o1-pro vs GLM 4.6

OpenAI

74#137

Zhipu AI

74#135

Signal-by-Signal Comparison

Signal	o1-pro	Delta	GLM 4.6
Capabilities	83	+17	67
Benchmarks	77	+12	64
Pricing	5	-93	98
Context window size	84	0	84
Recency	40	-35	76
Output Capacity	83	-2	85
Overall Result	2 wins	of 6	4 wins

GLM 4.6 wins 4 of 6 signals

Score History

Score History (26 data points)

o1-proGLM 4.6

o1-pro

73.6

current score

Leader

Tied

right now

GLM 4.6

73.6

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)

o1-pro

OpenAI

Per request$0.450000

Daily$1500.00

Monthly$45000.00

Annual$540000.00

GLM 4.6

Zhipu AI

Best Value

Per request$0.001500

Daily$5.00

Monthly$150.00

Annual$1800.00

GLM 4.6 saves you $44850.00/month

That's $538200.00/year compared to o1-pro at your current usage level of 100K calls/month.

100% cheaper

Choose GLM 4.6 for cost optimization

o1-pro pricing:

Input:$150.00/M tokens

Output:$600.00/M tokens

GLM 4.6 pricing:

Input:$0.50/M tokens

Output:$2.00/M tokens

Tie

o1-pro

OpenAI

Composite Score

Tie

GLM 4.6

Zhipu AI

Composite Score

Signal-by-Signal Comparison

Metric	o1-pro	GLM 4.6	Winner
Overall Score	74	74	--
Rank	#137	#135	GLM 4.6
Quality Rank	#137	#135	GLM 4.6
Adoption Rank	#137	#135	GLM 4.6
Parameters	--	--	--
Context Window	200K	205K	GLM 4.6
Pricing	$150.00/$600.00/M	$0.50/$2.00/M	--
Signal Scores
Capabilities	83	67	o1-pro
Benchmarks	77	64	o1-pro
Pricing	5	98	GLM 4.6
Context window size	84	84	GLM 4.6
Recency	40	76	GLM 4.6
Output Capacity	83	85	GLM 4.6

Benchmark Head-to-Head(12 benchmarks)

o1-pro: 0GLM 4.6: 2

o1-pro

GLM 4.6

Normalized 0-100%

MMLU

91.8%-

MMLU-Pro

80.3%-

GPQA Diamond

78%-

MATH-500

96.4%-

HumanEval

92.4%-

SWE-bench Verified

48.9%55.4%

AIME 2024

83.3%-

IFEval

86.5%-

BBH

89%-

Arena Elo

13501425

HLE

8.12%-

BigCodeBench

32.4%-

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.

o1-proStrong Performer

Scores 74/100 (rank #137), placing it in the top 53% of all 290 models tracked.

Raw Quality0/100

Cost Efficiency0/100

Speed0/100

GLM 4.6Strong Performer

Scores 74/100 (rank #135), placing it in the top 54% of all 290 models tracked.

Raw Quality0/100

Cost Efficiency0/100

Speed0/100

With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.

When to Use Each Model

Choose o1-pro when you need:

Multimodal workflows that require image understanding
Step-by-step reasoning and chain-of-thought problem solving

Choose GLM 4.6 when you need:

High-volume production workloads where API costs must be minimized
Agentic applications using tool/function calling
Step-by-step reasoning and chain-of-thought problem solving
Self-hosted deployments where you need full control over the model

Cost-Performance Analysis

o1-pro

Input cost$150.00/M tokens

Output cost$600.00/M tokens

Cost per quality point$10.190

Est. monthly (1M tokens/day)$11250.00

GLM 4.6Best Value

Input cost$0.50/M tokens

Output cost$2.00/M tokens

Cost per quality point$0.034

Est. monthly (1M tokens/day)$37.50

GLM 4.6 offers 100% better value per quality point. At 1M tokens/day, you'd spend $37.50/month with GLM 4.6 vs $11250.00/month with o1-pro - a $11212.50 monthly difference.

Latency & Speed

o1-proFaster

Speed score0/100

GLM 4.6

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

o1-pro

Customer support chatbot

Suitable for user-facing chat with competitive response times. GLM 4.6 also offers lower per-token costs for high-volume support

o1-pro

Long document analysis

Larger context window (205K tokens) can process longer documents, contracts, and research papers in a single pass

GLM 4.6

Batch data extraction

Lower output pricing ($2.00/M) reduces costs when processing thousands of records daily

GLM 4.6

Creative writing & content

Higher overall composite score (74/100) correlates with better nuance, coherence, and style in long-form content

o1-pro

Image understanding & OCR

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

o1-pro

Which Should You Choose?

Our recommendation:

o1-pro

o1-pro and GLM 4.6 are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.

By Use Case

Best for Quality

o1-pro

Marginally better benchmark scores; both are excellent

Best for Cost

GLM 4.6

100% lower pricing; better value at scale

Best for Reliability

o1-pro

Higher uptime and faster response speeds

Best for Prototyping

o1-pro

Stronger community support and better developer experience

Best for Production

o1-pro

Wider enterprise adoption and proven at scale

o1-pro

Recommended

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