Pairwise benchmark snapshot · Aug 16, 2026

OpenAI: o1 vs Yi-Large benchmark

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

This page compares OpenAI: o1 (OpenAI) and Yi-Large (01.AI) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, OpenAI: o1 is ahead by 82 points (1,512 vs 1,430; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, Yi-Large resolves 53.8% versus 48.9% (seed-bootstrap (Aug 1, 2026)). Yi-Large streams faster (90 tok/s) while Yi-Large is the cheaper output token ($0.3/1M). OpenAI: o1 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

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

Elo 1,512$60/1M 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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Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI: o1 (5)vsYi-Large (5)

OpenAI: o1: 5W (50%)Overall: Even matchupYi-Large: 5W (50%)

← OpenAI: o1Yi-Large →

OpenAI: o1 (4/5)

Intelligence & Reasoning

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

Preference Elo

1,512vs1,430

Coding Elo

1,485vs1,410

SWE-bench

48.9%vs53.8%

LiveBench

65.4%vs60.5%

GPQA Diamond

78%vs72.4%

OpenAI: o1: 4WYi-Large: 1W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

45 tok/svs90 tok/s

Time to first token

420 msvs220 ms

OpenAI: o1: 0WYi-Large: 2W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$60/1Mvs$0.3/1M

Input price

$15/1Mvs$0.3/1M

Context window

200kvs33k

OpenAI: o1: 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 AdvantageOpenAI: o1 4/6

Preference Elo

OpenAI: o1

82 pts advantage

Throughput Speed

Yi-Large

45 tok/s faster

Price Efficiency

Yi-Large

$59.70/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+6%1,512

EloHigh is better

1,430

Code Elo

High is better

+5%1,485

Code EloHigh is better

1,410

LiveBench

High is better

+8%65.4%

LiveBenchHigh is better

60.5%

SWE-bench

High is better

48.9%

SWE-benchHigh is better

53.8%+10%

GPQA

High is better

+8%78%

GPQAHigh is better

72.4%

TTFT

Low is better

420 ms

TTFTLow is better

220 ms+91%

Speed

High is better

45 tok/s

SpeedHigh is better

90 tok/s+100%

In $

Low is better

$15/1M

In $Low is better

$0.3/1M+4900%

Out $

Low is better

$60/1M

Out $Low is better

$0.3/1M+19900%

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 99% 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$1,425.00 / mo

In: $525.00Out: $900.00

Yi-Large$15.00 / mo

In: $10.50Out: $4.50

Estimated Cost Delta

Yi-Large is estimated to save $1,410.00/month ($16,920/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
Yi-Large:Pick Yi-Large when time-to-first-token matters more than peak Elo.
3
OpenAI: o1:Pick OpenAI: o1 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 UIYi-Large

Lower TTFT (220 ms).

Long-document RAGOpenAI: o1

Larger window (200k).

Screenshots / visionOpenAI: o1

OpenAI: o1 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	Yi-Large	Lower TTFT (220 ms).
Long-document RAG	OpenAI: o1	Larger window (200k).
Screenshots / vision	OpenAI: o1	OpenAI: o1 is the side marked multimodal in the catalog.

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LIVE

OpenAI

OpenAI: o1

Elo 1,512

LiveBench65.4%

SWE-bench48.9%

Speed45 tok/s

Output cost$60/1M

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 or Yi-Large?: OpenAI: o1 has the higher preference Elo in our latest snapshot (1,512). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, OpenAI: o1 or Yi-Large?: Yi-Large leads SWE-bench at 53.8% vs 48.9%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Yi-Large output tokens are $0.3/1M versus $60/1M. Input prices and retry rates still move the real bill.
Which is faster, OpenAI: o1 or Yi-Large?: Yi-Large has the lower time-to-first-token (220 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: OpenAI: o1 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 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 matchups →|All Yi-Large matchups →

OpenAI: o1 vs GPT-4o (previous gpt-flagship)OpenAI: o1 vs GPT-4.5 Orion (next gpt-flagship)Yi-Large vs Yi-Lightning (next other)GPT-5.6 Terra vs OpenAI: o1 Yi-Lightning vs OpenAI: o1 GPT-5 vs OpenAI: o1

OpenAI: o1 vs Yi-Large benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI: o1 (5)vsYi-Large (5)

OpenAI: o1: 5W (50%)Overall: Even matchupYi-Large: 5W (50%)

← OpenAI: o1Yi-Large →

OpenAI: o1 (4/5)

Intelligence & Reasoning

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

Preference Elo

1,512vs1,430

Coding Elo

1,485vs1,410

SWE-bench

48.9%vs53.8%

LiveBench

65.4%vs60.5%

GPQA Diamond

78%vs72.4%

OpenAI: o1: 4WYi-Large: 1W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

45 tok/svs90 tok/s

Time to first token

420 msvs220 ms

OpenAI: o1: 0WYi-Large: 2W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$60/1Mvs$0.3/1M

Input price

$15/1Mvs$0.3/1M

Context window

200kvs33k

OpenAI: o1: 1WYi-Large: 2W

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 UIYi-Large

Lower TTFT (220 ms).

Long-document RAGOpenAI: o1

Larger window (200k).

Screenshots / visionOpenAI: o1

OpenAI: o1 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	Yi-Large	Lower TTFT (220 ms).
Long-document RAG	OpenAI: o1	Larger window (200k).
Screenshots / vision	OpenAI: o1	OpenAI: o1 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, OpenAI: o1 or Yi-Large?

OpenAI: o1 has the higher preference Elo in our latest snapshot (1,512). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, OpenAI: o1 or Yi-Large?

Yi-Large leads SWE-bench at 53.8% vs 48.9%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Yi-Large output tokens are $0.3/1M versus $60/1M. Input prices and retry rates still move the real bill.

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

Yi-Large has the lower time-to-first-token (220 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

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