Pairwise benchmark snapshot · Aug 1, 2026

Llama 3.1 405B vs Yi-Large benchmark

Name: Llama 3.1 405B vs Yi-Large AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

In this head-to-head showdown, Yi-Large delivers higher overall intelligence and human-preferred responses (Elo 1,430 vs 1,370), while Llama 3.1 405B leads in SWE-bench software engineering benchmarks (58.4%), while Yi-Large is more budget-friendly at $0.3/1M per 1M output tokens. Review the complete breakdown below to determine which model best fits your performance and budget requirements.

Share Analysis:

Verified Head-to-Head Advantage Breakdown

10 direct benchmark disciplines evaluated across capability, speed, and cost

Llama 3.1 405B (4)Yi-Large (6)

Llama 3.1 405B

4 of 10 Wins

✓LiveBench✓SWE-bench✓GPQA Diamond✓Context window

Yi-Large

6 of 10 Wins

✓Preference Elo✓Coding Elo✓Time to first token✓Output speed✓Input price✓Output price

Yi-Large

Δ 60 Arena Elo pts

Throughput LeaderYi-Large Wins

Yi-Large

90 tok/s

Value per Dollar LeaderYi-Large Wins

Yi-Large

$0.3 / 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 Western standard models with China's leading frontier rival across 6 skill dimensions. Tap any spoke or dot to inspect.

Tap any node to inspect

Percentile 0–100

Head-to-Head Comparison

Overall Matchup Breakdown

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

Llama 3.1 405B (4)vsYi-Large (6)

Llama 3.1 405B: 4W (40%)Overall: Yi-LargeYi-Large: 6W (60%)

← Llama 3.1 405BYi-Large →

🏆Llama 3.1 405B(3/5)

Intelligence & Reasoning

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

Benchmark

Llama 3.1 405BvsYi-Large

Preference Elo

1,370vs1,430+60

Coding Elo

1,385vs1,410+25

SWE-bench

58.4%vs53.8%+4.6%

LiveBench

61.2%vs60.5%+0.7%

GPQA Diamond

75.2%vs72.4%+2.8%

Llama 3.1 405B: 3WYi-Large: 2W

🏆Yi-Large(2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Benchmark

Llama 3.1 405BvsYi-Large

Output speed

42 tok/svs90 tok/s+48 tok/s

Time to first token

360 msvs220 ms+140 ms

Llama 3.1 405B: 0WYi-Large: 2W

🏆Yi-Large(2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Benchmark

Llama 3.1 405BvsYi-Large

Output price

$3.5/1Mvs$0.3/1M+$3.2/1M

Input price

$1.25/1Mvs$0.3/1M+$0.95/1M

Context window

128kvs33k+95k

Llama 3.1 405B: 1WYi-Large: 2W

Side-by-Side Benchmark Matrix

Preference Elo+60

Llama 3.1 405B1,370

seed-bootstrap · Aug 1, 2026

Yi-Large1,430

seed-bootstrap · Aug 1, 2026

Coding Elo+25

Llama 3.1 405B1,385

seed-bootstrap · Aug 1, 2026

Yi-Large1,410

seed-bootstrap · Aug 1, 2026

LiveBench+0.7%

Llama 3.1 405B61.2%

seed-bootstrap · Aug 1, 2026

Yi-Large60.5%

seed-bootstrap · Aug 1, 2026

SWE-bench+4.6%

Llama 3.1 405B58.4%

seed-bootstrap · Aug 1, 2026

Yi-Large53.8%

seed-bootstrap · Aug 1, 2026

GPQA Diamond+2.8%

Llama 3.1 405B75.2%

seed-bootstrap · Aug 1, 2026

Yi-Large72.4%

seed-bootstrap · Aug 1, 2026

Time to first token+140 ms

Llama 3.1 405B360 ms

seed-bootstrap · Aug 1, 2026

Yi-Large220 ms

seed-bootstrap · Aug 1, 2026

Output speed+48 tok/s

Llama 3.1 405B42 tok/s

seed-bootstrap · Aug 1, 2026

Yi-Large90 tok/s

seed-bootstrap · Aug 1, 2026

Input price+$0.95/1M

Llama 3.1 405B$1.25/1M

seed-bootstrap · Aug 1, 2026

Yi-Large$0.3/1M

seed-bootstrap · Aug 1, 2026

Output price+$3.2/1M

Llama 3.1 405B$3.5/1M

seed-bootstrap · Aug 1, 2026

Yi-Large$0.3/1M

seed-bootstrap · Aug 1, 2026

Context window+95k

Llama 3.1 405B128k

seed-bootstrap · Aug 1, 2026

Yi-Large33k

seed-bootstrap · Aug 1, 2026

Benchmark	Llama 3.1 405B	Yi-Large	Advantage Delta
Preference Elo	1,370 seed-bootstrap · Aug 1, 2026	1,430 seed-bootstrap · Aug 1, 2026	+60
Coding Elo	1,385 seed-bootstrap · Aug 1, 2026	1,410 seed-bootstrap · Aug 1, 2026	+25
LiveBench	61.2% seed-bootstrap · Aug 1, 2026	60.5% seed-bootstrap · Aug 1, 2026	+0.7%
SWE-bench	58.4% seed-bootstrap · Aug 1, 2026	53.8% seed-bootstrap · Aug 1, 2026	+4.6%
GPQA Diamond	75.2% seed-bootstrap · Aug 1, 2026	72.4% seed-bootstrap · Aug 1, 2026	+2.8%
Time to first token	360 ms seed-bootstrap · Aug 1, 2026	220 ms seed-bootstrap · Aug 1, 2026	+140 ms
Output speed	42 tok/s seed-bootstrap · Aug 1, 2026	90 tok/s seed-bootstrap · Aug 1, 2026	+48 tok/s
Input price	$1.25/1M seed-bootstrap · Aug 1, 2026	$0.3/1M seed-bootstrap · Aug 1, 2026	+$0.95/1M
Output price	$3.5/1M seed-bootstrap · Aug 1, 2026	$0.3/1M seed-bootstrap · Aug 1, 2026	+$3.2/1M
Context window	128k seed-bootstrap · Aug 1, 2026	33k seed-bootstrap · Aug 1, 2026	+95k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 84% 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)

