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

FLUX.2 [max] vs Gemini 2.0 Flash Thinking benchmark

Name: FLUX.2 [max] vs Gemini 2.0 Flash Thinking AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares FLUX.2 [max] (Black Forest Labs) and Gemini 2.0 Flash Thinking (Google) using the latest snapshots we have as of Aug 17, 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.

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Black Forest Labs

FLUX.2 [max]

Black Forest Labs maximum-capacity flow-matching model with photoreal anatomy and leading typography fidelity.

Img Elo 1,233$70/1k imgs

Google

Gemini 2.0 Flash Thinking

Google experimental reasoning model that visualizes thoughts in real-time.

Img Elo 1,490

Gemini 2.0 Flash Thinking Inference Compute Profile

Inference-Time Compute & Reasoning EffortDynamic CoT

Gemini 2.0 Flash Thinking 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

220 ms

1.0x (Standard eval)

CoT Thinking Tokens

~2,400

Billed as output tokens

Est. Cost / 1k Calls

$1.13

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.

FLUX.2 [max] (5)vsGemini 2.0 Flash Thinking (0)

FLUX.2 [max]: 5W (100%)Overall: FLUX.2 [max]Gemini 2.0 Flash Thinking: 0W (0%)

← FLUX.2 [max]Gemini 2.0 Flash Thinking →

FLUX.2 [max] (3/3)

Visual Quality & Adherence

Arena Preference Elo, prompt fidelity & typography score

Image Elo

1,233vs—

Prompt adherence

91.2%vs—

Text rendering

92.8%vs—

FLUX.2 [max]: 3WGemini 2.0 Flash Thinking: 0W

FLUX.2 [max] (1/1)

Generation Latency

Time to render full-resolution image snapshot (seconds)

Generation time

5.1svs—

FLUX.2 [max]: 1WGemini 2.0 Flash Thinking: 0W

FLUX.2 [max] (1/1)

Cost Efficiency

API inference cost per 1,000 generated images

Price per 1k images

$70/1kvs—

FLUX.2 [max]: 1WGemini 2.0 Flash Thinking: 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 AdvantageFLUX.2 [max] 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

1,233

Img EloHigh is better

—

Gen Time

Low is better

5.1s

Gen TimeLow is better

—

Img $

Low is better

$70/1k

Img $Low is better

—

Prompt %

High is better

91.2%

Prompt %High is better

—

Text %

High is better

92.8%

Text %High is better

—

Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 100% with Gemini 2.0 Flash Thinking

Monthly Image Production Volume25k images / month (25000 imgs)

Quick Presets:

FLUX.2 [max]$1,750.00 / mo

Rate: $70/1k

Gemini 2.0 Flash Thinking$0.00 / mo

Rate: $0/1k

Estimated Cost Delta

Gemini 2.0 Flash Thinking is estimated to save $1,750.00/month ($21,000/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).

Community Sentiment

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CompareLLM Matrix

Verified head-to-head card

LIVE

Black Forest Labs

FLUX.2 [max]

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost—

Google

Gemini 2.0 Flash Thinking

Elo 1,490

LiveBench67%

SWE-bench66.8%

Speed115 tok/s

Output cost$0.4/1M

Frequently Asked Questions

Which is better overall, FLUX.2 [max] or Gemini 2.0 Flash Thinking?: 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, FLUX.2 [max] or Gemini 2.0 Flash Thinking?: 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, FLUX.2 [max] or Gemini 2.0 Flash Thinking?: 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. FLUX.2 [max] and Gemini 2.0 Flash Thinking Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All FLUX.2 [max] matchups →|All Gemini 2.0 Flash Thinking matchups →

FLUX.2 [max] vs FLUX.2 [flex] (previous flux)Gemini 2.0 Flash Thinking vs Gemini 2.5 Flash (next gemini-flash)Gemini 3 Pro vs Gemini 2.0 Flash Thinking Gemini 3 Flash vs Gemini 2.0 Flash Thinking Gemini 3.6 Pro vs Gemini 2.0 Flash Thinking Gemini 3.6 Flash vs Gemini 2.0 Flash Thinking

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

FLUX.2 [max] (5)vsGemini 2.0 Flash Thinking (0)

FLUX.2 [max]: 5W (100%)Overall: FLUX.2 [max]Gemini 2.0 Flash Thinking: 0W (0%)

← FLUX.2 [max]Gemini 2.0 Flash Thinking →

FLUX.2 [max] (3/3)

Visual Quality & Adherence

Arena Preference Elo, prompt fidelity & typography score

Image Elo

1,233vs—

Prompt adherence

91.2%vs—

Text rendering

92.8%vs—

FLUX.2 [max]: 3WGemini 2.0 Flash Thinking: 0W

FLUX.2 [max] (1/1)

Generation Latency

Time to render full-resolution image snapshot (seconds)

Generation time

5.1svs—

FLUX.2 [max]: 1WGemini 2.0 Flash Thinking: 0W

FLUX.2 [max] (1/1)

Cost Efficiency

API inference cost per 1,000 generated images

Price per 1k images

$70/1kvs—

FLUX.2 [max]: 1WGemini 2.0 Flash Thinking: 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, FLUX.2 [max] or Gemini 2.0 Flash Thinking?

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, FLUX.2 [max] or Gemini 2.0 Flash Thinking?

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, FLUX.2 [max] or Gemini 2.0 Flash Thinking?

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. FLUX.2 [max] and Gemini 2.0 Flash Thinking Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.