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

OpenAI o1-mini vs Qwen: Qwen-Plus benchmark

Name: OpenAI o1-mini vs Qwen: Qwen-Plus AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares OpenAI o1-mini (OpenAI) and Qwen: Qwen-Plus (Alibaba) using the latest snapshots we have as of Aug 17, 2026. Qwen: Qwen-Plus wins on output price at $0.78/1M. Qwen: Qwen-Plus has the larger context window (1M 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.

Share Analysis:

WhatsApp Telegram X / Post LinkedIn Reddit

OpenAI

OpenAI o1-mini

OpenAI high-speed, cost-effective reasoning model optimized for STEM, math, and code generation.

Elo 1,445$4.4/1M out

Alibaba

Qwen: Qwen-Plus

Auto-discovered from OpenRouter (qwen/qwen-plus). Preview until a second source matches.

$0.78/1M out

Swap or Add Any Model to this Matchup

Instant pairwise benchmark recalculation across 400+ LLMs

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.

OpenAI o1-mini (7)vsQwen: Qwen-Plus (3)

OpenAI o1-mini: 7W (70%)Overall: OpenAI o1-miniQwen: Qwen-Plus: 3W (30%)

← OpenAI o1-miniQwen: Qwen-Plus →

OpenAI o1-mini (5/5)

Intelligence & Reasoning

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

Preference Elo

1,445vs—

Coding Elo

1,468vs—

SWE-bench

56.4%vs—

LiveBench

62%vs—

GPQA Diamond

76.8%vs—

OpenAI o1-mini: 5WQwen: Qwen-Plus: 0W

OpenAI o1-mini (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

85 tok/svs—

Time to first token

280 msvs—

OpenAI o1-mini: 2WQwen: Qwen-Plus: 0W

Qwen: Qwen-Plus (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4.4/1Mvs$0.78/1M

Input price

$1.1/1Mvs$0.26/1M

Context window

128kvs1M

OpenAI o1-mini: 0WQwen: Qwen-Plus: 3W

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 AdvantageOpenAI o1-mini 4/6

Preference Elo

No shared data

Throughput Speed

No shared data

Token Price Efficiency

Qwen: Qwen-Plus

$3.62/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,445

EloHigh is better

—

Code Elo

High is better

1,468

Code EloHigh is better

—

LiveBench

High is better

62%

LiveBenchHigh is better

—

SWE-bench

High is better

56.4%

SWE-benchHigh is better

—

GPQA

High is better

76.8%

GPQAHigh is better

—

TTFT

Low is better

280 ms

TTFTLow is better

—

Speed

High is better

85 tok/s

SpeedHigh is better

—

In $

Low is better

$1.10 / 1M

In $Low is better

$0.26 / 1M+323%

Out $

Low is better

$4.40 / 1M

Out $Low is better

$0.78 / 1M+464%

Context

High is better

128k

ContextHigh is better

1M+681%

Interactive Simulator (USD)

Workload Cost & Savings Calculator

Simulate monthly production API costs in USD (US Dollar).

Save up to 80% with Qwen: Qwen-Plus

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

OpenAI o1-mini$104.50 / mo

In: $38.5Out: $66

Qwen: Qwen-Plus$20.80 / mo

In: $9.1Out: $11.7

Estimated Cost Delta

Qwen: Qwen-Plus is estimated to save $83.70/month ($1,004/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Qwen: Qwen-Plus:Pick Qwen: Qwen-Plus when you are optimizing output cost.
2
Qwen: Qwen-Plus:Pick Qwen: Qwen-Plus for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatQwen: Qwen-Plus

Lower output list price ($0.78/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGQwen: Qwen-Plus

Larger window (1M).

Screenshots / visionQwen: Qwen-Plus

Qwen: Qwen-Plus 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	Qwen: Qwen-Plus	Lower output list price ($0.78/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Qwen: Qwen-Plus	Larger window (1M).
Screenshots / vision	Qwen: Qwen-Plus	Qwen: Qwen-Plus is the side marked multimodal in the catalog.

