o1-pro vs Qwen3.8 27B

OpenAI

74#113

Alibaba

73#114

Signal-by-Signal Comparison

Signal	o1-pro	Delta	Qwen3.8 27B
Capabilities	83	--	83
Benchmarks	77	+5	72
Pricing	5	-92	98
Context window size	84	-11	95
Recency	37	-63	100
Output Capacity	80	-2	82
Overall Result	1 wins	of 6	4 wins

Qwen3.8 27B wins 4 of 6 signals

Score History

Score History (28 data points)

o1-proQwen3.8 27B

o1-pro

73.6

current score

Leader

o1-pro

right now

Qwen3.8 27B

73.2

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

Qwen3.8 27B

Alibaba

Best Value

Per request$0.001700

Daily$5.67

Monthly$170.00

Annual$2040.00

Qwen3.8 27B saves you $44830.00/month

That's $537960.00/year compared to o1-pro at your current usage level of 100K calls/month.

100% cheaper

Choose Qwen3.8 27B for cost optimization

o1-pro pricing:

Input:$150.00/M tokens

Output:$600.00/M tokens

Qwen3.8 27B pricing:

Input:$0.42/M tokens

Output:$2.55/M tokens

Winner

o1-pro

OpenAI

Composite Score

Qwen3.8 27B

Alibaba

Composite Score

Signal-by-Signal Comparison

Metric	o1-pro	Qwen3.8 27B	Winner
Overall Score	74	73	o1-pro
Rank	#113	#114	o1-pro
Quality Rank	#113	#114	o1-pro
Adoption Rank	#113	#114	o1-pro
Parameters	--	27B	--
Context Window	200K	1000K	Qwen3.8 27B
Pricing	$150.00/$600.00/M	$0.42/$2.55/M	--
Signal Scores
Capabilities	83	83	o1-pro
Benchmarks	77	72	o1-pro
Pricing	5	98	Qwen3.8 27B
Context window size	84	95	Qwen3.8 27B
Recency	37	100	Qwen3.8 27B
Output Capacity	80	82	Qwen3.8 27B

Benchmark Head-to-Head(12 benchmarks)

o1-pro: 0Qwen3.8 27B: 1

o1-pro

Qwen3.8 27B

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%-

AIME 2024

83.3%-

IFEval

86.5%-

BBH

89%-

Arena Elo

13501436

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 #113), placing it in the top 61% of all 290 models tracked.

Raw Quality0/100

Cost Efficiency0/100

Speed0/100

Qwen3.8 27BStrong Performer

Scores 73/100 (rank #114), placing it in the top 61% 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:

Step-by-step reasoning and chain-of-thought problem solving

Choose Qwen3.8 27B when you need:

High-volume production workloads where API costs must be minimized
Processing long documents or large codebases (1000K token context)
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

Qwen3.8 27BBest Value

Input cost$0.42/M tokens

Output cost$2.55/M tokens

Cost per quality point$0.041

Est. monthly (1M tokens/day)$44.63

Qwen3.8 27B offers 100% better value per quality point. At 1M tokens/day, you'd spend $44.63/month with Qwen3.8 27B vs $11250.00/month with o1-pro - a $11205.38 monthly difference.

Latency & Speed

o1-proFaster

Speed score0/100

Qwen3.8 27B

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. Qwen3.8 27B also offers lower per-token costs for high-volume support

o1-pro

Long document analysis

Larger context window (1000K tokens) can process longer documents, contracts, and research papers in a single pass

Qwen3.8 27B

Batch data extraction

Lower output pricing ($2.55/M) reduces costs when processing thousands of records daily

Qwen3.8 27B

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 Qwen3.8 27B are extremely close in overall performance (only 0.3999999999999915 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

Qwen3.8 27B

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