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

Gemini 3.6 Pro vs MAI-Image-2.5 benchmark

Name: Gemini 3.6 Pro vs MAI-Image-2.5 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 3.6 Pro (Google) and MAI-Image-2.5 (Microsoft AI) 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.

Img Elo 1,570

Microsoft AI

MAI-Image-2.5

Microsoft AI proprietary foundation image model for Copilot Studio and enterprise creative workflows.

Img Elo 1,307$48.1/1k imgs

Gemini 3.6 Pro Inference Compute Profile

Inference-Time Compute & Reasoning EffortDynamic CoT

Gemini 3.6 Pro Reasoning Scaler & Token Billing Simulator

Param: thinking_budget

Reasoning models scale test-time compute by generating hidden chain-of-thought (CoT) tokens. These tokens are billed at standard output rates and directly expand latency (TTFT) in exchange for higher GPQA Diamond and SWE-bench Verified problem resolve rates.

Estimated TTFT

205 ms

1.0x (Standard eval)

CoT Thinking Tokens

~2,400

Billed as output tokens

Est. Cost / 1k Calls

$14.13

6.6x base invoice

STEM / SWE Boost

+4.8%

GPQA & SWE-bench scaling

Medium (8k tokens) Operational Profile

Balanced reasoning for coding and multimodal comprehension.

Direct Provider APIGoogle

"thinking": {
  "type": "enabled",
  "budget_tokens": 16384
}

OpenRouter Unified APIUnified Gateway

"reasoning": {
  "effort": "medium",
  "max_tokens": 2400
}

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 (0)vsMAI-Image-2.5 (5)

Gemini 3.6 Pro: 0W (0%)Overall: MAI-Image-2.5MAI-Image-2.5: 5W (100%)

← Gemini 3.6 ProMAI-Image-2.5 →

MAI-Image-2.5 (3/3)

Visual Quality & Adherence

Arena Preference Elo, prompt fidelity & typography score

Image Elo

—vs1,307

Prompt adherence

—vs92%

Text rendering

—vs93%

Gemini 3.6 Pro: 0WMAI-Image-2.5: 3W

MAI-Image-2.5 (1/1)

Generation Latency

Time to render full-resolution image snapshot (seconds)

Generation time

—vs3.8s

Gemini 3.6 Pro: 0WMAI-Image-2.5: 1W

MAI-Image-2.5 (1/1)

Cost Efficiency

API inference cost per 1,000 generated images

Price per 1k images

—vs$48.1/1k

Gemini 3.6 Pro: 0WMAI-Image-2.5: 1W

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 AdvantageMAI-Image-2.5 5/6

Image Preference Elo

No shared data

Generation Latency

No shared data

Image Price Efficiency

No shared data

Full Benchmark Score Matrix

—

Img Elo

High is better

—

Img EloHigh is better

1,307

Gen Time

Low is better

—

Gen TimeLow is better

3.8s

Img $

Low is better

—

Img $Low is better

$48.1/1k

Prompt %

High is better

—

Prompt %High is better

92%

Text %

High is better

—

Text %High is better

93%

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 100% with Gemini 3.6 Pro

Monthly Image Production Volume25k images / month (25000 imgs)

Quick Presets:

Gemini 3.6 Pro$0.00 / mo

Rate: $0/1k

MAI-Image-2.5$1,202.50 / mo

Rate: $48.1/1k

Estimated Cost Delta

Gemini 3.6 Pro is estimated to save $1,202.50/month ($14,430/year).

Based on 25k images generated

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-mai-image-2-5/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

Microsoft AI

MAI-Image-2.5

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost—

Frequently Asked Questions

Which is better overall, Gemini 3.6 Pro or MAI-Image-2.5?: 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 MAI-Image-2.5?: 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 MAI-Image-2.5?: 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 MAI-Image-2.5 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 MAI-Image-2.5 matchups →

Gemini 3.6 Pro vs Gemini 3 Pro (previous gemini-pro)MAI-Image-2.5 vs HiDream-O1-Image-1.5 (previous other)MAI-Image-2.5 vs MAI-Image-2.5-Flash (next other)Gemini 3.7 Flash vs Gemini 3.6 Pro Gemini 3.6 Flash vs Gemini 3.6 Pro Gemini 3 Flash 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 (0)vsMAI-Image-2.5 (5)

Gemini 3.6 Pro: 0W (0%)Overall: MAI-Image-2.5MAI-Image-2.5: 5W (100%)

← Gemini 3.6 ProMAI-Image-2.5 →

MAI-Image-2.5 (3/3)

Visual Quality & Adherence

Arena Preference Elo, prompt fidelity & typography score

Image Elo

—vs1,307

Prompt adherence

—vs92%

Text rendering

—vs93%

Gemini 3.6 Pro: 0WMAI-Image-2.5: 3W

MAI-Image-2.5 (1/1)

Generation Latency

Time to render full-resolution image snapshot (seconds)

Generation time

—vs3.8s

Gemini 3.6 Pro: 0WMAI-Image-2.5: 1W

MAI-Image-2.5 (1/1)

Cost Efficiency

API inference cost per 1,000 generated images

Price per 1k images

—vs$48.1/1k

Gemini 3.6 Pro: 0WMAI-Image-2.5: 1W

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 MAI-Image-2.5?

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 MAI-Image-2.5?

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 MAI-Image-2.5?

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 MAI-Image-2.5 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Gemini 3.6 Pro Reasoning Scaler & Token Billing Simulator

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Visual Quality & Adherence

Generation Latency

Cost Efficiency

Multi-Dimensional Capability Radar

No shared data

No shared data

No shared data

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

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

MAI-Image-2.5

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Gemini 3.6 Pro Reasoning Scaler & Token Billing Simulator

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Visual Quality & Adherence

Generation Latency

Cost Efficiency

Multi-Dimensional Capability Radar

No shared data

No shared data

No shared data

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

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

MAI-Image-2.5

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons