Pairwise benchmark snapshot · Aug 17, 2026

Llama 3.3 70B vs Yi-Large benchmark

Name: Llama 3.3 70B vs Yi-Large AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Llama 3.3 70B (Meta) and Yi-Large (01.AI) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, Yi-Large is ahead by 145 points (1,430 vs 1,285; lmarena (Aug 17, 2026)). On SWE-bench coding, Yi-Large resolves 53.8% versus 45.1% (swebench (Aug 17, 2026)). Llama 3.3 70B streams faster (140 tok/s) while Yi-Large is the cheaper output token ($0.3/1M). Llama 3.3 70B 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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Model A

Model B

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Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Llama 3.3 70B (4)vsYi-Large (6)

Llama 3.3 70B: 4W (40%)Overall: Yi-LargeYi-Large: 6W (60%)

← Llama 3.3 70BYi-Large →

Yi-Large (5/5)

Intelligence & Reasoning

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

Preference Elo

1,285vs1,430

Coding Elo

—vs1,410

SWE-bench

45.1%vs53.8%

LiveBench

49.8%vs60.5%

GPQA Diamond

65.7%vs72.4%

Llama 3.3 70B: 0WYi-Large: 5W

Llama 3.3 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

140 tok/svs90 tok/s

Time to first token

160 msvs220 ms

Llama 3.3 70B: 2WYi-Large: 0W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.32/1Mvs$0.3/1M

Input price

$0.1/1Mvs$0.3/1M

Context window

131kvs33k

Llama 3.3 70B: 2WYi-Large: 1W

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 4/6

Preference Elo

Yi-Large

145 pts advantage

Throughput Speed

Llama 3.3 70B

50 tok/s faster

Token Price Efficiency

Yi-Large

$0.02/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,285

EloHigh is better

1,430+11%

Code Elo

High is better

—

Code EloHigh is better

1,410

LiveBench

High is better

49.8%

LiveBenchHigh is better

60.5%+21%

SWE-bench

High is better

45.1%

SWE-benchHigh is better

53.8%+19%

GPQA

High is better

65.7%

GPQAHigh is better

72.4%+10%

TTFT

Low is better

+38%160 ms

TTFTLow is better

220 ms

Speed

High is better

+56%140 tok/s

SpeedHigh is better

90 tok/s

In $

Low is better

+200%$0.10 / 1M

In $Low is better

$0.30 / 1M

Out $

Low is better

$0.32 / 1M

Out $Low is better

$0.30 / 1M+7%

Context

High is better

+300%131k

ContextHigh is better

33k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 45% with Llama 3.3 70B

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Llama 3.3 70B$8.30 / mo

In: $3.5Out: $4.8

Yi-Large$15.00 / mo

In: $10.5Out: $4.5

Estimated Cost Delta

Llama 3.3 70B is estimated to save $6.70/month ($80/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Yi-Large:Pick Yi-Large when repo-level coding accuracy is the constraint.
2
Llama 3.3 70B:Pick Llama 3.3 70B 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 agentsYi-Large

Higher SWE-bench (53.8%).

High-volume chatYi-Large

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

Voice / low-latency UILlama 3.3 70B

Lower TTFT (160 ms).

Long-document RAGLlama 3.3 70B

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	Yi-Large	Higher SWE-bench (53.8%).
High-volume chat	Yi-Large	Lower output list price ($0.3/1M).
Voice / low-latency UI	Llama 3.3 70B	Lower TTFT (160 ms).
Long-document RAG	Llama 3.3 70B	Larger window (131k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchDec 6, 2024

Llama 3.3 70B stays as the previous Meta 70B workhorse

Dec 2024 instruct 70B. Still a self-host baseline. Llama 4 is the 2026 buy.

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LIVE

Llama 3.3 70B

Elo 1,285

LiveBench49.8%

SWE-bench45.1%

Speed140 tok/s

Output cost$0.32/1M

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, Llama 3.3 70B 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 3.3 70B or Yi-Large?: Yi-Large leads SWE-bench at 53.8% vs 45.1%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Yi-Large output tokens are $0.3/1M versus $0.32/1M. Input prices and retry rates still move the real bill.
Which is faster, Llama 3.3 70B or Yi-Large?: Llama 3.3 70B has the lower time-to-first-token (160 ms vs 220 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Llama 3.3 70B accepts 131k tokens versus 33k.
Can I self-host either model?: Llama 3.3 70B 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 3.3 70B 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 3.3 70B matchups →|All Yi-Large matchups →

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

Llama 3.3 70B vs Yi-Large benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Llama 3.3 70B (4)vsYi-Large (6)

Llama 3.3 70B: 4W (40%)Overall: Yi-LargeYi-Large: 6W (60%)

← Llama 3.3 70BYi-Large →

Yi-Large (5/5)

Intelligence & Reasoning

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

Preference Elo

1,285vs1,430

Coding Elo

—vs1,410

SWE-bench

45.1%vs53.8%

LiveBench

49.8%vs60.5%

GPQA Diamond

65.7%vs72.4%

Llama 3.3 70B: 0WYi-Large: 5W

Llama 3.3 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

140 tok/svs90 tok/s

Time to first token

160 msvs220 ms

Llama 3.3 70B: 2WYi-Large: 0W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.32/1Mvs$0.3/1M

Input price

$0.1/1Mvs$0.3/1M

Context window

131kvs33k

Llama 3.3 70B: 2WYi-Large: 1W

Recommended Workload Routing

Repo / coding agentsYi-Large

Higher SWE-bench (53.8%).

High-volume chatYi-Large

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

Voice / low-latency UILlama 3.3 70B

Lower TTFT (160 ms).

Long-document RAGLlama 3.3 70B

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	Yi-Large	Higher SWE-bench (53.8%).
High-volume chat	Yi-Large	Lower output list price ($0.3/1M).
Voice / low-latency UI	Llama 3.3 70B	Lower TTFT (160 ms).
Long-document RAG	Llama 3.3 70B	Larger window (131k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Llama 3.3 70B 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 3.3 70B or Yi-Large?

Yi-Large leads SWE-bench at 53.8% vs 45.1%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Yi-Large output tokens are $0.3/1M versus $0.32/1M. Input prices and retry rates still move the real bill.

Which is faster, Llama 3.3 70B or Yi-Large?

Llama 3.3 70B has the lower time-to-first-token (160 ms vs 220 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Llama 3.3 70B accepts 131k tokens versus 33k.

Can I self-host either model?

Llama 3.3 70B 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 3.3 70B 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

Llama 3.3 70B

Yi-Large

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Llama 3.3 70B stays as the previous Meta 70B workhorse

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CompareLLM Matrix

Llama 3.3 70B

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

Llama 3.3 70B

Yi-Large

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Llama 3.3 70B stays as the previous Meta 70B workhorse

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Llama 3.3 70B

Yi-Large

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons