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

OpenAI: o4 Mini High vs Qwen QwQ 32B benchmark

Name: OpenAI: o4 Mini High vs Qwen QwQ 32B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares OpenAI: o4 Mini High (OpenAI) and Qwen QwQ 32B (Alibaba) using the latest snapshots we have as of Aug 17, 2026. Qwen QwQ 32B wins on output price at $1.2/1M. OpenAI: o4 Mini High has the larger context window (200k 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: o4 Mini High

Auto-discovered from OpenRouter (openai/o4-mini-high). Preview until a second source matches.

$4.4/1M out

Alibaba

Qwen QwQ 32B

Alibaba specialized open reasoning model competing with frontier closed reasoning models.

QwQ ReasoningElo 1,495$1.2/1M out

Qwen QwQ 32B Inference Compute Profile

Inference-Time Compute & Reasoning EffortDynamic CoT

Qwen QwQ 32B Reasoning Scaler & Token Billing Simulator

Param: enable_reasoning

Reasoning models scale test-time compute by generating hidden chain-of-thought (CoT) tokens. These tokens are billed at standard output rates and directly expand latency (TTFT) in exchange for higher GPQA Diamond and SWE-bench Verified problem resolve rates.

Estimated TTFT

960 ms

3x latency

CoT Thinking Tokens

~7,200

Billed as output tokens

Est. Cost / 1k Calls

$9.15

17.9x base invoice

STEM / SWE Boost

+11.8%

GPQA & SWE-bench scaling

High (Max QwQ CoT) Operational Profile

Exhaustive exploration of problem constraints and self-verification.

Direct Provider APIAlibaba

"enable_reasoning": true

OpenRouter Unified APIUnified Gateway

"reasoning": {
  "effort": "high",
  "max_tokens": 7200
}

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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: o4 Mini High (1)vsQwen QwQ 32B (9)

OpenAI: o4 Mini High: 1W (10%)Overall: Qwen QwQ 32BQwen QwQ 32B: 9W (90%)

← OpenAI: o4 Mini HighQwen QwQ 32B →

Qwen QwQ 32B (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,495

Coding Elo

—vs1,520

SWE-bench

—vs67.5%

LiveBench

—vs68%

GPQA Diamond

—vs83.2%

OpenAI: o4 Mini High: 0WQwen QwQ 32B: 5W

Qwen QwQ 32B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs68 tok/s

Time to first token

—vs320 ms

OpenAI: o4 Mini High: 0WQwen QwQ 32B: 2W

Qwen QwQ 32B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4.4/1Mvs$1.2/1M

Input price

$1.1/1Mvs$0.3/1M

Context window

200kvs131k

OpenAI: o4 Mini High: 1WQwen QwQ 32B: 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 QwQ 32B 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Token Price Efficiency

Qwen QwQ 32B

$3.20/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,495

Code Elo

High is better

—

Code EloHigh is better

1,520

LiveBench

High is better

—

LiveBenchHigh is better

68%

SWE-bench

High is better

—

SWE-benchHigh is better

67.5%

GPQA

High is better

—

GPQAHigh is better

83.2%

TTFT

Low is better

—

TTFTLow is better

320 ms

Speed

High is better

—

SpeedHigh is better

68 tok/s

In $

Low is better

$1.10 / 1M

In $Low is better

$0.30 / 1M+267%

Out $

Low is better

$4.40 / 1M

Out $Low is better

$1.20 / 1M+267%

Context

High is better

+53%200k

ContextHigh is better

131k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 73% with Qwen QwQ 32B

Monthly Volume50M tokens/mo

Quick Presets:

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

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

OpenAI: o4 Mini High$104.50 / mo

In: $38.5Out: $66

Qwen QwQ 32B$28.50 / mo

In: $10.5Out: $18

Estimated Cost Delta

Qwen QwQ 32B is estimated to save $76.00/month ($912/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Qwen QwQ 32B:Pick Qwen QwQ 32B when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatQwen QwQ 32B

Lower output list price ($1.2/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGOpenAI: o4 Mini High

Larger window (200k).

