Pairwise benchmark snapshot · Aug 17, 2026

DeepSeek Coder V2 vs Yi-Large benchmark

Name: DeepSeek Coder V2 vs Yi-Large AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares DeepSeek Coder V2 (DeepSeek) and Yi-Large (01.AI) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, Yi-Large is ahead by 65 points (1,430 vs 1,365; lmarena (Aug 17, 2026)). On SWE-bench coding, DeepSeek Coder V2 resolves 60.5% versus 53.8% (swebench (Aug 17, 2026)). Yi-Large streams faster (90 tok/s) while DeepSeek Coder V2 is the cheaper output token ($0.28/1M). DeepSeek Coder V2 has the larger context window (128k 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 Coder V2

DeepSeek open-weight Mixture-of-Experts coding model supporting 338 programming languages and 128k context.

Elo 1,365$0.28/1M 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.

DeepSeek Coder V2 (5)vsYi-Large (5)

DeepSeek Coder V2: 5W (50%)Overall: Even matchupYi-Large: 5W (50%)

← DeepSeek Coder V2Yi-Large →

Yi-Large (3/5)

Intelligence & Reasoning

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

Preference Elo

1,365vs1,430

Coding Elo

1,442vs1,410

SWE-bench

60.5%vs53.8%

LiveBench

58.6%vs60.5%

GPQA Diamond

70.2%vs72.4%

DeepSeek Coder V2: 2WYi-Large: 3W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

75 tok/svs90 tok/s

Time to first token

290 msvs220 ms

DeepSeek Coder V2: 0WYi-Large: 2W

DeepSeek Coder V2 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.28/1Mvs$0.3/1M

Input price

$0.14/1Mvs$0.3/1M

Context window

128kvs33k

DeepSeek Coder V2: 3WYi-Large: 0W

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 AdvantageEven 3–3

Preference Elo

Yi-Large

65 pts advantage

Throughput Speed

Yi-Large

15 tok/s faster

Price Efficiency

DeepSeek Coder V2

$0.02/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,365

EloHigh is better

1,430+5%

Code Elo

High is better

+2%1,442

Code EloHigh is better

1,410

LiveBench

High is better

58.6%

LiveBenchHigh is better

60.5%+3%

SWE-bench

High is better

+12%60.5%

SWE-benchHigh is better

53.8%

GPQA

High is better

70.2%

GPQAHigh is better

72.4%+3%

TTFT

Low is better

290 ms

TTFTLow is better

220 ms+32%

Speed

High is better

75 tok/s

SpeedHigh is better

90 tok/s+20%

In $

Low is better

+114%$0.14/1M

In $Low is better

$0.3/1M

Out $

Low is better

+7%$0.28/1M

Out $Low is better

$0.3/1M

Context

High is better

+291%128k

ContextHigh is better

33k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 39% with DeepSeek Coder V2

Monthly Volume50M tokens/mo

Quick Presets:

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

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

DeepSeek Coder V2$9.10 / mo

In: $4.90Out: $4.20

Yi-Large$15.00 / mo

In: $10.50Out: $4.50

Estimated Cost Delta

DeepSeek Coder V2 is estimated to save $5.90/month ($71/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
DeepSeek Coder V2:Pick DeepSeek Coder V2 when repo-level coding accuracy is the constraint.
2
Yi-Large:Pick Yi-Large when time-to-first-token matters more than peak Elo.
3
Yi-Large:Pick Yi-Large as the default general assistant.

Recommended Workload Routing

Repo / coding agentsDeepSeek Coder V2

Higher SWE-bench (60.5%).

High-volume chatDeepSeek Coder V2

Lower output list price ($0.28/1M).

Voice / low-latency UIYi-Large

Lower TTFT (220 ms).

Long-document RAGDeepSeek Coder V2

Larger window (128k).

Screenshots / visionYi-Large

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

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	DeepSeek Coder V2	Higher SWE-bench (60.5%).
High-volume chat	DeepSeek Coder V2	Lower output list price ($0.28/1M).
Voice / low-latency UI	Yi-Large	Lower TTFT (220 ms).
Long-document RAG	DeepSeek Coder V2	Larger window (128k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Community Sentiment

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LIVE

DeepSeek

DeepSeek Coder V2

Elo 1,365

LiveBench58.6%

SWE-bench60.5%

Speed75 tok/s

Output cost$0.28/1M

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 Coder V2 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 Coder V2 or Yi-Large?: DeepSeek Coder V2 leads SWE-bench at 60.5% vs 53.8%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: DeepSeek Coder V2 output tokens are $0.28/1M versus $0.3/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek Coder V2 or Yi-Large?: Yi-Large has the lower time-to-first-token (220 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: DeepSeek Coder V2 accepts 128k tokens versus 33k.
Can I self-host either model?: DeepSeek Coder V2 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 Coder V2 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 Coder V2 matchups →|All Yi-Large matchups →

DeepSeek Coder V2 vs DeepSeek V3 (next deepseek)Yi-Large vs Yi-Lightning (next other)DeepSeek V4 Flash vs Yi-Large DeepSeek V4 Pro vs Yi-Large DeepSeek R1 vs DeepSeek Coder V2 Mistral Large 3 vs Yi-Large

DeepSeek Coder V2 vs Yi-Large benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek Coder V2 (5)vsYi-Large (5)

DeepSeek Coder V2: 5W (50%)Overall: Even matchupYi-Large: 5W (50%)

← DeepSeek Coder V2Yi-Large →

Yi-Large (3/5)

Intelligence & Reasoning

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

Preference Elo

1,365vs1,430

Coding Elo

1,442vs1,410

SWE-bench

60.5%vs53.8%

LiveBench

58.6%vs60.5%

GPQA Diamond

70.2%vs72.4%

DeepSeek Coder V2: 2WYi-Large: 3W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

75 tok/svs90 tok/s

Time to first token

290 msvs220 ms

DeepSeek Coder V2: 0WYi-Large: 2W

DeepSeek Coder V2 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.28/1Mvs$0.3/1M

Input price

$0.14/1Mvs$0.3/1M

Context window

128kvs33k

DeepSeek Coder V2: 3WYi-Large: 0W

Recommended Workload Routing

Repo / coding agentsDeepSeek Coder V2

Higher SWE-bench (60.5%).

High-volume chatDeepSeek Coder V2

Lower output list price ($0.28/1M).

Voice / low-latency UIYi-Large

Lower TTFT (220 ms).

Long-document RAGDeepSeek Coder V2

Larger window (128k).

Screenshots / visionYi-Large

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

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	DeepSeek Coder V2	Higher SWE-bench (60.5%).
High-volume chat	DeepSeek Coder V2	Lower output list price ($0.28/1M).
Voice / low-latency UI	Yi-Large	Lower TTFT (220 ms).
Long-document RAG	DeepSeek Coder V2	Larger window (128k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, DeepSeek Coder V2 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 Coder V2 or Yi-Large?

DeepSeek Coder V2 leads SWE-bench at 60.5% vs 53.8%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

DeepSeek Coder V2 output tokens are $0.28/1M versus $0.3/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek Coder V2 or Yi-Large?

Yi-Large has the lower time-to-first-token (220 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

DeepSeek Coder V2 accepts 128k tokens versus 33k.

Can I self-host either model?

DeepSeek Coder V2 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 Coder V2 and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.