Pairwise benchmark snapshot · Aug 1, 2026

Qwen QwQ 32B vs Yi-Large benchmark

Name: Qwen QwQ 32B vs Yi-Large AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Qwen QwQ 32B (Alibaba) and Yi-Large (01.AI) using the latest snapshots we have as of Aug 1, 2026. On preference Elo, Qwen QwQ 32B is ahead by 65 points (1,495 vs 1,430; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, Qwen QwQ 32B resolves 67.5% versus 53.8% (seed-bootstrap (Aug 1, 2026)). Yi-Large streams faster (90 tok/s) while Yi-Large is the cheaper output token ($0.3/1M). Qwen QwQ 32B has the larger context window (131k 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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Alibaba

Qwen QwQ 32B

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

Elo 1,495$1.2/1M out

01.AI

Yi-Large

01.AI full-scale dense model for complex instruction following.

Elo 1,430$0.3/1M out

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Model A

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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.

Qwen QwQ 32B (6)vsYi-Large (3)

Qwen QwQ 32B: 6W (67%)Overall: Qwen QwQ 32BYi-Large: 3W (33%)

← Qwen QwQ 32B1 tiesYi-Large →

Qwen QwQ 32B (5/5)

Intelligence & Reasoning

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

Preference Elo

1,495vs1,430

Coding Elo

1,520vs1,410

SWE-bench

67.5%vs53.8%

LiveBench

68%vs60.5%

GPQA Diamond

83.2%vs72.4%

Qwen QwQ 32B: 5WYi-Large: 0W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

68 tok/svs90 tok/s

Time to first token

320 msvs220 ms

Qwen QwQ 32B: 0WYi-Large: 2W

Tied (1/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.2/1Mvs$0.3/1M

Input price

$0.3/1Mvs$0.3/1M

Context window

131kvs33k

Qwen QwQ 32B: 1WYi-Large: 1W

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 4/6

Preference Elo

Qwen QwQ 32B

65 pts advantage

Throughput Speed

Yi-Large

22 tok/s faster

Price Efficiency

Yi-Large

$0.90/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+5%1,495

EloHigh is better

1,430

Code Elo

High is better

+8%1,520

Code EloHigh is better

1,410

LiveBench

High is better

+12%68%

LiveBenchHigh is better

60.5%

SWE-bench

High is better

+25%67.5%

SWE-benchHigh is better

53.8%

GPQA

High is better

+15%83.2%

GPQAHigh is better

72.4%

TTFT

Low is better

320 ms

TTFTLow is better

220 ms+45%

Speed

High is better

68 tok/s

SpeedHigh is better

90 tok/s+32%

In $

Low is better

$0.3/1M

In $Low is better

$0.3/1M

Out $

Low is better

$1.2/1M

Out $Low is better

$0.3/1M+300%

Context

High is better

+300%131k

ContextHigh is better

33k

Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 47% with Yi-Large

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Qwen QwQ 32B$28.50 / mo

In: $10.50Out: $18.00

Yi-Large$15.00 / mo

In: $10.50Out: $4.50

Estimated Cost Delta

Yi-Large is estimated to save $13.50/month ($162/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Qwen QwQ 32B:Pick Qwen QwQ 32B when repo-level coding accuracy is the constraint.
2
Yi-Large:Pick Yi-Large when you are optimizing output cost.
3
Yi-Large:Pick Yi-Large when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsQwen QwQ 32B

Higher SWE-bench (67.5%).

High-volume chatYi-Large

Lower output list price ($0.3/1M).

Voice / low-latency UIYi-Large

Lower TTFT (220 ms).

Long-document RAGQwen QwQ 32B

Larger window (131k).

Screenshots / visionQwen QwQ 32B

Qwen QwQ 32B is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen QwQ 32B	Higher SWE-bench (67.5%).
High-volume chat	Yi-Large	Lower output list price ($0.3/1M).
Voice / low-latency UI	Yi-Large	Lower TTFT (220 ms).
Long-document RAG	Qwen QwQ 32B	Larger window (131k).
Screenshots / vision	Qwen QwQ 32B	Qwen QwQ 32B is the side marked multimodal in the catalog.

