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

Qwen: Qwen3.5-9B vs Yi-Large benchmark

Name: Qwen: Qwen3.5-9B vs Yi-Large AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

In this head-to-head showdown, Qwen: Qwen3.5-9B is more budget-friendly at $0.15/1M per 1M output tokens. Review the complete breakdown below to determine which model best fits your performance and budget requirements.

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Executive Verdict Summary

Qwen: Qwen3.5-9B holds empirical advantage (3 of 3 metrics)

Coding / SWESWE-bench

Yi-LargeTop code solve rate

ReasoningGPQA / Live

Yi-LargeTop logical accuracy

Best Value$/1M Tok

Qwen: Qwen3.5-9BLowest API billing cost

Speed / TokTok/Sec

Yi-LargeFastest stream rate

Verified Advantage Breakdown

3 benchmarks evaluated across capability, speed, and cost

Qwen: Qwen3.5-9B (3)Yi-Large (0)

Qwen: Qwen3.5-9B

3 of 3 Wins

✓Input price✓Output price✓Context window

Yi-Large

0 of 3 Wins

Competitive baseline across remaining benchmarks

Alibaba

Leads 3 of 3

Qwen: Qwen3.5-9B

Auto-discovered from OpenRouter (qwen/qwen3.5-9b). Preview until a second source matches.

In: $0.1 · Out: $0.15/1M

01.AI

Yi-Large

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

Elo 1,430In: $0.3 · Out: $0.3/1M

Top Rival Showdowns for Qwen: Qwen3.5-9B

All Matchups

Compare vs:vs Claude Opus 4.5 vs GPT-5 vs Gemini 3 Pro vs Grok 4.6 vs DeepSeek V4 Pro

Preference Leader

No shared data

Single model data

Throughput Leader

No shared data

No latency data

Value per Dollar LeaderQwen: Qwen3.5-9B Wins

Qwen: Qwen3.5-9B

$0.15 / 1M output

Capability Percentiles

Relative percentile scores computed across all active models in the benchmark catalog.

Multi-Dimensional Capability Radar

Leaders Matchup Capability Radar

Comparing top standard models across 6 key capability dimensions. Tap any spoke or dot to inspect.

Tap any node to inspect

0–100 %ile

Overall Matchup Breakdown

Category wins across reasoning intelligence, generation speed, and token cost.

Qwen: Qwen3.5-9B (3)vsYi-Large (7)

Qwen: Qwen3.5-9B: 3W (30%)Yi-Large: 7W (70%)

← Qwen: Qwen3.5-9BYi-Large →

🏆Yi-Large Leads(5/5)

Intelligence & Reasoning

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

Benchmark

Qwen: Qwen3.5-9BvsYi-Large

Preference Elo

—vs1,430

Coding Elo

—vs1,410

SWE-bench

—vs53.8%

LiveBench

—vs60.5%

GPQA Diamond

—vs72.4%

Qwen: Qwen3.5-9B: 0WYi-Large: 5W

🏆Yi-Large Leads(2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Benchmark

Qwen: Qwen3.5-9BvsYi-Large

Output speed

—vs90 tok/s

Time to first token

—vs220 ms

Qwen: Qwen3.5-9B: 0WYi-Large: 2W

🏆Qwen: Qwen3.5-9B Leads(3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Benchmark

Qwen: Qwen3.5-9BvsYi-Large

Output price

$0.15/1Mvs$0.3/1M+$0.15/1M

Input price

$0.1/1Mvs$0.3/1M+$0.2/1M

Context window

262kvs33k+229k

Qwen: Qwen3.5-9B: 3WYi-Large: 0W

Side-by-Side Benchmark Matrix

Qwen: Qwen3.5-9BYi-Large

Preference Elo

Qwen: Qwen3.5-9B—

Yi-Large1,430

Coding Elo

Qwen: Qwen3.5-9B—

Yi-Large1,410

LiveBench

Qwen: Qwen3.5-9B—

Yi-Large60.5%

SWE-bench

Qwen: Qwen3.5-9B—

Yi-Large53.8%

GPQA Diamond

Qwen: Qwen3.5-9B—

Yi-Large72.4%

Time to first token

Qwen: Qwen3.5-9B—

Yi-Large220 ms

Output speed

Qwen: Qwen3.5-9B—

Yi-Large90 tok/s

Input priceQwen: Qwen3.5-9B +$0.2/1M

Qwen: Qwen3.5-9B$0.1/1M

Yi-Large$0.3/1M

Output priceQwen: Qwen3.5-9B +$0.15/1M

Qwen: Qwen3.5-9B$0.15/1M

Yi-Large$0.3/1M

Context windowQwen: Qwen3.5-9B +229k

Qwen: Qwen3.5-9B262k

Yi-Large33k

Benchmark	Qwen: Qwen3.5-9B	Yi-Large	Advantage Delta
Preference Elo	—	1,430 lmarena · Aug 17, 2026	—
Coding Elo	—	1,410 lmarena · Aug 17, 2026	—
LiveBench	—	60.5% livebench · Aug 17, 2026	—
SWE-bench	—	53.8% swebench · Aug 17, 2026	—
GPQA Diamond	—	72.4% seed-bootstrap · Aug 1, 2026	—
Time to first token	—	220 ms seed-bootstrap · Aug 1, 2026	—
Output speed	—	90 tok/s seed-bootstrap · Aug 1, 2026	—
Input price	$0.1/1M openrouter · Aug 17, 2026	$0.3/1M seed-bootstrap · Aug 1, 2026	+$0.2/1M
Output price	$0.15/1M openrouter · Aug 17, 2026	$0.3/1M seed-bootstrap · Aug 1, 2026	+$0.15/1M
Context window	262k openrouter · Aug 17, 2026	33k seed-bootstrap · Aug 1, 2026	+229k

Workload Cost & Savings Calculator

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

Save 62% with Qwen: Qwen3.5-9B

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Qwen: Qwen3.5-9B$5.75 / mo

In: $3.5Out: $2.25

Yi-Large$15.00 / mo

In: $10.5Out: $4.5

Estimated Cost Delta

Qwen: Qwen3.5-9B is estimated to save $9.25/month ($111/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Qwen: Qwen3.5-9B:Pick Qwen: Qwen3.5-9B when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatQwen: Qwen3.5-9B

Lower output list price ($0.15/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGQwen: Qwen3.5-9B

Larger window (262k).

