Pairwise benchmark snapshot · Aug 17, 2026

Qwen 2.5 Coder 32B vs Yi-Large benchmark

Name: Qwen 2.5 Coder 32B vs Yi-Large AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Qwen 2.5 Coder 32B (Alibaba) and Yi-Large (01.AI) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, Yi-Large is ahead by 5 points (1,430 vs 1,425; lmarena (Aug 17, 2026)). On SWE-bench coding, Qwen 2.5 Coder 32B resolves 65.2% versus 53.8% (seed-bootstrap (Aug 1, 2026)). Qwen 2.5 Coder 32B streams faster (110 tok/s) while Yi-Large is the cheaper output token ($0.3/1M). Qwen 2.5 Coder 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 2.5 Coder 32B

Alibaba dedicated open-weight code generation model with near-frontier SWE-bench Verified coding capability.

Elo 1,425$0.72/1M 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

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.

Qwen 2.5 Coder 32B (8)vsYi-Large (2)

Qwen 2.5 Coder 32B: 8W (80%)Overall: Qwen 2.5 Coder 32BYi-Large: 2W (20%)

← Qwen 2.5 Coder 32BYi-Large →

Qwen 2.5 Coder 32B (4/5)

Intelligence & Reasoning

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

Preference Elo

1,425vs1,430

Coding Elo

1,495vs1,410

SWE-bench

65.2%vs53.8%

LiveBench

64.8%vs60.5%

GPQA Diamond

74%vs72.4%

Qwen 2.5 Coder 32B: 4WYi-Large: 1W

Qwen 2.5 Coder 32B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

110 tok/svs90 tok/s

Time to first token

180 msvs220 ms

Qwen 2.5 Coder 32B: 2WYi-Large: 0W

Qwen 2.5 Coder 32B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.72/1Mvs$0.3/1M

Input price

$0.18/1Mvs$0.3/1M

Context window

131kvs33k

Qwen 2.5 Coder 32B: 2WYi-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 2.5 Coder 32B 5/6

Preference Elo

Yi-Large

5 pts advantage

Throughput Speed

Qwen 2.5 Coder 32B

20 tok/s faster

Token Price Efficiency

Yi-Large

$0.42/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,425

EloHigh is better

1,430

Code Elo

High is better

+6%1,495

Code EloHigh is better

1,410

LiveBench

High is better

+7%64.8%

LiveBenchHigh is better

60.5%

SWE-bench

High is better

+21%65.2%

SWE-benchHigh is better

53.8%

GPQA

High is better

+2%74%

GPQAHigh is better

72.4%

TTFT

Low is better

+22%180 ms

TTFTLow is better

220 ms

Speed

High is better

+22%110 tok/s

SpeedHigh is better

90 tok/s

In $

Low is better

+67%$0.18 / 1M

In $Low is better

$0.30 / 1M

Out $

Low is better

$0.72 / 1M

Out $Low is better

$0.30 / 1M+140%

Context

High is better

+300%131k

ContextHigh is better

33k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 12% 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 2.5 Coder 32B$17.10 / mo

In: $6.3Out: $10.8

Yi-Large$15.00 / mo

In: $10.5Out: $4.5

Estimated Cost Delta

Yi-Large is estimated to save $2.10/month ($25/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Qwen 2.5 Coder 32B:Pick Qwen 2.5 Coder 32B when repo-level coding accuracy is the constraint.
2
Yi-Large:Pick Yi-Large when you are optimizing output cost.
3
Qwen 2.5 Coder 32B:Pick Qwen 2.5 Coder 32B when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsQwen 2.5 Coder 32B

Higher SWE-bench (65.2%).

High-volume chatYi-Large

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

Voice / low-latency UIQwen 2.5 Coder 32B

Lower TTFT (180 ms).

Long-document RAGQwen 2.5 Coder 32B

Larger window (131k).