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LIVE

Alibaba

Qwen QwQ 32B

Elo 1,495

LiveBench68%

SWE-bench67.5%

Speed68 tok/s

Output cost$1.2/1M

01.AI

Yi-Large

Elo 1,430

LiveBench60.5%

SWE-bench53.8%

Speed90 tok/s

Output cost$0.3/1M

Frequently Asked Questions

Which is better overall, Qwen QwQ 32B or Yi-Large?: Qwen QwQ 32B has the higher preference Elo in our latest snapshot (1,495). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Qwen QwQ 32B or Yi-Large?: Qwen QwQ 32B leads SWE-bench at 67.5% vs 53.8%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Yi-Large output tokens are $0.3/1M versus $1.2/1M. Input prices and retry rates still move the real bill.
Which is faster, Qwen QwQ 32B or Yi-Large?: Yi-Large has the lower time-to-first-token (220 ms vs 320 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Qwen QwQ 32B accepts 131k tokens versus 33k.
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. Qwen QwQ 32B and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Qwen QwQ 32B matchups →|All Yi-Large matchups →

Qwen QwQ 32B vs Qwen3 235B (previous qwen)Qwen QwQ 32B vs Qwen 3 Max (next qwen)Yi-Large vs Yi-Lightning (next other)Yi-Lightning vs Qwen QwQ 32B Qwen 2.5 Plus vs Yi-Large Qwen3 235B vs Yi-Large

Qwen QwQ 32B vs Yi-Large benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Qwen QwQ 32B (6)vsYi-Large (3)

Qwen QwQ 32B: 6W (67%)Overall: Qwen QwQ 32BYi-Large: 3W (33%)

← Qwen QwQ 32B1 tiesYi-Large →

Qwen QwQ 32B (5/5)

Intelligence & Reasoning

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

Preference Elo

1,495vs1,430

Coding Elo

1,520vs1,410

SWE-bench

67.5%vs53.8%

LiveBench

68%vs60.5%

GPQA Diamond

83.2%vs72.4%

Qwen QwQ 32B: 5WYi-Large: 0W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

68 tok/svs90 tok/s

Time to first token

320 msvs220 ms

Qwen QwQ 32B: 0WYi-Large: 2W

Tied (1/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.2/1Mvs$0.3/1M

Input price

$0.3/1Mvs$0.3/1M

Context window

131kvs33k

Qwen QwQ 32B: 1WYi-Large: 1W

Recommended Workload Routing

Repo / coding agentsQwen QwQ 32B

Higher SWE-bench (67.5%).

High-volume chatYi-Large

Lower output list price ($0.3/1M).

Voice / low-latency UIYi-Large

Lower TTFT (220 ms).

Long-document RAGQwen QwQ 32B

Larger window (131k).

Screenshots / visionQwen QwQ 32B

Qwen QwQ 32B is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen QwQ 32B	Higher SWE-bench (67.5%).
High-volume chat	Yi-Large	Lower output list price ($0.3/1M).
Voice / low-latency UI	Yi-Large	Lower TTFT (220 ms).
Long-document RAG	Qwen QwQ 32B	Larger window (131k).
Screenshots / vision	Qwen QwQ 32B	Qwen QwQ 32B is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Qwen QwQ 32B or Yi-Large?

Qwen QwQ 32B has the higher preference Elo in our latest snapshot (1,495). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Qwen QwQ 32B or Yi-Large?

Qwen QwQ 32B leads SWE-bench at 67.5% vs 53.8%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Yi-Large output tokens are $0.3/1M versus $1.2/1M. Input prices and retry rates still move the real bill.

Which is faster, Qwen QwQ 32B or Yi-Large?

Yi-Large has the lower time-to-first-token (220 ms vs 320 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Qwen QwQ 32B accepts 131k tokens versus 33k.

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. Qwen QwQ 32B and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.