Pairwise benchmark snapshot · Aug 16, 2026noindex (thin pair)

Sao10K: Llama 3.1 Euryale 70B v2.2 vs Yi-Large benchmark

Name: Sao10K: Llama 3.1 Euryale 70B v2.2 vs Yi-Large AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Sao10K: Llama 3.1 Euryale 70B v2.2 (sao10k) and Yi-Large (01.AI) using the latest snapshots we have as of Aug 16, 2026. Yi-Large wins on output price at $0.3/1M. Sao10K: Llama 3.1 Euryale 70B v2.2 has the larger context window (131k 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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sao10k

Sao10K: Llama 3.1 Euryale 70B v2.2

Auto-discovered from OpenRouter (sao10k/l3.1-euryale-70b). Preview until a second source matches.

$0.85/1M 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.

Sao10K: Llama 3.1 Euryale 70B v2.2 (1)vsYi-Large (9)

Sao10K: Llama 3.1 Euryale 70B v2.2: 1W (10%)Overall: Yi-LargeYi-Large: 9W (90%)

← Sao10K: Llama 3.1 Euryale 70B v2.2Yi-Large →

Yi-Large (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,430

Coding Elo

—vs1,410

SWE-bench

—vs53.8%

LiveBench

—vs60.5%

GPQA Diamond

—vs72.4%

Sao10K: Llama 3.1 Euryale 70B v2.2: 0WYi-Large: 5W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs90 tok/s

Time to first token

—vs220 ms

Sao10K: Llama 3.1 Euryale 70B v2.2: 0WYi-Large: 2W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.85/1Mvs$0.3/1M

Input price

$0.85/1Mvs$0.3/1M

Context window

131kvs33k

Sao10K: Llama 3.1 Euryale 70B v2.2: 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 AdvantageYi-Large 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

Yi-Large

$0.55/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,430

Code Elo

High is better

—

Code EloHigh is better

1,410

LiveBench

High is better

—

LiveBenchHigh is better

60.5%

SWE-bench

High is better

—

SWE-benchHigh is better

53.8%

GPQA

High is better

—

GPQAHigh is better

72.4%

TTFT

Low is better

—

TTFTLow is better

220 ms

Speed

High is better

—

SpeedHigh is better

90 tok/s

In $

Low is better

$0.85/1M

In $Low is better

$0.3/1M+183%

Out $

Low is better

$0.85/1M

Out $Low is better

$0.3/1M+183%

Context

High is better

+300%131k

ContextHigh is better

33k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 65% 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)

Sao10K: Llama 3.1 Euryale 70B v2.2$42.50 / mo

In: $29.75Out: $12.75

Yi-Large$15.00 / mo

In: $10.50Out: $4.50

Estimated Cost Delta

Yi-Large is estimated to save $27.50/month ($330/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Yi-Large:Pick Yi-Large when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatYi-Large

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

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGSao10K: Llama 3.1 Euryale 70B v2.2

Larger window (131k).

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	Yi-Large	Lower output list price ($0.3/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Sao10K: Llama 3.1 Euryale 70B v2.2	Larger window (131k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

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LIVE

sao10k

Sao10K: Llama 3.1 Euryale 70B v2.2

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$0.85/1M

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, Sao10K: Llama 3.1 Euryale 70B v2.2 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, Sao10K: Llama 3.1 Euryale 70B v2.2 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?: Yi-Large output tokens are $0.3/1M versus $0.85/1M. Input prices and retry rates still move the real bill.
Which is faster, Sao10K: Llama 3.1 Euryale 70B v2.2 or Yi-Large?: We do not have TTFT for both models.
Which has the larger context window?: Sao10K: Llama 3.1 Euryale 70B v2.2 accepts 131k 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. Sao10K: Llama 3.1 Euryale 70B v2.2 and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Sao10K: Llama 3.1 Euryale 70B v2.2 matchups →|All Yi-Large matchups →

Yi-Large vs Yi-Lightning (next other)Claude Opus 4.5 vs Yi-Large Claude Opus 4.6 vs Yi-Large Claude Sonnet 4.5 vs Yi-Large GPT-5 vs Yi-Large Gemini 3 Pro vs Yi-Large

Sao10K: Llama 3.1 Euryale 70B v2.2 vs Yi-Large benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Sao10K: Llama 3.1 Euryale 70B v2.2 (1)vsYi-Large (9)

Sao10K: Llama 3.1 Euryale 70B v2.2: 1W (10%)Overall: Yi-LargeYi-Large: 9W (90%)

← Sao10K: Llama 3.1 Euryale 70B v2.2Yi-Large →

Yi-Large (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,430

Coding Elo

—vs1,410

SWE-bench

—vs53.8%

LiveBench

—vs60.5%

GPQA Diamond

—vs72.4%

Sao10K: Llama 3.1 Euryale 70B v2.2: 0WYi-Large: 5W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs90 tok/s

Time to first token

—vs220 ms

Sao10K: Llama 3.1 Euryale 70B v2.2: 0WYi-Large: 2W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.85/1Mvs$0.3/1M

Input price

$0.85/1Mvs$0.3/1M

Context window

131kvs33k

Sao10K: Llama 3.1 Euryale 70B v2.2: 1WYi-Large: 2W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatYi-Large

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

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGSao10K: Llama 3.1 Euryale 70B v2.2

Larger window (131k).

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	Yi-Large	Lower output list price ($0.3/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Sao10K: Llama 3.1 Euryale 70B v2.2	Larger window (131k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Sao10K: Llama 3.1 Euryale 70B v2.2 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, Sao10K: Llama 3.1 Euryale 70B v2.2 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?

Yi-Large output tokens are $0.3/1M versus $0.85/1M. Input prices and retry rates still move the real bill.

Which is faster, Sao10K: Llama 3.1 Euryale 70B v2.2 or Yi-Large?

We do not have TTFT for both models.

Which has the larger context window?

Sao10K: Llama 3.1 Euryale 70B v2.2 accepts 131k 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. Sao10K: Llama 3.1 Euryale 70B v2.2 and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.