Pairwise benchmark snapshot · Aug 17, 2026

OpenAI o1-mini vs Yi-Large benchmark

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

This page compares OpenAI o1-mini (OpenAI) and Yi-Large (01.AI) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, OpenAI o1-mini is ahead by 15 points (1,445 vs 1,430; lmarena (Aug 17, 2026)). On SWE-bench coding, OpenAI o1-mini resolves 56.4% versus 53.8% (swebench (Aug 17, 2026)). Yi-Large streams faster (90 tok/s) while Yi-Large is the cheaper output token ($0.3/1M). OpenAI o1-mini has the larger context window (128k 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-mini

OpenAI high-speed, cost-effective reasoning model optimized for STEM, math, and code generation.

Elo 1,445$4.4/1M 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-mini (6)vsYi-Large (4)

OpenAI o1-mini: 6W (60%)Overall: OpenAI o1-miniYi-Large: 4W (40%)

← OpenAI o1-miniYi-Large →

OpenAI o1-mini (5/5)

Intelligence & Reasoning

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

Preference Elo

1,445vs1,430

Coding Elo

1,468vs1,410

SWE-bench

56.4%vs53.8%

LiveBench

62%vs60.5%

GPQA Diamond

76.8%vs72.4%

OpenAI o1-mini: 5WYi-Large: 0W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

85 tok/svs90 tok/s

Time to first token

280 msvs220 ms

OpenAI o1-mini: 0WYi-Large: 2W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4.4/1Mvs$0.3/1M

Input price

$1.1/1Mvs$0.3/1M

Context window

128kvs33k

OpenAI o1-mini: 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.

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Percentile 0–100

Dimensional AdvantageOpenAI o1-mini 4/6

Preference Elo

OpenAI o1-mini

15 pts advantage

Throughput Speed

Yi-Large

5 tok/s faster

Price Efficiency

Yi-Large

$4.10/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+1%1,445

EloHigh is better

1,430

Code Elo

High is better

+4%1,468

Code EloHigh is better

1,410

LiveBench

High is better

+2%62%

LiveBenchHigh is better

60.5%

SWE-bench

High is better

+5%56.4%

SWE-benchHigh is better

53.8%

GPQA

High is better

+6%76.8%

GPQAHigh is better

72.4%

TTFT

Low is better

280 ms

TTFTLow is better

220 ms+27%

Speed

High is better

85 tok/s

SpeedHigh is better

90 tok/s+6%

In $

Low is better

$1.1/1M

In $Low is better

$0.3/1M+267%

Out $

Low is better

$4.4/1M

Out $Low is better

$0.3/1M+1367%

Context

High is better

+291%128k

ContextHigh is better

33k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 86% 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-mini$104.50 / mo

In: $38.50Out: $66.00

Yi-Large$15.00 / mo

In: $10.50Out: $4.50

Estimated Cost Delta

Yi-Large is estimated to save $89.50/month ($1,074/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
OpenAI o1-mini:Pick OpenAI o1-mini 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 agentsOpenAI o1-mini

Higher SWE-bench (56.4%).

High-volume chatYi-Large

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

Voice / low-latency UIYi-Large

Lower TTFT (220 ms).

Long-document RAGOpenAI o1-mini

Larger window (128k).

Screenshots / visionOpenAI o1-mini

OpenAI o1-mini is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	OpenAI o1-mini	Higher SWE-bench (56.4%).
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	OpenAI o1-mini	Larger window (128k).
Screenshots / vision	OpenAI o1-mini	OpenAI o1-mini is the side marked multimodal in the catalog.

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LIVE

OpenAI

OpenAI o1-mini

Elo 1,445

LiveBench62%

SWE-bench56.4%

Speed85 tok/s

Output cost$4.4/1M

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-mini or Yi-Large?: OpenAI o1-mini has the higher preference Elo in our latest snapshot (1,445). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, OpenAI o1-mini or Yi-Large?: OpenAI o1-mini leads SWE-bench at 56.4% 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 $4.4/1M. Input prices and retry rates still move the real bill.
Which is faster, OpenAI o1-mini or Yi-Large?: Yi-Large has the lower time-to-first-token (220 ms vs 280 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: OpenAI o1-mini accepts 128k 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-mini 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-mini matchups →|All Yi-Large matchups →

OpenAI o1-mini vs GPT-4o mini (previous gpt-mini)OpenAI o1-mini vs OpenAI: o3 Mini (next gpt-mini)Yi-Large vs Yi-Lightning (next other)Yi-Lightning vs OpenAI o1-mini GPT-4.5 Orion vs OpenAI o1-mini GPT-5.6 Terra vs OpenAI o1-mini

OpenAI o1-mini vs Yi-Large benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI o1-mini (6)vsYi-Large (4)

OpenAI o1-mini: 6W (60%)Overall: OpenAI o1-miniYi-Large: 4W (40%)

← OpenAI o1-miniYi-Large →

OpenAI o1-mini (5/5)

Intelligence & Reasoning

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

Preference Elo

1,445vs1,430

Coding Elo

1,468vs1,410

SWE-bench

56.4%vs53.8%

LiveBench

62%vs60.5%

GPQA Diamond

76.8%vs72.4%

OpenAI o1-mini: 5WYi-Large: 0W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

85 tok/svs90 tok/s

Time to first token

280 msvs220 ms

OpenAI o1-mini: 0WYi-Large: 2W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4.4/1Mvs$0.3/1M

Input price

$1.1/1Mvs$0.3/1M

Context window

128kvs33k

OpenAI o1-mini: 1WYi-Large: 2W

Recommended Workload Routing

Repo / coding agentsOpenAI o1-mini

Higher SWE-bench (56.4%).

High-volume chatYi-Large

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

Voice / low-latency UIYi-Large

Lower TTFT (220 ms).

Long-document RAGOpenAI o1-mini

Larger window (128k).

Screenshots / visionOpenAI o1-mini

OpenAI o1-mini is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	OpenAI o1-mini	Higher SWE-bench (56.4%).
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	OpenAI o1-mini	Larger window (128k).
Screenshots / vision	OpenAI o1-mini	OpenAI o1-mini is the side marked multimodal in the catalog.

Frequently Asked Questions

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

OpenAI o1-mini has the higher preference Elo in our latest snapshot (1,445). “Better” still depends on coding, price, and latency — see the table.

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

OpenAI o1-mini leads SWE-bench at 56.4% 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 $4.4/1M. Input prices and retry rates still move the real bill.

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

Yi-Large has the lower time-to-first-token (220 ms vs 280 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

OpenAI o1-mini accepts 128k 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-mini and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.