Pairwise benchmark snapshot · Aug 17, 2026

OpenAI o1-mini vs Qwen 2.5 Plus benchmark

Name: OpenAI o1-mini vs Qwen 2.5 Plus AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares OpenAI o1-mini (OpenAI) and Qwen 2.5 Plus (Alibaba) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, OpenAI o1-mini is ahead by 0 points (1,445 vs 1,445; lmarena (Aug 17, 2026)). On SWE-bench coding, Qwen 2.5 Plus resolves 59.2% versus 56.4% (swebench (Aug 17, 2026)). Qwen 2.5 Plus streams faster (110 tok/s) while Qwen 2.5 Plus is the cheaper output token ($1/1M). OpenAI o1-mini has the larger context window (128k 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.

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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 2.5 Plus

Alibaba balanced flagship API model with high-throughput general reasoning.

Elo 1,445$1/1M out

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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 (2)vsQwen 2.5 Plus (6)

OpenAI o1-mini: 2W (25%)Overall: Qwen 2.5 PlusQwen 2.5 Plus: 6W (75%)

← OpenAI o1-mini2 tiesQwen 2.5 Plus →

Tied (2/5)

Intelligence & Reasoning

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

Preference Elo

1,445vs1,445

Coding Elo

1,468vs1,460

SWE-bench

56.4%vs59.2%

LiveBench

62%vs63.5%

GPQA Diamond

76.8%vs75.8%

OpenAI o1-mini: 2WQwen 2.5 Plus: 2W

Qwen 2.5 Plus (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

85 tok/svs110 tok/s

Time to first token

280 msvs180 ms

OpenAI o1-mini: 0WQwen 2.5 Plus: 2W

Qwen 2.5 Plus (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4.4/1Mvs$1/1M

Input price

$1.1/1Mvs$0.25/1M

Context window

128kvs128k

OpenAI o1-mini: 0WQwen 2.5 Plus: 2W

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 AdvantageQwen 2.5 Plus 3/6

Preference Elo

OpenAI o1-mini

Tied rating

Throughput Speed

Qwen 2.5 Plus

25 tok/s faster

Token Price Efficiency

Qwen 2.5 Plus

$3.40/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,445

EloHigh is better

1,445

Code Elo

High is better

+1%1,468

Code EloHigh is better

1,460

LiveBench

High is better

62%

LiveBenchHigh is better

63.5%+2%

SWE-bench

High is better

56.4%

SWE-benchHigh is better

59.2%+5%

GPQA

High is better

+1%76.8%

GPQAHigh is better

75.8%

TTFT

Low is better

280 ms

TTFTLow is better

180 ms+56%

Speed

High is better

85 tok/s

SpeedHigh is better

110 tok/s+29%

In $

Low is better

$1.10 / 1M

In $Low is better

$0.25 / 1M+340%

Out $

Low is better

$4.40 / 1M

Out $Low is better

$1.00 / 1M+340%

Context

High is better

128k

ContextHigh is better

128k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 77% with Qwen 2.5 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 2.5 Plus$23.75 / mo

In: $8.75Out: $15

Estimated Cost Delta

Qwen 2.5 Plus is estimated to save $80.75/month ($969/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Qwen 2.5 Plus:Pick Qwen 2.5 Plus when repo-level coding accuracy is the constraint.
2
Qwen 2.5 Plus:Pick Qwen 2.5 Plus when time-to-first-token matters more than peak Elo.
3
OpenAI o1-mini:Pick OpenAI o1-mini as the default general assistant.

Recommended Workload Routing

Repo / coding agentsQwen 2.5 Plus

Higher SWE-bench (59.2%).

High-volume chatQwen 2.5 Plus

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

Voice / low-latency UIQwen 2.5 Plus

Lower TTFT (180 ms).

Long-document RAGOpenAI o1-mini

Larger window (128k).

Screenshots / visionQwen 2.5 Plus

Qwen 2.5 Plus is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen 2.5 Plus	Higher SWE-bench (59.2%).
High-volume chat	Qwen 2.5 Plus	Lower output list price ($1/1M).
Voice / low-latency UI	Qwen 2.5 Plus	Lower TTFT (180 ms).
Long-document RAG	OpenAI o1-mini	Larger window (128k).
Screenshots / vision	Qwen 2.5 Plus	Qwen 2.5 Plus is the side marked multimodal in the catalog.

