
83
67
GPT-4.1
Benchmarks
70
81
GLM 5.2
Pricing
92
98
GLM 5.2
Context window size
96
96
GPT-4.1
Recency
47
100
GLM 5.2
Output Capacity
75
85
GLM 5.2
Benchmark Head-to-Head(12 benchmarks)
GPT-4.1: 0GLM 5.2: 2
GPT-4.1
GLM 5.2
Normalized 0-100%
MMLU
89.2%-
MMLU-Pro
81.8%86%
GPQA Diamond
56%-
MATH-500
78.5%-
HumanEval
91.5%-
SWE-bench Verified
54.6%-
IFEval
88.2%-
BBH
84%-
Arena Elo
13001457
LiveBench
64.5%-
HLE
5.4%-
BigCodeBench
31.8%-
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.

GPT-4.1Competitive

Scores 68/100 (rank #127), placing it in the top 57% 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 11-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.

When to Use Each Model

Choose GPT-4.1 when you need:

  • Multimodal workflows that require image understanding

Choose GLM 5.2 when you need:

  • High-volume production workloads where API costs must be minimized
  • Step-by-step reasoning and chain-of-thought problem solving
  • Self-hosted deployments where you need full control over the model
Cost-Performance Analysis
GPT-4.1
Input cost$2.00/M tokens
Output cost$8.00/M tokens
Cost per quality point$0.148
Est. monthly (1M tokens/day)$150.00
GLM 5.2Best Value
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

GLM 5.2 offers 70% better value per quality point. At 1M tokens/day, you'd spend $45.67/month with GLM 5.2 vs $150.00/month with GPT-4.1 - a $104.33 monthly difference.

Latency & Speed
GPT-4.1Faster
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

GPT-4.1

Customer support chatbot

Suitable for user-facing chat with competitive response times. GLM 5.2 also offers lower per-token costs for high-volume support

GPT-4.1

Long document analysis

Larger context window (1049K tokens) can process longer documents, contracts, and research papers in a single pass

GLM 5.2

Batch data extraction

Lower output pricing ($2.31/M) reduces costs when processing thousands of records daily

GLM 5.2

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

GPT-4.1
Which Should You Choose?
Our recommendation:
GLM 5.2

GLM 5.2 clearly outperforms GPT-4.1 with a significant 10.799999999999997-point lead. For most general use cases, GLM 5.2 is the stronger choice. However, GPT-4.1 may still excel in niche scenarios.

by OpenAI

  • Choose for Quality - Marginally better benchmark scores; both are excellent
  • 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
GLM 5.2
Recommended

by Zhipu AI

  • Choose for Cost - 70% lower pricing; better value at scale
Capability Comparison
CapabilityGPT-4.1GLM 5.2
Vision (Image Input)differs
Function Calling
Streaming
JSON Mode
Reasoningdiffers
Web Searchdiffers
Image Output
Monthly Cost Calculator
1,000tokens (600 in / 400 out)
100requests/day (3,000/month)

GPT-4.1

OpenAI

$13.20
estimated monthly cost

GLM 5.2

Zhipu AI

Best Value
$4.09
estimated monthly cost

GLM 5.2 saves you $9.11/month

That's 69% cheaper than GPT-4.1 at 1,000 tokens/request and 100 requests/day.

Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.

Parameters & Context
ParameterGPT-4.1GLM 5.2
Context Window1.0M1.0M
Max Output Tokens32,768131,072
Open SourceNoYes
CreatedApr 14, 2025Jun 16, 2026
Last updated: 57m ago

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