Screenshots / visionYi-Large

Yi-Large is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen 2.5 Coder 32B	Higher SWE-bench (65.2%).
High-volume chat	Yi-Large	Lower output list price ($0.3/1M).
Voice / low-latency UI	Qwen 2.5 Coder 32B	Lower TTFT (180 ms).
Long-document RAG	Qwen 2.5 Coder 32B	Larger window (131k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

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CompareLLM Matrix

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LIVE

Alibaba

Qwen 2.5 Coder 32B

Elo 1,425

LiveBench64.8%

SWE-bench65.2%

Speed110 tok/s

Output cost$0.72/1M

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 2.5 Coder 32B or Yi-Large?: Yi-Large has the higher preference Elo in our latest snapshot (1,430). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Qwen 2.5 Coder 32B or Yi-Large?: Qwen 2.5 Coder 32B leads SWE-bench at 65.2% 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 $0.72/1M. Input prices and retry rates still move the real bill.
Which is faster, Qwen 2.5 Coder 32B or Yi-Large?: Qwen 2.5 Coder 32B has the lower time-to-first-token (180 ms vs 220 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Qwen 2.5 Coder 32B accepts 131k tokens versus 33k.
Can I self-host either model?: Qwen 2.5 Coder 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 2.5 Coder 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 2.5 Coder 32B matchups →|All Yi-Large matchups →

Qwen 2.5 Coder 32B vs Qwen 2.5 Plus (previous qwen)Qwen 2.5 Coder 32B vs Qwen3 235B (next qwen)Yi-Large vs Yi-Lightning (next other)Qwen QwQ 32B vs Yi-Large Qwen 3 Max vs Yi-Large Qwen2.5 Coder 32B Instruct vs Qwen 2.5 Coder 32B

Qwen 2.5 Coder 32B vs Yi-Large benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Qwen 2.5 Coder 32B (8)vsYi-Large (2)

Qwen 2.5 Coder 32B: 8W (80%)Overall: Qwen 2.5 Coder 32BYi-Large: 2W (20%)

← Qwen 2.5 Coder 32BYi-Large →

Qwen 2.5 Coder 32B (4/5)

Intelligence & Reasoning

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

Preference Elo

1,425vs1,430

Coding Elo

1,495vs1,410

SWE-bench

65.2%vs53.8%

LiveBench

64.8%vs60.5%

GPQA Diamond

74%vs72.4%

Qwen 2.5 Coder 32B: 4WYi-Large: 1W

Qwen 2.5 Coder 32B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

110 tok/svs90 tok/s

Time to first token

180 msvs220 ms

Qwen 2.5 Coder 32B: 2WYi-Large: 0W

Qwen 2.5 Coder 32B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.72/1Mvs$0.3/1M

Input price

$0.18/1Mvs$0.3/1M

Context window

131kvs33k

Qwen 2.5 Coder 32B: 2WYi-Large: 1W

Recommended Workload Routing

Repo / coding agentsQwen 2.5 Coder 32B

Higher SWE-bench (65.2%).

High-volume chatYi-Large

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

Voice / low-latency UIQwen 2.5 Coder 32B

Lower TTFT (180 ms).

Long-document RAGQwen 2.5 Coder 32B

Larger window (131k).

Screenshots / visionYi-Large

Yi-Large is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Qwen 2.5 Coder 32B	Higher SWE-bench (65.2%).
High-volume chat	Yi-Large	Lower output list price ($0.3/1M).
Voice / low-latency UI	Qwen 2.5 Coder 32B	Lower TTFT (180 ms).
Long-document RAG	Qwen 2.5 Coder 32B	Larger window (131k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Qwen 2.5 Coder 32B or Yi-Large?

Yi-Large has the higher preference Elo in our latest snapshot (1,430). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Qwen 2.5 Coder 32B or Yi-Large?

Qwen 2.5 Coder 32B leads SWE-bench at 65.2% 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 $0.72/1M. Input prices and retry rates still move the real bill.

Which is faster, Qwen 2.5 Coder 32B or Yi-Large?

Qwen 2.5 Coder 32B has the lower time-to-first-token (180 ms vs 220 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Qwen 2.5 Coder 32B accepts 131k tokens versus 33k.

Can I self-host either model?

Qwen 2.5 Coder 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 2.5 Coder 32B and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.