Community Sentiment

Cast Your Matchup Vote

0 total votes

Community Discussions (0)

No comments posted on this matchup yet. Be the first to share an evaluation note!

Share card

The real PNG is generated at /compare/o1-mini-vs-qwen-plus/opengraph-image for crawlers.

PNG

CompareLLM Matrix

Verified head-to-head card

LIVE

OpenAI

OpenAI o1-mini

Elo 1,445

LiveBench62%

SWE-bench56.4%

Speed85 tok/s

Output cost$4.4/1M

Alibaba

Qwen: Qwen-Plus

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$0.78/1M

Frequently Asked Questions

Which is better overall, OpenAI o1-mini or Qwen: Qwen-Plus?: 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, OpenAI o1-mini or Qwen: Qwen-Plus?: 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?: Qwen: Qwen-Plus output tokens are $0.78/1M versus $4.4/1M. Input prices and retry rates still move the real bill.
Which is faster, OpenAI o1-mini or Qwen: Qwen-Plus?: We do not have TTFT for both models.
Which has the larger context window?: Qwen: Qwen-Plus accepts 1M tokens versus 128k.
Can I self-host either model?: Neither model is marked open-weights here. You are comparing hosted APIs.
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. OpenAI o1-mini and Qwen: Qwen-Plus Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All OpenAI o1-mini matchups →|All Qwen: Qwen-Plus matchups →

OpenAI o1-mini vs GPT-4o mini (previous gpt-mini)OpenAI o1-mini vs OpenAI: o3 Mini (next gpt-mini)Qwen 3 Max vs OpenAI o1-mini GPT-4.5 Orion vs OpenAI o1-mini GPT-5.6 Terra vs OpenAI o1-mini GPT-5 vs OpenAI o1-mini

OpenAI o1-mini vs Qwen: Qwen-Plus benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI o1-mini (7)vsQwen: Qwen-Plus (3)

OpenAI o1-mini: 7W (70%)Overall: OpenAI o1-miniQwen: Qwen-Plus: 3W (30%)

← OpenAI o1-miniQwen: Qwen-Plus →

OpenAI o1-mini (5/5)

Intelligence & Reasoning

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

Preference Elo

1,445vs—

Coding Elo

1,468vs—

SWE-bench

56.4%vs—

LiveBench

62%vs—

GPQA Diamond

76.8%vs—

OpenAI o1-mini: 5WQwen: Qwen-Plus: 0W

OpenAI o1-mini (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

85 tok/svs—

Time to first token

280 msvs—

OpenAI o1-mini: 2WQwen: Qwen-Plus: 0W

Qwen: Qwen-Plus (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4.4/1Mvs$0.78/1M

Input price

$1.1/1Mvs$0.26/1M

Context window

128kvs1M

OpenAI o1-mini: 0WQwen: Qwen-Plus: 3W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatQwen: Qwen-Plus

Lower output list price ($0.78/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGQwen: Qwen-Plus

Larger window (1M).

Screenshots / visionQwen: Qwen-Plus

Qwen: Qwen-Plus 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	Qwen: Qwen-Plus	Lower output list price ($0.78/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Qwen: Qwen-Plus	Larger window (1M).
Screenshots / vision	Qwen: Qwen-Plus	Qwen: Qwen-Plus is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, OpenAI o1-mini or Qwen: Qwen-Plus?

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, OpenAI o1-mini or Qwen: Qwen-Plus?

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?

Qwen: Qwen-Plus output tokens are $0.78/1M versus $4.4/1M. Input prices and retry rates still move the real bill.

Which is faster, OpenAI o1-mini or Qwen: Qwen-Plus?

We do not have TTFT for both models.

Which has the larger context window?

Qwen: Qwen-Plus accepts 1M tokens versus 128k.

Can I self-host either model?

Neither model is marked open-weights here. You are comparing hosted APIs.

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. OpenAI o1-mini and Qwen: Qwen-Plus Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.