Pairwise benchmark snapshot · Aug 1, 2026

OpenAI o1-mini vs Yi-Lightning benchmark

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

This page compares OpenAI o1-mini (OpenAI) and Yi-Lightning (01.AI) using the latest snapshots we have as of Aug 1, 2026. On preference Elo, Yi-Lightning is ahead by 30 points (1,475 vs 1,445; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, Yi-Lightning resolves 63.4% versus 56.4% (seed-bootstrap (Aug 1, 2026)). Yi-Lightning streams faster (130 tok/s) while Yi-Lightning is the cheaper output token ($0.14/1M). 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-Lightning

01.AI ultra-fast reasoning model delivering top LiveBench efficiency.

Elo 1,475$0.14/1M 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-mini (1)vsYi-Lightning (9)

OpenAI o1-mini: 1W (10%)Overall: Yi-LightningYi-Lightning: 9W (90%)

← OpenAI o1-miniYi-Lightning →

Yi-Lightning (5/5)

Intelligence & Reasoning

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

Preference Elo

1,445vs1,475

Coding Elo

1,468vs1,480

SWE-bench

56.4%vs63.4%

LiveBench

62%vs65%

GPQA Diamond

76.8%vs78%

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

Yi-Lightning (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

85 tok/svs130 tok/s

Time to first token

280 msvs150 ms

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

Yi-Lightning (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4.4/1Mvs$0.14/1M

Input price

$1.1/1Mvs$0.14/1M

Context window

128kvs16k

OpenAI o1-mini: 1WYi-Lightning: 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-Lightning 5/6

Preference Elo

Yi-Lightning

30 pts advantage

Throughput Speed

Yi-Lightning

45 tok/s faster

Price Efficiency

Yi-Lightning

$4.26/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,445

EloHigh is better

1,475+2%

Code Elo

High is better

1,468

Code EloHigh is better

1,480+1%

LiveBench

High is better

62%

LiveBenchHigh is better

65%+5%

SWE-bench

High is better

56.4%

SWE-benchHigh is better

63.4%+12%

GPQA

High is better

76.8%

GPQAHigh is better

78%+2%

TTFT

Low is better

280 ms

TTFTLow is better

150 ms+87%

Speed

High is better

85 tok/s

SpeedHigh is better

130 tok/s+53%

In $

Low is better

$1.1/1M

In $Low is better

$0.14/1M+686%

Out $

Low is better

$4.4/1M

Out $Low is better

$0.14/1M+3043%

Context

High is better

+681%128k

ContextHigh is better

16k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 93% with Yi-Lightning

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-Lightning$7.00 / mo

In: $4.90Out: $2.10

Estimated Cost Delta

Yi-Lightning is estimated to save $97.50/month ($1,170/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Yi-Lightning:Pick Yi-Lightning when repo-level coding accuracy is the constraint.
2
Yi-Lightning:Pick Yi-Lightning when time-to-first-token matters more than peak Elo.
3
Yi-Lightning:Pick Yi-Lightning as the default general assistant.

Recommended Workload Routing

Repo / coding agentsYi-Lightning

Higher SWE-bench (63.4%).

High-volume chatYi-Lightning

Lower output list price ($0.14/1M).

Voice / low-latency UIYi-Lightning

Lower TTFT (150 ms).

Long-document RAGOpenAI o1-mini

Larger window (128k).

Screenshots / visionYi-Lightning

Yi-Lightning is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Yi-Lightning	Higher SWE-bench (63.4%).
High-volume chat	Yi-Lightning	Lower output list price ($0.14/1M).
Voice / low-latency UI	Yi-Lightning	Lower TTFT (150 ms).
Long-document RAG	OpenAI o1-mini	Larger window (128k).
Screenshots / vision	Yi-Lightning	Yi-Lightning is the side marked multimodal in the catalog.

Community Sentiment

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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-Lightning

Elo 1,475

LiveBench65%

SWE-bench63.4%

Speed130 tok/s

Output cost$0.14/1M

Frequently Asked Questions

Which is better overall, OpenAI o1-mini or Yi-Lightning?: Yi-Lightning has the higher preference Elo in our latest snapshot (1,475). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, OpenAI o1-mini or Yi-Lightning?: Yi-Lightning leads SWE-bench at 63.4% vs 56.4%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Yi-Lightning output tokens are $0.14/1M versus $4.4/1M. Input prices and retry rates still move the real bill.
Which is faster, OpenAI o1-mini or Yi-Lightning?: Yi-Lightning has the lower time-to-first-token (150 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 16k.
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-Lightning 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-Lightning matchups →

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

OpenAI o1-mini vs Yi-Lightning benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI o1-mini (1)vsYi-Lightning (9)

OpenAI o1-mini: 1W (10%)Overall: Yi-LightningYi-Lightning: 9W (90%)

← OpenAI o1-miniYi-Lightning →

Yi-Lightning (5/5)

Intelligence & Reasoning

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

Preference Elo

1,445vs1,475

Coding Elo

1,468vs1,480

SWE-bench

56.4%vs63.4%

LiveBench

62%vs65%

GPQA Diamond

76.8%vs78%

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

Yi-Lightning (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

85 tok/svs130 tok/s

Time to first token

280 msvs150 ms

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

Yi-Lightning (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4.4/1Mvs$0.14/1M

Input price

$1.1/1Mvs$0.14/1M

Context window

128kvs16k

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

Recommended Workload Routing

Repo / coding agentsYi-Lightning

Higher SWE-bench (63.4%).

High-volume chatYi-Lightning

Lower output list price ($0.14/1M).

Voice / low-latency UIYi-Lightning

Lower TTFT (150 ms).

Long-document RAGOpenAI o1-mini

Larger window (128k).

Screenshots / visionYi-Lightning

Yi-Lightning is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Yi-Lightning	Higher SWE-bench (63.4%).
High-volume chat	Yi-Lightning	Lower output list price ($0.14/1M).
Voice / low-latency UI	Yi-Lightning	Lower TTFT (150 ms).
Long-document RAG	OpenAI o1-mini	Larger window (128k).
Screenshots / vision	Yi-Lightning	Yi-Lightning is the side marked multimodal in the catalog.

Frequently Asked Questions

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

Yi-Lightning has the higher preference Elo in our latest snapshot (1,475). “Better” still depends on coding, price, and latency — see the table.

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

Yi-Lightning leads SWE-bench at 63.4% vs 56.4%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Yi-Lightning output tokens are $0.14/1M versus $4.4/1M. Input prices and retry rates still move the real bill.

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

Yi-Lightning has the lower time-to-first-token (150 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 16k.

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-Lightning Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.