Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)

OpenAI: o1 (batch) vs Yi-Large benchmark

Name: OpenAI: o1 (batch) vs Yi-Large AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares OpenAI: o1 (batch) (OpenAI) and Yi-Large (01.AI) using the latest snapshots we have as of Aug 17, 2026. Yi-Large wins on output price at $0.3/1M. OpenAI: o1 (batch) has the larger context window (200k 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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OpenAI

OpenAI: o1 (batch)

Auto-discovered from OpenRouter (openai/o1:batch). Preview until a second source matches.

$30/1M 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.

OpenAI: o1 (batch) (1)vsYi-Large (9)

OpenAI: o1 (batch): 1W (10%)Overall: Yi-LargeYi-Large: 9W (90%)

← OpenAI: o1 (batch)Yi-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%

OpenAI: o1 (batch): 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

OpenAI: o1 (batch): 0WYi-Large: 2W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$30/1Mvs$0.3/1M

Input price

$7.5/1Mvs$0.3/1M

Context window

200kvs33k

OpenAI: o1 (batch): 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

$29.70/1M 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

$7.5/1M

In $Low is better

$0.3/1M+2400%

Out $

Low is better

$30/1M

Out $Low is better

$0.3/1M+9900%

Context

High is better

+510%200k

ContextHigh is better

33k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 98% 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)

OpenAI: o1 (batch)$712.50 / mo

In: $262.50Out: $450.00

Yi-Large$15.00 / mo

In: $10.50Out: $4.50

Estimated Cost Delta

Yi-Large is estimated to save $697.50/month ($8,370/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 RAGOpenAI: o1 (batch)

Larger window (200k).

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	OpenAI: o1 (batch)	Larger window (200k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

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LIVE

OpenAI

OpenAI: o1 (batch)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$30/1M

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, OpenAI: o1 (batch) 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, OpenAI: o1 (batch) 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 $30/1M. Input prices and retry rates still move the real bill.
Which is faster, OpenAI: o1 (batch) or Yi-Large?: We do not have TTFT for both models.
Which has the larger context window?: OpenAI: o1 (batch) accepts 200k 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. OpenAI: o1 (batch) and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All OpenAI: o1 (batch) matchups →|All Yi-Large matchups →

Yi-Large vs Yi-Lightning (next other)GPT-5 vs Yi-Large GPT-4.5 Orion vs Yi-Large GPT-5.6 Sol vs Yi-Large GPT-5.6 Terra vs Yi-Large GPT-5 mini vs Yi-Large

OpenAI: o1 (batch) vs Yi-Large benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI: o1 (batch) (1)vsYi-Large (9)

OpenAI: o1 (batch): 1W (10%)Overall: Yi-LargeYi-Large: 9W (90%)

← OpenAI: o1 (batch)Yi-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%

OpenAI: o1 (batch): 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

OpenAI: o1 (batch): 0WYi-Large: 2W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$30/1Mvs$0.3/1M

Input price

$7.5/1Mvs$0.3/1M

Context window

200kvs33k

OpenAI: o1 (batch): 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 RAGOpenAI: o1 (batch)

Larger window (200k).

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	OpenAI: o1 (batch)	Larger window (200k).
Screenshots / vision	Yi-Large	Yi-Large is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, OpenAI: o1 (batch) 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, OpenAI: o1 (batch) 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 $30/1M. Input prices and retry rates still move the real bill.

Which is faster, OpenAI: o1 (batch) or Yi-Large?

We do not have TTFT for both models.

Which has the larger context window?

OpenAI: o1 (batch) accepts 200k 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. OpenAI: o1 (batch) and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.