Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)

GPT-4 Turbo vs Qwen: Qwen3.5-35B-A3B benchmark

Name: GPT-4 Turbo vs Qwen: Qwen3.5-35B-A3B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares GPT-4 Turbo (OpenAI) and Qwen: Qwen3.5-35B-A3B (Alibaba) using the latest snapshots we have as of Aug 17, 2026. Qwen: Qwen3.5-35B-A3B wins on output price at $1.8/1M. Qwen: Qwen3.5-35B-A3B has the larger context window (262k 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

GPT-4 Turbo

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

Elo 1,255$30/1M out

Alibaba

Qwen: Qwen3.5-35B-A3B

Auto-discovered from OpenRouter (qwen/qwen3.5-35b-a3b). Preview until a second source matches.

$1.7999999999999998/1M 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.

GPT-4 Turbo (6)vsQwen: Qwen3.5-35B-A3B (3)

GPT-4 Turbo: 6W (67%)Overall: GPT-4 TurboQwen: Qwen3.5-35B-A3B: 3W (33%)

← GPT-4 TurboQwen: Qwen3.5-35B-A3B →

GPT-4 Turbo (4/5)

Intelligence & Reasoning

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

Preference Elo

1,255vs—

Coding Elo

—vs—

SWE-bench

33.2%vs—

LiveBench

47%vs—

GPQA Diamond

48%vs—

GPT-4 Turbo: 4WQwen: Qwen3.5-35B-A3B: 0W

GPT-4 Turbo (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

55 tok/svs—

Time to first token

350 msvs—

GPT-4 Turbo: 2WQwen: Qwen3.5-35B-A3B: 0W

Qwen: Qwen3.5-35B-A3B (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$30/1Mvs$1.8/1M

Input price

$10/1Mvs$0.22/1M

Context window

128kvs262k

GPT-4 Turbo: 0WQwen: Qwen3.5-35B-A3B: 3W

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 AdvantageGPT-4 Turbo 4/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

Qwen: Qwen3.5-35B-A3B

$28.20/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,255

EloHigh is better

—

LiveBench

High is better

47%

LiveBenchHigh is better

—

SWE-bench

High is better

33.2%

SWE-benchHigh is better

—

GPQA

High is better

48%

GPQAHigh is better

—

TTFT

Low is better

350 ms

TTFTLow is better

—

Speed

High is better

55 tok/s

SpeedHigh is better

—

In $

Low is better

$10/1M

In $Low is better

$0.22/1M+4344%

Out $

Low is better

$30/1M

Out $Low is better

$1.8/1M+1567%

Context

High is better

128k

ContextHigh is better

262k+105%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 96% with Qwen: Qwen3.5-35B-A3B

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: $350.00Out: $450.00

Qwen: Qwen3.5-35B-A3B$34.87 / mo

In: $7.87Out: $27.00

Estimated Cost Delta

Qwen: Qwen3.5-35B-A3B is estimated to save $765.13/month ($9,182/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Qwen: Qwen3.5-35B-A3B:Pick Qwen: Qwen3.5-35B-A3B when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatQwen: Qwen3.5-35B-A3B

Lower output list price ($1.8/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGQwen: Qwen3.5-35B-A3B

Larger window (262k).

Screenshots / visionGPT-4 Turbo

GPT-4 Turbo is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	Qwen: Qwen3.5-35B-A3B	Lower output list price ($1.8/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Qwen: Qwen3.5-35B-A3B	Larger window (262k).
Screenshots / vision	GPT-4 Turbo	GPT-4 Turbo is the side marked multimodal in the catalog.

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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

Alibaba

Qwen: Qwen3.5-35B-A3B

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$1.8/1M

Frequently Asked Questions

Which is better overall, GPT-4 Turbo or Qwen: Qwen3.5-35B-A3B?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, GPT-4 Turbo or Qwen: Qwen3.5-35B-A3B?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: Qwen: Qwen3.5-35B-A3B output tokens are $1.8/1M versus $30/1M. Input prices and retry rates still move the real bill.
Which is faster, GPT-4 Turbo or Qwen: Qwen3.5-35B-A3B?: We do not have TTFT for both models.
Which has the larger context window?: Qwen: Qwen3.5-35B-A3B accepts 262k tokens versus 128k.
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 Qwen: Qwen3.5-35B-A3B 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 Qwen: Qwen3.5-35B-A3B matchups →

GPT-4 Turbo vs GPT-4o (next gpt-flagship)GPT-5 vs GPT-4 Turbo Qwen 3 Max vs GPT-4 Turbo GPT-4.5 Orion vs GPT-4 Turbo GPT-5.6 Sol vs GPT-4 Turbo GPT-5.6 Terra vs GPT-4 Turbo

GPT-4 Turbo vs Qwen: Qwen3.5-35B-A3B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

GPT-4 Turbo (6)vsQwen: Qwen3.5-35B-A3B (3)

GPT-4 Turbo: 6W (67%)Overall: GPT-4 TurboQwen: Qwen3.5-35B-A3B: 3W (33%)

← GPT-4 TurboQwen: Qwen3.5-35B-A3B →

GPT-4 Turbo (4/5)

Intelligence & Reasoning

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

Preference Elo

1,255vs—

Coding Elo

—vs—

SWE-bench

33.2%vs—

LiveBench

47%vs—

GPQA Diamond

48%vs—

GPT-4 Turbo: 4WQwen: Qwen3.5-35B-A3B: 0W

GPT-4 Turbo (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

55 tok/svs—

Time to first token

350 msvs—

GPT-4 Turbo: 2WQwen: Qwen3.5-35B-A3B: 0W

Qwen: Qwen3.5-35B-A3B (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$30/1Mvs$1.8/1M

Input price

$10/1Mvs$0.22/1M

Context window

128kvs262k

GPT-4 Turbo: 0WQwen: Qwen3.5-35B-A3B: 3W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatQwen: Qwen3.5-35B-A3B

Lower output list price ($1.8/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGQwen: Qwen3.5-35B-A3B

Larger window (262k).

Screenshots / visionGPT-4 Turbo

GPT-4 Turbo is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	Qwen: Qwen3.5-35B-A3B	Lower output list price ($1.8/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Qwen: Qwen3.5-35B-A3B	Larger window (262k).
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 Qwen: Qwen3.5-35B-A3B?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, GPT-4 Turbo or Qwen: Qwen3.5-35B-A3B?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

Qwen: Qwen3.5-35B-A3B output tokens are $1.8/1M versus $30/1M. Input prices and retry rates still move the real bill.

Which is faster, GPT-4 Turbo or Qwen: Qwen3.5-35B-A3B?

We do not have TTFT for both models.

Which has the larger context window?

Qwen: Qwen3.5-35B-A3B accepts 262k tokens versus 128k.

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 Qwen: Qwen3.5-35B-A3B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.