Screenshots / visionYi-Large

Yi-Large 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: Qwen3.5-9B	Lower output list price ($0.15/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Qwen: Qwen3.5-9B	Larger window (262k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Cast Your Matchup Vote

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CompareLLM AI Matrix

Verified Head-to-Head Telemetry

LIVE BENCHMARK

Alibaba

Qwen: Qwen3.5-9B

Elo n/a

SWE-bench—

Speed (tok/s)262k

Input / 1M Tokens$0.1/1M

Output / 1M Tokens$0.15/1M

01.AI

Yi-Large

Elo 1,430

SWE-bench53.8%

Speed (tok/s)90 tok/s

Input / 1M Tokens$0.3/1M

Output / 1M Tokens$0.3/1M

Frequently Asked Questions

Which is better overall, Qwen: Qwen3.5-9B or Yi-Large?: 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, Qwen: Qwen3.5-9B or Yi-Large?: 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: Qwen3.5-9B output tokens are $0.15/1M versus $0.3/1M. Input prices and retry rates still move the real bill.
Which is faster, Qwen: Qwen3.5-9B or Yi-Large?: We do not have TTFT for both models.
Which has the larger context window?: Qwen: Qwen3.5-9B accepts 262k tokens versus 33k.
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. Qwen: Qwen3.5-9B 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: Qwen3.5-9B matchups →|All Yi-Large matchups →

Yi-Large vs Yi-Lightning (next other)Qwen 3 Max vs Yi-Large Qwen QwQ 32B vs Yi-Large Qwen 2.5 Plus vs Yi-Large Qwen2.5 Coder 32B Instruct vs Yi-Large Claude Opus 4.5 vs Yi-Large

Capability Percentiles

Relative percentile scores computed across all active models in the benchmark catalog.

Multi-Dimensional Capability Radar

Leaders Matchup Capability Radar

Comparing top standard models across 6 key capability dimensions. Tap any spoke or dot to inspect.

Tap any node to inspect

0–100 %ile

Side-by-Side Benchmark Matrix

Qwen: Qwen3.5-9BYi-Large

Preference Elo

Qwen: Qwen3.5-9B—

Yi-Large1,430

Coding Elo

Qwen: Qwen3.5-9B—

Yi-Large1,410

LiveBench

Qwen: Qwen3.5-9B—

Yi-Large60.5%

SWE-bench

Qwen: Qwen3.5-9B—

Yi-Large53.8%

GPQA Diamond

Qwen: Qwen3.5-9B—

Yi-Large72.4%

Time to first token

Qwen: Qwen3.5-9B—

Yi-Large220 ms

Output speed

Qwen: Qwen3.5-9B—

Yi-Large90 tok/s

Input priceQwen: Qwen3.5-9B +$0.2/1M

Qwen: Qwen3.5-9B$0.1/1M

Yi-Large$0.3/1M

Output priceQwen: Qwen3.5-9B +$0.15/1M

Qwen: Qwen3.5-9B$0.15/1M

Yi-Large$0.3/1M

Context windowQwen: Qwen3.5-9B +229k

Qwen: Qwen3.5-9B262k

Yi-Large33k

Benchmark

Qwen: Qwen3.5-9B

Yi-Large

Advantage Delta

Preference Elo

—

1,430

lmarena · Aug 17, 2026

—

Coding Elo

—

1,410

lmarena · Aug 17, 2026

—

LiveBench

—

60.5%

livebench · Aug 17, 2026

—

SWE-bench

—

53.8%

swebench · Aug 17, 2026

—

GPQA Diamond

—

72.4%

seed-bootstrap · Aug 1, 2026

—

Time to first token

—

220 ms

seed-bootstrap · Aug 1, 2026

—

Output speed

—

90 tok/s

seed-bootstrap · Aug 1, 2026

—

Input price

$0.1/1M

openrouter · Aug 17, 2026

$0.3/1M

seed-bootstrap · Aug 1, 2026

+$0.2/1M

Output price

$0.15/1M

openrouter · Aug 17, 2026

$0.3/1M

seed-bootstrap · Aug 1, 2026

+$0.15/1M

Context window

262k

openrouter · Aug 17, 2026

33k

seed-bootstrap · Aug 1, 2026

+229k

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatQwen: Qwen3.5-9B

Lower output list price ($0.15/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGQwen: Qwen3.5-9B

Larger window (262k).

Screenshots / visionYi-Large

Yi-Large 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: Qwen3.5-9B	Lower output list price ($0.15/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Qwen: Qwen3.5-9B	Larger window (262k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Qwen: Qwen3.5-9B or Yi-Large?

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, Qwen: Qwen3.5-9B or Yi-Large?

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: Qwen3.5-9B output tokens are $0.15/1M versus $0.3/1M. Input prices and retry rates still move the real bill.

Which is faster, Qwen: Qwen3.5-9B or Yi-Large?

We do not have TTFT for both models.

Which has the larger context window?

Qwen: Qwen3.5-9B accepts 262k tokens versus 33k.

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. Qwen: Qwen3.5-9B and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.