Pairwise benchmark snapshot · Aug 16, 2026noindex (thin pair)

Gemini 3.6 Pro vs Qwen Image 2.0 Pro benchmark

Name: Gemini 3.6 Pro vs Qwen Image 2.0 Pro AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 3.6 Pro (Google) and Qwen Image 2.0 Pro (Alibaba) using the latest snapshots we have as of Aug 16, 2026. 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

Google

Gemini 3.6 Pro

Current Google Pro-class multimodal model. Long context, strong coding, billed like the 3.x Pro tier.

Elo 1,570$5/1M out

Alibaba

Qwen Image 2.0 Pro

Alibaba multimodal image synthesis foundation model with strong bilingual Chinese/English typography and cultural asset fidelity.

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.

Gemini 3.6 Pro (10)vsQwen Image 2.0 Pro (0)

Gemini 3.6 Pro: 10W (100%)Overall: Gemini 3.6 ProQwen Image 2.0 Pro: 0W (0%)

← Gemini 3.6 ProQwen Image 2.0 Pro →

Gemini 3.6 Pro (5/5)

Intelligence & Reasoning

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

Preference Elo

1,570vs—

Coding Elo

1,566vs—

SWE-bench

74.6%vs—

LiveBench

71.8%vs—

GPQA Diamond

84.4%vs—

Gemini 3.6 Pro: 5WQwen Image 2.0 Pro: 0W

Gemini 3.6 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

102 tok/svs—

Time to first token

205 msvs—

Gemini 3.6 Pro: 2WQwen Image 2.0 Pro: 0W

Gemini 3.6 Pro (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs—

Input price

$1.25/1Mvs—

Context window

2Mvs—

Gemini 3.6 Pro: 3WQwen Image 2.0 Pro: 0W

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 AdvantageQwen Image 2.0 Pro 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

No shared data

Full Benchmark Score Matrix

—

Elo

High is better

1,570

EloHigh is better

—

Code Elo

High is better

1,566

Code EloHigh is better

—

LiveBench

High is better

71.8%

LiveBenchHigh is better

—

SWE-bench

High is better

74.6%

SWE-benchHigh is better

—

GPQA

High is better

84.4%

GPQAHigh is better

—

TTFT

Low is better

205 ms

TTFTLow is better

—

Speed

High is better

102 tok/s

SpeedHigh is better

—

In $

Low is better

$1.25/1M

In $Low is better

—

Out $

Low is better

$5/1M

Out $Low is better

—

Context

High is better

ContextHigh is better

—

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatinsufficient data

Need list prices.

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGinsufficient data

Need context sizes.

Screenshots / visioninsufficient data

Both accept images. Defaulting to the higher-Elo side (undefined).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	insufficient data	Need list prices.
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	insufficient data	Need context sizes.
Screenshots / vision	insufficient data	Both accept images. Defaulting to the higher-Elo side (undefined).

Related Editorial Dispatches & Benchmark Notes

View all news

compareAug 13, 2026

Gemini Flash vs Pro: when Flash is the whole product

3.7 Flash vs 3.6 Pro is the current Google volume-versus-upgrade pair.

compareJul 24, 2026

Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Top Anthropic vs top Google. Usually Opus 5 vs 3.6 Pro. Two different invoices.

compareJul 16, 2026

Gemini vs GPT: long context versus the OpenAI flagship invoice

Hub remaps to top Google vs top OpenAI Elo. Usually 3.6 Pro vs Sol. Window vs price is the plot.

launchJul 15, 2026

Gemini 3.6 Pro: Google’s current Pro-class long-context row

2M context, Pro-class Elo and coding. Upgrade path from Flash is this page, not a vibe check.

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/gemini-3-6-pro-vs-qwen-image-2-pro/opengraph-image for crawlers.

PNG

CompareLLM Matrix

Verified head-to-head card

LIVE

Google

Gemini 3.6 Pro

Elo 1,570

LiveBench71.8%

SWE-bench74.6%

Speed102 tok/s

Output cost$5/1M

Alibaba

Qwen Image 2.0 Pro

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost—

Frequently Asked Questions

Which is better overall, Gemini 3.6 Pro or Qwen Image 2.0 Pro?: 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, Gemini 3.6 Pro or Qwen Image 2.0 Pro?: 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?: List prices are missing for at least one model.
Which is faster, Gemini 3.6 Pro or Qwen Image 2.0 Pro?: We do not have TTFT for both models.
Which has the larger context window?: Context window is missing for at least one model.
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 3.6 Pro and Qwen Image 2.0 Pro Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Gemini 3.6 Pro matchups →|All Qwen Image 2.0 Pro matchups →

Gemini 3.6 Pro vs Gemini 3 Pro (previous gemini-pro)Gemini 3.7 Flash vs Gemini 3.6 Pro Qwen 3 Max vs Gemini 3.6 Pro Gemini 3.6 Flash vs Gemini 3.6 Pro Gemini 3 Flash vs Gemini 3.6 Pro Gemini 2.0 Flash Thinking vs Gemini 3.6 Pro

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 3.6 Pro (10)vsQwen Image 2.0 Pro (0)

Gemini 3.6 Pro: 10W (100%)Overall: Gemini 3.6 ProQwen Image 2.0 Pro: 0W (0%)

← Gemini 3.6 ProQwen Image 2.0 Pro →

Gemini 3.6 Pro (5/5)

Intelligence & Reasoning

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

Preference Elo

1,570vs—

Coding Elo

1,566vs—

SWE-bench

74.6%vs—

LiveBench

71.8%vs—

GPQA Diamond

84.4%vs—

Gemini 3.6 Pro: 5WQwen Image 2.0 Pro: 0W

Gemini 3.6 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

102 tok/svs—

Time to first token

205 msvs—

Gemini 3.6 Pro: 2WQwen Image 2.0 Pro: 0W

Gemini 3.6 Pro (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs—

Input price

$1.25/1Mvs—

Context window

2Mvs—

Gemini 3.6 Pro: 3WQwen Image 2.0 Pro: 0W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatinsufficient data

Need list prices.

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGinsufficient data

Need context sizes.

Screenshots / visioninsufficient data

Both accept images. Defaulting to the higher-Elo side (undefined).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	insufficient data	Need list prices.
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	insufficient data	Need context sizes.
Screenshots / vision	insufficient data	Both accept images. Defaulting to the higher-Elo side (undefined).

Frequently Asked Questions

Which is better overall, Gemini 3.6 Pro or Qwen Image 2.0 Pro?

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, Gemini 3.6 Pro or Qwen Image 2.0 Pro?

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?

List prices are missing for at least one model.

Which is faster, Gemini 3.6 Pro or Qwen Image 2.0 Pro?

We do not have TTFT for both models.

Which has the larger context window?

Context window is missing for at least one model.

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 3.6 Pro and Qwen Image 2.0 Pro Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

No shared data

No shared data

No shared data

Full Benchmark Score Matrix

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Gemini Flash vs Pro: when Flash is the whole product

Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Gemini vs GPT: long context versus the OpenAI flagship invoice

Gemini 3.6 Pro: Google’s current Pro-class long-context row

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Gemini 3.6 Pro

Qwen Image 2.0 Pro

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

No shared data

No shared data

No shared data

Full Benchmark Score Matrix

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Gemini Flash vs Pro: when Flash is the whole product

Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Gemini vs GPT: long context versus the OpenAI flagship invoice

Gemini 3.6 Pro: Google’s current Pro-class long-context row

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Gemini 3.6 Pro

Qwen Image 2.0 Pro

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons