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

OpenAI o1-mini vs Qwen: Qwen3.6 35B A3B benchmark

Name: OpenAI o1-mini vs Qwen: Qwen3.6 35B A3B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares OpenAI o1-mini (OpenAI) and Qwen: Qwen3.6 35B A3B (Alibaba) using the latest snapshots we have as of Aug 17, 2026. Qwen: Qwen3.6 35B A3B wins on output price at $1/1M. Qwen: Qwen3.6 35B A3B has the larger context window (262k 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: Qwen3.6 35B A3B

Auto-discovered from OpenRouter (qwen/qwen3.6-35b-a3b). Preview until a second source matches.

$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 (7)vsQwen: Qwen3.6 35B A3B (3)

OpenAI o1-mini: 7W (70%)Overall: OpenAI o1-miniQwen: Qwen3.6 35B A3B: 3W (30%)

← OpenAI o1-miniQwen: Qwen3.6 35B A3B →

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: Qwen3.6 35B A3B: 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: Qwen3.6 35B A3B: 0W

Qwen: Qwen3.6 35B A3B (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4.4/1Mvs$1/1M

Input price

$1.1/1Mvs$0.14/1M

Context window

128kvs262k

OpenAI o1-mini: 0WQwen: Qwen3.6 35B A3B: 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: Qwen3.6 35B A3B

$3.40/1M 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

$0.14 / 1M+686%

Out $

Low is better

$4.40 / 1M

Out $Low is better

$1.00 / 1M+340%

Context

High is better

128k

ContextHigh is better

262k+105%

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 81% with Qwen: Qwen3.6 35B A3B

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: Qwen3.6 35B A3B$19.90 / mo

In: $4.9Out: $15

Estimated Cost Delta

Qwen: Qwen3.6 35B A3B is estimated to save $84.60/month ($1,015/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Qwen: Qwen3.6 35B A3B:Pick Qwen: Qwen3.6 35B A3B when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatQwen: Qwen3.6 35B A3B

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

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGQwen: Qwen3.6 35B A3B

Larger window (262k).

Screenshots / visionQwen: Qwen3.6 35B A3B

Qwen: Qwen3.6 35B A3B 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: Qwen3.6 35B A3B	Lower output list price ($1/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Qwen: Qwen3.6 35B A3B	Larger window (262k).
Screenshots / vision	Qwen: Qwen3.6 35B A3B	Qwen: Qwen3.6 35B A3B is the side marked multimodal in the catalog.

Community Sentiment

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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: Qwen3.6 35B A3B

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$1/1M

Frequently Asked Questions

Which is better overall, OpenAI o1-mini or Qwen: Qwen3.6 35B A3B?: 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: Qwen3.6 35B A3B?: 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: Qwen3.6 35B A3B 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: Qwen3.6 35B A3B?: We do not have TTFT for both models.
Which has the larger context window?: Qwen: Qwen3.6 35B A3B accepts 262k 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: Qwen3.6 35B A3B 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: Qwen3.6 35B A3B 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: Qwen3.6 35B A3B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI o1-mini (7)vsQwen: Qwen3.6 35B A3B (3)

OpenAI o1-mini: 7W (70%)Overall: OpenAI o1-miniQwen: Qwen3.6 35B A3B: 3W (30%)

← OpenAI o1-miniQwen: Qwen3.6 35B A3B →

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: Qwen3.6 35B A3B: 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: Qwen3.6 35B A3B: 0W

Qwen: Qwen3.6 35B A3B (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4.4/1Mvs$1/1M

Input price

$1.1/1Mvs$0.14/1M

Context window

128kvs262k

OpenAI o1-mini: 0WQwen: Qwen3.6 35B A3B: 3W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatQwen: Qwen3.6 35B A3B

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

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGQwen: Qwen3.6 35B A3B

Larger window (262k).

Screenshots / visionQwen: Qwen3.6 35B A3B

Qwen: Qwen3.6 35B A3B 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: Qwen3.6 35B A3B	Lower output list price ($1/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Qwen: Qwen3.6 35B A3B	Larger window (262k).
Screenshots / vision	Qwen: Qwen3.6 35B A3B	Qwen: Qwen3.6 35B A3B is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, OpenAI o1-mini or Qwen: Qwen3.6 35B A3B?

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: Qwen3.6 35B A3B?

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: Qwen3.6 35B A3B 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: Qwen3.6 35B A3B?

We do not have TTFT for both models.

Which has the larger context window?

Qwen: Qwen3.6 35B A3B accepts 262k 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: Qwen3.6 35B A3B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.