Pairwise benchmark snapshot · Aug 16, 2026

DeepSeek R1 vs Yi-Large benchmark

Name: DeepSeek R1 vs Yi-Large AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares DeepSeek R1 (DeepSeek) and Yi-Large (01.AI) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, Yi-Large is ahead by 72 points (1,430 vs 1,358; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, DeepSeek R1 resolves 65.2% 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). DeepSeek R1 has the larger context window (64k 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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DeepSeek

DeepSeek R1

Open-weights reasoning model trained with large-scale RL.

Elo 1,358$2.5/1M 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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Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek R1 (3)vsYi-Large (7)

DeepSeek R1: 3W (30%)Overall: Yi-LargeYi-Large: 7W (70%)

← DeepSeek R1Yi-Large →

Yi-Large (3/5)

Intelligence & Reasoning

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

Preference Elo

1,358vs1,430

Coding Elo

—vs1,410

SWE-bench

65.2%vs53.8%

LiveBench

59%vs60.5%

GPQA Diamond

79.8%vs72.4%

DeepSeek R1: 2WYi-Large: 3W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

62 tok/svs90 tok/s

Time to first token

420 msvs220 ms

DeepSeek R1: 0WYi-Large: 2W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.5/1Mvs$0.3/1M

Input price

$0.7/1Mvs$0.3/1M

Context window

64kvs33k

DeepSeek R1: 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 AdvantageDeepSeek R1 4/6

Preference Elo

Yi-Large

72 pts advantage

Throughput Speed

Yi-Large

28 tok/s faster

Price Efficiency

Yi-Large

$2.20/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,358

EloHigh is better

1,430+5%

Code Elo

High is better

—

Code EloHigh is better

1,410

LiveBench

High is better

59%

LiveBenchHigh is better

60.5%+3%

SWE-bench

High is better

+21%65.2%

SWE-benchHigh is better

53.8%

GPQA

High is better

+10%79.8%

GPQAHigh is better

72.4%

TTFT

Low is better

420 ms

TTFTLow is better

220 ms+91%

Speed

High is better

62 tok/s

SpeedHigh is better

90 tok/s+45%

In $

Low is better

$0.7/1M

In $Low is better

$0.3/1M+133%

Out $

Low is better

$2.5/1M

Out $Low is better

$0.3/1M+733%

Context

High is better

+95%64k

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)

DeepSeek R1$62.00 / mo

In: $24.50Out: $37.50

Yi-Large$15.00 / mo

In: $10.50Out: $4.50

Estimated Cost Delta

Yi-Large is estimated to save $47.00/month ($564/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
DeepSeek R1:Pick DeepSeek R1 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 agentsDeepSeek R1

Higher SWE-bench (65.2%).

High-volume chatYi-Large

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

Voice / low-latency UIYi-Large

Lower TTFT (220 ms).

Long-document RAGDeepSeek R1

Larger window (64k).

Screenshots / visionYi-Large

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

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	DeepSeek R1	Higher SWE-bench (65.2%).
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	DeepSeek R1	Larger window (64k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchJan 20, 2025

DeepSeek R1 stays as the open-weights RL baseline

Jan 2025 reasoning model. Still searched. V4 Pro is the newer DeepSeek buy.

Community Sentiment

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LIVE

DeepSeek

DeepSeek R1

Elo 1,358

LiveBench59%

SWE-bench65.2%

Speed62 tok/s

Output cost$2.5/1M

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, DeepSeek R1 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, DeepSeek R1 or Yi-Large?: DeepSeek R1 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 $2.5/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek R1 or Yi-Large?: Yi-Large has the lower time-to-first-token (220 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: DeepSeek R1 accepts 64k tokens versus 33k.
Can I self-host either model?: DeepSeek R1 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. DeepSeek R1 and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All DeepSeek R1 matchups →|All Yi-Large matchups →

DeepSeek R1 vs DeepSeek V3 (previous deepseek)DeepSeek R1 vs DeepSeek V4 Pro (next deepseek)Yi-Large vs Yi-Lightning (next other)DeepSeek V4 Flash vs Yi-Large DeepSeek V4 Pro vs Yi-Large DeepSeek Coder V2 vs DeepSeek R1

DeepSeek R1 vs Yi-Large benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek R1 (3)vsYi-Large (7)

DeepSeek R1: 3W (30%)Overall: Yi-LargeYi-Large: 7W (70%)

← DeepSeek R1Yi-Large →

Yi-Large (3/5)

Intelligence & Reasoning

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

Preference Elo

1,358vs1,430

Coding Elo

—vs1,410

SWE-bench

65.2%vs53.8%

LiveBench

59%vs60.5%

GPQA Diamond

79.8%vs72.4%

DeepSeek R1: 2WYi-Large: 3W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

62 tok/svs90 tok/s

Time to first token

420 msvs220 ms

DeepSeek R1: 0WYi-Large: 2W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.5/1Mvs$0.3/1M

Input price

$0.7/1Mvs$0.3/1M

Context window

64kvs33k

DeepSeek R1: 1WYi-Large: 2W

Recommended Workload Routing

Repo / coding agentsDeepSeek R1

Higher SWE-bench (65.2%).

High-volume chatYi-Large

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

Voice / low-latency UIYi-Large

Lower TTFT (220 ms).

Long-document RAGDeepSeek R1

Larger window (64k).

Screenshots / visionYi-Large

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

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	DeepSeek R1	Higher SWE-bench (65.2%).
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	DeepSeek R1	Larger window (64k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, DeepSeek R1 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, DeepSeek R1 or Yi-Large?

DeepSeek R1 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 $2.5/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek R1 or Yi-Large?

Yi-Large has the lower time-to-first-token (220 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

DeepSeek R1 accepts 64k tokens versus 33k.

Can I self-host either model?

DeepSeek R1 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. DeepSeek R1 and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

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

Yi-Large

Yi-Large

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

DeepSeek R1 stays as the open-weights RL baseline

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

DeepSeek R1

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

Yi-Large

Yi-Large

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

DeepSeek R1 stays as the open-weights RL baseline

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

DeepSeek R1

Yi-Large

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons