Pairwise benchmark snapshot · Aug 16, 2026

Qwen3.8 27B vs Yi-Large benchmark

Name: Qwen3.8 27B vs Yi-Large AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Qwen3.8 27B (Alibaba) and Yi-Large (01.AI) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, Yi-Large is ahead by 32 points (1,430 vs 1,398; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, Qwen3.8 27B resolves 58.8% versus 53.8% (seed-bootstrap (Aug 16, 2026)). Qwen3.8 27B streams faster (152 tok/s) while Yi-Large is the cheaper output token ($0.3/1M). Qwen3.8 27B 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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Alibaba

Qwen3.8 27B

Alibaba open-weight 27B drop dated Aug 14 2026. Dense enough to self-host; not a frontier MoE.

Elo 1,398$3.1999999999999997/1M 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.

Qwen3.8 27B (7)vsYi-Large (3)

Qwen3.8 27B: 7W (70%)Overall: Qwen3.8 27BYi-Large: 3W (30%)

← Qwen3.8 27BYi-Large →

Qwen3.8 27B (4/5)

Intelligence & Reasoning

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

Preference Elo

1,398vs1,430

Coding Elo

1,422vs1,410

SWE-bench

58.8%vs53.8%

LiveBench

60.6%vs60.5%

GPQA Diamond

73.4%vs72.4%

Qwen3.8 27B: 4WYi-Large: 1W

Qwen3.8 27B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

152 tok/svs90 tok/s

Time to first token

140 msvs220 ms

Qwen3.8 27B: 2WYi-Large: 0W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$3.2/1Mvs$0.3/1M

Input price

$0.45/1Mvs$0.3/1M

Context window

262kvs33k

Qwen3.8 27B: 1WYi-Large: 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 AdvantageQwen3.8 27B 4/6

Preference Elo

Yi-Large

32 pts advantage

Throughput Speed

Qwen3.8 27B

62 tok/s faster

Price Efficiency

Yi-Large

$2.90/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,398

EloHigh is better

1,430+2%

Code Elo

High is better

+1%1,422

Code EloHigh is better

1,410

LiveBench

High is better

60.6%

LiveBenchHigh is better

60.5%

SWE-bench

High is better

+9%58.8%

SWE-benchHigh is better

53.8%

GPQA

High is better

+1%73.4%

GPQAHigh is better

72.4%

TTFT

Low is better

+57%140 ms

TTFTLow is better

220 ms

Speed

High is better

+69%152 tok/s

SpeedHigh is better

90 tok/s

In $

Low is better

$0.45/1M

In $Low is better

$0.3/1M+50%

Out $

Low is better

$3.2/1M

Out $Low is better

$0.3/1M+967%

Context

High is better

+700%262k

ContextHigh is better

33k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 76% 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)

Qwen3.8 27B$63.75 / mo

In: $15.75Out: $48.00

Yi-Large$15.00 / mo

In: $10.50Out: $4.50

Estimated Cost Delta

Yi-Large is estimated to save $48.75/month ($585/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Qwen3.8 27B:Pick Qwen3.8 27B when repo-level coding accuracy is the constraint.
2
Yi-Large:Pick Yi-Large when you are optimizing output cost.
3
Qwen3.8 27B:Pick Qwen3.8 27B when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsQwen3.8 27B

Higher SWE-bench (58.8%).

High-volume chatYi-Large

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

Voice / low-latency UIQwen3.8 27B

Lower TTFT (140 ms).

Long-document RAGQwen3.8 27B

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	Qwen3.8 27B	Higher SWE-bench (58.8%).
High-volume chat	Yi-Large	Lower output list price ($0.3/1M).
Voice / low-latency UI	Qwen3.8 27B	Lower TTFT (140 ms).
Long-document RAG	Qwen3.8 27B	Larger window (262k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchAug 14, 2026

Qwen3.8 27B (Aug 14): dense enough to self-host, not a frontier MoE

Alibaba open-weight 27B drop. Local/open-weight lists should see it. It will not win frontier-agents.

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LIVE

Alibaba

Qwen3.8 27B

Elo 1,398

LiveBench60.6%

SWE-bench58.8%

Speed152 tok/s

Output cost$3.2/1M

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, Qwen3.8 27B 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, Qwen3.8 27B or Yi-Large?: Qwen3.8 27B leads SWE-bench at 58.8% 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 $3.2/1M. Input prices and retry rates still move the real bill.
Which is faster, Qwen3.8 27B or Yi-Large?: Qwen3.8 27B has the lower time-to-first-token (140 ms vs 220 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Qwen3.8 27B accepts 262k tokens versus 33k.
Can I self-host either model?: Qwen3.8 27B 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. Qwen3.8 27B and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Qwen3.8 27B matchups →|All Yi-Large matchups →

Qwen3.8 27B vs Qwen 3 Max (previous qwen)Yi-Large vs Yi-Lightning (next other)Qwen QwQ 32B vs Yi-Large Qwen 3 Max vs Yi-Large Qwen 2.5 Coder 32B vs Yi-Large Qwen3 235B vs Yi-Large

Qwen3.8 27B vs Yi-Large benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Qwen3.8 27B (7)vsYi-Large (3)

Qwen3.8 27B: 7W (70%)Overall: Qwen3.8 27BYi-Large: 3W (30%)

← Qwen3.8 27BYi-Large →

Qwen3.8 27B (4/5)

Intelligence & Reasoning

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

Preference Elo

1,398vs1,430

Coding Elo

1,422vs1,410

SWE-bench

58.8%vs53.8%

LiveBench

60.6%vs60.5%

GPQA Diamond

73.4%vs72.4%

Qwen3.8 27B: 4WYi-Large: 1W

Qwen3.8 27B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

152 tok/svs90 tok/s

Time to first token

140 msvs220 ms

Qwen3.8 27B: 2WYi-Large: 0W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$3.2/1Mvs$0.3/1M

Input price

$0.45/1Mvs$0.3/1M

Context window

262kvs33k

Qwen3.8 27B: 1WYi-Large: 2W

Recommended Workload Routing

Repo / coding agentsQwen3.8 27B

Higher SWE-bench (58.8%).

High-volume chatYi-Large

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

Voice / low-latency UIQwen3.8 27B

Lower TTFT (140 ms).

Long-document RAGQwen3.8 27B

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	Qwen3.8 27B	Higher SWE-bench (58.8%).
High-volume chat	Yi-Large	Lower output list price ($0.3/1M).
Voice / low-latency UI	Qwen3.8 27B	Lower TTFT (140 ms).
Long-document RAG	Qwen3.8 27B	Larger window (262k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Qwen3.8 27B 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, Qwen3.8 27B or Yi-Large?

Qwen3.8 27B leads SWE-bench at 58.8% 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 $3.2/1M. Input prices and retry rates still move the real bill.

Which is faster, Qwen3.8 27B or Yi-Large?

Qwen3.8 27B has the lower time-to-first-token (140 ms vs 220 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Qwen3.8 27B accepts 262k tokens versus 33k.

Can I self-host either model?

Qwen3.8 27B 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. Qwen3.8 27B and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

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Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

Yi-Large

Qwen3.8 27B

Yi-Large

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Qwen3.8 27B (Aug 14): dense enough to self-host, not a frontier MoE

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Qwen3.8 27B

Yi-Large

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

Yi-Large

Qwen3.8 27B

Yi-Large

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Qwen3.8 27B (Aug 14): dense enough to self-host, not a frontier MoE

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Qwen3.8 27B

Yi-Large

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons