Pairwise benchmark snapshot · Aug 1, 2026

Gemini 1.5 Pro vs Yi-Large benchmark

Name: Gemini 1.5 Pro vs Yi-Large AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 1.5 Pro (Google) and Yi-Large (01.AI) using the latest snapshots we have as of Aug 1, 2026. On preference Elo, Yi-Large is ahead by 170 points (1,430 vs 1,260; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, Yi-Large resolves 53.8% versus 38% (seed-bootstrap (Aug 1, 2026)). Yi-Large streams faster (90 tok/s) while Yi-Large is the cheaper output token ($0.3/1M). Gemini 1.5 Pro has the larger context window (2M 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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Google

Gemini 1.5 Pro

First million-token Gemini Pro. Baseline for 1.5 vs 2.5 vs 3.x Pro.

Elo 1,260$5/1M 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.

Gemini 1.5 Pro (1)vsYi-Large (9)

Gemini 1.5 Pro: 1W (10%)Overall: Yi-LargeYi-Large: 9W (90%)

← Gemini 1.5 ProYi-Large →

Yi-Large (5/5)

Intelligence & Reasoning

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

Preference Elo

1,260vs1,430

Coding Elo

—vs1,410

SWE-bench

38%vs53.8%

LiveBench

49.2%vs60.5%

GPQA Diamond

58%vs72.4%

Gemini 1.5 Pro: 0WYi-Large: 5W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs90 tok/s

Time to first token

240 msvs220 ms

Gemini 1.5 Pro: 0WYi-Large: 2W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$0.3/1M

Input price

$1.25/1Mvs$0.3/1M

Context window

2Mvs33k

Gemini 1.5 Pro: 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

Yi-Large

170 pts advantage

Throughput Speed

Yi-Large

20 tok/s faster

Price Efficiency

Yi-Large

$4.70/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,260

EloHigh is better

1,430+13%

Code Elo

High is better

—

Code EloHigh is better

1,410

LiveBench

High is better

49.2%

LiveBenchHigh is better

60.5%+23%

SWE-bench

High is better

38%

SWE-benchHigh is better

53.8%+42%

GPQA

High is better

58%

GPQAHigh is better

72.4%+25%

TTFT

Low is better

240 ms

TTFTLow is better

220 ms+9%

Speed

High is better

70 tok/s

SpeedHigh is better

90 tok/s+29%

In $

Low is better

$1.25/1M

In $Low is better

$0.3/1M+317%

Out $

Low is better

$5/1M

Out $Low is better

$0.3/1M+1567%

Context

High is better

+6004%2M

ContextHigh is better

33k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 87% 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)

Gemini 1.5 Pro$118.75 / mo

In: $43.75Out: $75.00

Yi-Large$15.00 / mo

In: $10.50Out: $4.50

Estimated Cost Delta

Yi-Large is estimated to save $103.75/month ($1,245/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
Gemini 1.5 Pro:Pick Gemini 1.5 Pro for million-token RAG or long-document jobs.

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 RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionGemini 1.5 Pro

Gemini 1.5 Pro 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	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	Gemini 1.5 Pro	Gemini 1.5 Pro is the side marked multimodal in the catalog.

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LIVE

Google

Gemini 1.5 Pro

Elo 1,260

LiveBench49.2%

SWE-bench38%

Speed70 tok/s

Output cost$5/1M

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, Gemini 1.5 Pro 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, Gemini 1.5 Pro or Yi-Large?: Yi-Large leads SWE-bench at 53.8% vs 38%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Yi-Large output tokens are $0.3/1M versus $5/1M. Input prices and retry rates still move the real bill.
Which is faster, Gemini 1.5 Pro or Yi-Large?: Yi-Large has the lower time-to-first-token (220 ms vs 240 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Gemini 1.5 Pro accepts 2M 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. Gemini 1.5 Pro and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Gemini 1.5 Pro matchups →|All Yi-Large matchups →

Gemini 1.5 Pro vs Gemini 2.5 Pro (next gemini-pro)Yi-Large vs Yi-Lightning (next other)Gemini 3 Pro vs Yi-Large Gemini 3 Flash vs Yi-Large Gemini 2.0 Flash Thinking vs Yi-Large Gemini 3.6 Pro vs Yi-Large

Gemini 1.5 Pro vs Yi-Large benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 1.5 Pro (1)vsYi-Large (9)

Gemini 1.5 Pro: 1W (10%)Overall: Yi-LargeYi-Large: 9W (90%)

← Gemini 1.5 ProYi-Large →

Yi-Large (5/5)

Intelligence & Reasoning

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

Preference Elo

1,260vs1,430

Coding Elo

—vs1,410

SWE-bench

38%vs53.8%

LiveBench

49.2%vs60.5%

GPQA Diamond

58%vs72.4%

Gemini 1.5 Pro: 0WYi-Large: 5W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs90 tok/s

Time to first token

240 msvs220 ms

Gemini 1.5 Pro: 0WYi-Large: 2W

Yi-Large (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$0.3/1M

Input price

$1.25/1Mvs$0.3/1M

Context window

2Mvs33k

Gemini 1.5 Pro: 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 RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionGemini 1.5 Pro

Gemini 1.5 Pro 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	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	Gemini 1.5 Pro	Gemini 1.5 Pro is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro 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, Gemini 1.5 Pro or Yi-Large?

Yi-Large leads SWE-bench at 53.8% vs 38%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Yi-Large output tokens are $0.3/1M versus $5/1M. Input prices and retry rates still move the real bill.

Which is faster, Gemini 1.5 Pro or Yi-Large?

Yi-Large has the lower time-to-first-token (220 ms vs 240 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Gemini 1.5 Pro accepts 2M 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. Gemini 1.5 Pro and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.