Screenshots / visionQwen QwQ 32B

Qwen QwQ 32B 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 QwQ 32B	Lower output list price ($1.2/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	OpenAI: o4 Mini High	Larger window (200k).
Screenshots / vision	Qwen QwQ 32B	Qwen QwQ 32B is the side marked multimodal in the catalog.

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CompareLLM Matrix

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LIVE

OpenAI

OpenAI: o4 Mini High

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$4.4/1M

Alibaba

Qwen QwQ 32B

Elo 1,495

LiveBench68%

SWE-bench67.5%

Speed68 tok/s

Output cost$1.2/1M

Frequently Asked Questions

Which is better overall, OpenAI: o4 Mini High or Qwen QwQ 32B?: 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: o4 Mini High or Qwen QwQ 32B?: 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 QwQ 32B output tokens are $1.2/1M versus $4.4/1M. Input prices and retry rates still move the real bill.
Which is faster, OpenAI: o4 Mini High or Qwen QwQ 32B?: We do not have TTFT for both models.
Which has the larger context window?: OpenAI: o4 Mini High accepts 200k tokens versus 131k.
Can I self-host either model?: Qwen QwQ 32B is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.
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: o4 Mini High and Qwen QwQ 32B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All OpenAI: o4 Mini High matchups →|All Qwen QwQ 32B matchups →

Qwen QwQ 32B vs Qwen3 235B (previous qwen)GPT-5 vs Qwen QwQ 32B Qwen 3 Max vs Qwen QwQ 32B GPT-4.5 Orion vs Qwen QwQ 32B GPT-5.6 Sol vs Qwen QwQ 32B GPT-5.6 Terra vs Qwen QwQ 32B

OpenAI: o4 Mini High vs Qwen QwQ 32B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI: o4 Mini High (1)vsQwen QwQ 32B (9)

OpenAI: o4 Mini High: 1W (10%)Overall: Qwen QwQ 32BQwen QwQ 32B: 9W (90%)

← OpenAI: o4 Mini HighQwen QwQ 32B →

Qwen QwQ 32B (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,495

Coding Elo

—vs1,520

SWE-bench

—vs67.5%

LiveBench

—vs68%

GPQA Diamond

—vs83.2%

OpenAI: o4 Mini High: 0WQwen QwQ 32B: 5W

Qwen QwQ 32B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs68 tok/s

Time to first token

—vs320 ms

OpenAI: o4 Mini High: 0WQwen QwQ 32B: 2W

Qwen QwQ 32B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4.4/1Mvs$1.2/1M

Input price

$1.1/1Mvs$0.3/1M

Context window

200kvs131k

OpenAI: o4 Mini High: 1WQwen QwQ 32B: 2W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatQwen QwQ 32B

Lower output list price ($1.2/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGOpenAI: o4 Mini High

Larger window (200k).

Screenshots / visionQwen QwQ 32B

Qwen QwQ 32B 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 QwQ 32B	Lower output list price ($1.2/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	OpenAI: o4 Mini High	Larger window (200k).
Screenshots / vision	Qwen QwQ 32B	Qwen QwQ 32B is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, OpenAI: o4 Mini High or Qwen QwQ 32B?

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: o4 Mini High or Qwen QwQ 32B?

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 QwQ 32B output tokens are $1.2/1M versus $4.4/1M. Input prices and retry rates still move the real bill.

Which is faster, OpenAI: o4 Mini High or Qwen QwQ 32B?

We do not have TTFT for both models.

Which has the larger context window?

OpenAI: o4 Mini High accepts 200k tokens versus 131k.

Can I self-host either model?

Qwen QwQ 32B is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.

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: o4 Mini High and Qwen QwQ 32B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.