Llama 3.1 405B$96.25 / mo

In: $43.75Out: $52.5

Yi-Large$15.00 / mo

In: $10.5Out: $4.5

Estimated Cost Delta

Yi-Large is estimated to save $81.25/month ($975/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Llama 3.1 405B:Pick Llama 3.1 405B 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 agentsLlama 3.1 405B

Higher SWE-bench (58.4%).

High-volume chatYi-Large

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

Voice / low-latency UIYi-Large

Lower TTFT (220 ms).

Long-document RAGLlama 3.1 405B

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	Llama 3.1 405B	Higher SWE-bench (58.4%).
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	Llama 3.1 405B	Larger window (128k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Community Sentiment

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CompareLLM AI Matrix

Verified Head-to-Head Telemetry

LIVE BENCHMARK

Llama 3.1 405B

Elo 1,370

SWE-bench58.4%

Speed (tok/s)42 tok/s

Input / 1M Tokens$1.25/1M

Output / 1M Tokens$3.5/1M

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, Llama 3.1 405B 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, Llama 3.1 405B or Yi-Large?: Llama 3.1 405B leads SWE-bench at 58.4% 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 $3.5/1M. Input prices and retry rates still move the real bill.
Which is faster, Llama 3.1 405B or Yi-Large?: Yi-Large has the lower time-to-first-token (220 ms vs 360 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Llama 3.1 405B accepts 128k tokens versus 33k.
Can I self-host either model?: Llama 3.1 405B 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. Llama 3.1 405B and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Llama 3.1 405B matchups →|All Yi-Large matchups →

Llama 3.1 405B vs Llama 3.1 70B (next llama)Yi-Large vs Yi-Lightning (next other)Llama 4 Maverick vs Yi-Large Llama 4 Scout vs Yi-Large Llama 3.3 70B vs Llama 3.1 405B Mistral Large 3 vs Yi-Large

Llama 3.1 405B vs Yi-Large benchmark

Capability Percentiles

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

Multi-Dimensional Capability Radar

Leaders Matchup Capability Radar

Comparing top Western standard models with China's leading frontier rival across 6 skill dimensions. Tap any spoke or dot to inspect.

Tap any node to inspect

Percentile 0–100

Benchmark

Llama 3.1 405B

Yi-Large

Advantage Delta

Preference Elo

1,370

seed-bootstrap · Aug 1, 2026

1,430

seed-bootstrap · Aug 1, 2026

+60

Coding Elo

1,385

seed-bootstrap · Aug 1, 2026

1,410

seed-bootstrap · Aug 1, 2026

+25

LiveBench

61.2%

seed-bootstrap · Aug 1, 2026

60.5%

seed-bootstrap · Aug 1, 2026

+0.7%

SWE-bench

58.4%

seed-bootstrap · Aug 1, 2026

53.8%

seed-bootstrap · Aug 1, 2026

+4.6%

GPQA Diamond

75.2%

seed-bootstrap · Aug 1, 2026

72.4%

seed-bootstrap · Aug 1, 2026

+2.8%

Time to first token

360 ms

seed-bootstrap · Aug 1, 2026

220 ms

seed-bootstrap · Aug 1, 2026

+140 ms

Output speed

42 tok/s

seed-bootstrap · Aug 1, 2026

90 tok/s

seed-bootstrap · Aug 1, 2026

+48 tok/s

Input price

$1.25/1M

seed-bootstrap · Aug 1, 2026

$0.3/1M

seed-bootstrap · Aug 1, 2026

+$0.95/1M

Output price

$3.5/1M

seed-bootstrap · Aug 1, 2026

$0.3/1M

seed-bootstrap · Aug 1, 2026

+$3.2/1M

Context window

128k

seed-bootstrap · Aug 1, 2026

33k

seed-bootstrap · Aug 1, 2026

+95k

Recommended Workload Routing

Repo / coding agentsLlama 3.1 405B

Higher SWE-bench (58.4%).

High-volume chatYi-Large

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

Voice / low-latency UIYi-Large

Lower TTFT (220 ms).

Long-document RAGLlama 3.1 405B

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	Llama 3.1 405B	Higher SWE-bench (58.4%).
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	Llama 3.1 405B	Larger window (128k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Llama 3.1 405B 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, Llama 3.1 405B or Yi-Large?

Llama 3.1 405B leads SWE-bench at 58.4% 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 $3.5/1M. Input prices and retry rates still move the real bill.

Which is faster, Llama 3.1 405B or Yi-Large?

Yi-Large has the lower time-to-first-token (220 ms vs 360 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Llama 3.1 405B accepts 128k tokens versus 33k.

Can I self-host either model?

Llama 3.1 405B 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. Llama 3.1 405B and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.