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CompareLLM Matrix

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LIVE

OpenAI

OpenAI o1-mini

Elo 1,445

LiveBench62%

SWE-bench56.4%

Speed85 tok/s

Output cost$4.4/1M

Alibaba

Qwen 2.5 Plus

Elo 1,445

LiveBench63.5%

SWE-bench59.2%

Speed110 tok/s

Output cost$1/1M

Frequently Asked Questions

Which is better overall, OpenAI o1-mini or Qwen 2.5 Plus?: OpenAI o1-mini has the higher preference Elo in our latest snapshot (1,445). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, OpenAI o1-mini or Qwen 2.5 Plus?: Qwen 2.5 Plus leads SWE-bench at 59.2% vs 56.4%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Qwen 2.5 Plus output tokens are $1/1M versus $4.4/1M. Input prices and retry rates still move the real bill.
Which is faster, OpenAI o1-mini or Qwen 2.5 Plus?: Qwen 2.5 Plus has the lower time-to-first-token (180 ms vs 280 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: OpenAI o1-mini accepts 128k 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 2.5 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 2.5 Plus matchups →

OpenAI o1-mini vs GPT-4o mini (previous gpt-mini)OpenAI o1-mini vs OpenAI: o3 Mini (next gpt-mini)Qwen 2.5 Plus vs Qwen2.5 Coder 32B Instruct (previous qwen)Qwen 2.5 Plus vs Qwen 2.5 Coder 32B (next qwen)Qwen 3 Max vs Qwen 2.5 Plus GPT-4.5 Orion vs Qwen 2.5 Plus

OpenAI o1-mini vs Qwen 2.5 Plus benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI o1-mini (2)vsQwen 2.5 Plus (6)

OpenAI o1-mini: 2W (25%)Overall: Qwen 2.5 PlusQwen 2.5 Plus: 6W (75%)

← OpenAI o1-mini2 tiesQwen 2.5 Plus →

Tied (2/5)

Intelligence & Reasoning

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

Preference Elo

1,445vs1,445

Coding Elo

1,468vs1,460

SWE-bench

56.4%vs59.2%

LiveBench

62%vs63.5%

GPQA Diamond

76.8%vs75.8%

OpenAI o1-mini: 2WQwen 2.5 Plus: 2W

Qwen 2.5 Plus (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

85 tok/svs110 tok/s

Time to first token

280 msvs180 ms

OpenAI o1-mini: 0WQwen 2.5 Plus: 2W

Qwen 2.5 Plus (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4.4/1Mvs$1/1M

Input price

$1.1/1Mvs$0.25/1M

Context window

128kvs128k

OpenAI o1-mini: 0WQwen 2.5 Plus: 2W

Recommended Workload Routing

Repo / coding agentsQwen 2.5 Plus

Higher SWE-bench (59.2%).

High-volume chatQwen 2.5 Plus

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

Voice / low-latency UIQwen 2.5 Plus

Lower TTFT (180 ms).

Long-document RAGOpenAI o1-mini

Larger window (128k).

Screenshots / visionQwen 2.5 Plus

Qwen 2.5 Plus is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen 2.5 Plus	Higher SWE-bench (59.2%).
High-volume chat	Qwen 2.5 Plus	Lower output list price ($1/1M).
Voice / low-latency UI	Qwen 2.5 Plus	Lower TTFT (180 ms).
Long-document RAG	OpenAI o1-mini	Larger window (128k).
Screenshots / vision	Qwen 2.5 Plus	Qwen 2.5 Plus is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, OpenAI o1-mini or Qwen 2.5 Plus?

OpenAI o1-mini has the higher preference Elo in our latest snapshot (1,445). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, OpenAI o1-mini or Qwen 2.5 Plus?

Qwen 2.5 Plus leads SWE-bench at 59.2% vs 56.4%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Qwen 2.5 Plus output tokens are $1/1M versus $4.4/1M. Input prices and retry rates still move the real bill.

Which is faster, OpenAI o1-mini or Qwen 2.5 Plus?

Qwen 2.5 Plus has the lower time-to-first-token (180 ms vs 280 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

OpenAI o1-mini accepts 128k 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 2.5 Plus Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.