Pairwise benchmark snapshot · Aug 17, 2026

GPT-4 Turbo vs Yi-Large benchmark

Name: GPT-4 Turbo vs Yi-Large AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares GPT-4 Turbo (OpenAI) and Yi-Large (01.AI) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, Yi-Large is ahead by 175 points (1,430 vs 1,255; lmarena (Aug 17, 2026)). On SWE-bench coding, Yi-Large resolves 53.8% versus 33.2% (swebench (Aug 17, 2026)). Yi-Large streams faster (90 tok/s) while Yi-Large is the cheaper output token ($0.3/1M). GPT-4 Turbo 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.

Share Analysis:

WhatsApp Telegram X / Post LinkedIn Reddit

OpenAI

GPT-4 Turbo

GPT-4 Turbo 128k. Historical flagship for gpt-4 turbo vs gpt-4o / gpt-5.

Elo 1,255$30/1M out

01.AI

Yi-Large

01.AI full-scale dense model for complex instruction following.

Elo 1,430$0.3/1M out

Swap or Add Any Model to this Matchup

Instant pairwise benchmark recalculation across 400+ LLMs

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.

GPT-4 Turbo (1)vsYi-Large (9)

GPT-4 Turbo: 1W (10%)Overall: Yi-LargeYi-Large: 9W (90%)

← GPT-4 TurboYi-Large →

Yi-Large (5/5)

Intelligence & Reasoning

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

Preference Elo

1,255vs1,430

Coding Elo

—vs1,410

SWE-bench

33.2%vs53.8%

LiveBench

47%vs60.5%

GPQA Diamond

48%vs72.4%

GPT-4 Turbo: 0WYi-Large: 5W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

55 tok/svs90 tok/s

Time to first token

350 msvs220 ms

GPT-4 Turbo: 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

$10/1Mvs$0.3/1M

Context window

128kvs33k

GPT-4 Turbo: 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

175 pts advantage

Throughput Speed

Yi-Large

35 tok/s faster

Token Price Efficiency

Yi-Large

$29.70/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,255

EloHigh is better

1,430+14%

Code Elo

High is better

—

Code EloHigh is better

1,410

LiveBench

High is better

47%

LiveBenchHigh is better

60.5%+29%

SWE-bench

High is better

33.2%

SWE-benchHigh is better

53.8%+62%

GPQA

High is better

48%

GPQAHigh is better

72.4%+51%

TTFT

Low is better

350 ms

TTFTLow is better

220 ms+59%

Speed

High is better

55 tok/s

SpeedHigh is better

90 tok/s+64%

In $

Low is better

$10.00 / 1M

In $Low is better

$0.30 / 1M+3233%

Out $

Low is better

$30.00 / 1M

Out $Low is better

$0.30 / 1M+9900%

Context

High is better

+291%128k

ContextHigh is better

33k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

Simulate monthly production API costs in USD (US Dollar).

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)

GPT-4 Turbo$800.00 / mo

In: $350Out: $450

Yi-Large$15.00 / mo

In: $10.5Out: $4.5

Estimated Cost Delta

Yi-Large is estimated to save $785.00/month ($9,420/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
Yi-Large:Pick Yi-Large 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 RAGGPT-4 Turbo

Larger window (128k).

Screenshots / visionGPT-4 Turbo

GPT-4 Turbo 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	GPT-4 Turbo	Larger window (128k).
Screenshots / vision	GPT-4 Turbo	GPT-4 Turbo is the side marked multimodal in the catalog.

Community Sentiment

Cast Your Matchup Vote

0 total votes

Community Discussions (0)

No comments posted on this matchup yet. Be the first to share an evaluation note!

Share card

The real PNG is generated at /compare/gpt-4-turbo-vs-yi-large/opengraph-image for crawlers.

PNG

CompareLLM Matrix

Verified head-to-head card

LIVE

OpenAI

GPT-4 Turbo

Elo 1,255

LiveBench47%

SWE-bench33.2%

Speed55 tok/s

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, GPT-4 Turbo 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, GPT-4 Turbo or Yi-Large?: Yi-Large leads SWE-bench at 53.8% vs 33.2%. SWE-bench is one harness, not your repo.
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, GPT-4 Turbo or Yi-Large?: Yi-Large has the lower time-to-first-token (220 ms vs 350 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: GPT-4 Turbo 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. GPT-4 Turbo and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All GPT-4 Turbo matchups →|All Yi-Large matchups →

GPT-4 Turbo vs GPT-4o (next gpt-flagship)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-4 Turbo vs Yi-Large benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GPT-4 Turbo (1)vsYi-Large (9)

GPT-4 Turbo: 1W (10%)Overall: Yi-LargeYi-Large: 9W (90%)

← GPT-4 TurboYi-Large →

Yi-Large (5/5)

Intelligence & Reasoning

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

Preference Elo

1,255vs1,430

Coding Elo

—vs1,410

SWE-bench

33.2%vs53.8%

LiveBench

47%vs60.5%

GPQA Diamond

48%vs72.4%

GPT-4 Turbo: 0WYi-Large: 5W

Yi-Large (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

55 tok/svs90 tok/s

Time to first token

350 msvs220 ms

GPT-4 Turbo: 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

$10/1Mvs$0.3/1M

Context window

128kvs33k

GPT-4 Turbo: 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 RAGGPT-4 Turbo

Larger window (128k).

Screenshots / visionGPT-4 Turbo

GPT-4 Turbo 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	GPT-4 Turbo	Larger window (128k).
Screenshots / vision	GPT-4 Turbo	GPT-4 Turbo is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, GPT-4 Turbo 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, GPT-4 Turbo or Yi-Large?

Yi-Large leads SWE-bench at 53.8% vs 33.2%. SWE-bench is one harness, not your repo.

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, GPT-4 Turbo or Yi-Large?

Yi-Large has the lower time-to-first-token (220 ms vs 350 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

GPT-4 Turbo 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. GPT-4 Turbo and Yi-Large Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.