Pairwise benchmark snapshot · Aug 1, 2026

FLUX.2 [flex] vs Luma UNI 1 Max benchmark

Name: FLUX.2 [flex] vs Luma UNI 1 Max AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares FLUX.2 [flex] (Black Forest Labs) and Luma UNI 1 Max (Luma Labs) using the latest snapshots we have as of Aug 1, 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 [flex]

Flexible step-distilled variant of FLUX.2 offering 99% of Max quality with 45% faster generation speeds.

Luma Labs

Luma UNI 1 Max

Luma Labs universal 3D-aware image synthesis engine with spatial geometry consistency.

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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.

FLUX.2 [flex] (0)vsLuma UNI 1 Max (0)

FLUX.2 [flex]: 0W (50%)Overall: Even matchupLuma UNI 1 Max: 0W (50%)

← FLUX.2 [flex]Luma UNI 1 Max →

Tied (0/5)

Intelligence & Reasoning

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

Preference Elo

—vs—

Coding Elo

—vs—

SWE-bench

—vs—

LiveBench

—vs—

GPQA Diamond

—vs—

FLUX.2 [flex]: 0WLuma UNI 1 Max: 0W

Tied (0/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs—

Time to first token

—vs—

FLUX.2 [flex]: 0WLuma UNI 1 Max: 0W

Tied (0/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

—vs—

Input price

—vs—

Context window

—vs—

FLUX.2 [flex]: 0WLuma UNI 1 Max: 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.

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Percentile 0–100

Dimensional AdvantageFLUX.2 [flex] 4/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

No shared data

Full Benchmark Score Matrix

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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

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LIVE

Black Forest Labs

FLUX.2 [flex]

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost—

Luma Labs

Luma UNI 1 Max

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost—

Frequently Asked Questions

Which is better overall, FLUX.2 [flex] or Luma UNI 1 Max?: 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 [flex] or Luma UNI 1 Max?: 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 [flex] or Luma UNI 1 Max?: 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 [flex] and Luma UNI 1 Max Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All FLUX.2 [flex] matchups →|All Luma UNI 1 Max matchups →

FLUX.2 [flex] vs FLUX.1.1 [pro] (previous flux)FLUX.2 [flex] vs FLUX.2 [max] (next flux)Luma UNI 1 Max vs HiDream-O1-Image-1.5 (next other)FLUX.2 [max] vs Luma UNI 1 Max FLUX.1.1 [pro] vs Luma UNI 1 Max GPT Image 2 (High) vs Luma UNI 1 Max

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

FLUX.2 [flex] (0)vsLuma UNI 1 Max (0)

FLUX.2 [flex]: 0W (50%)Overall: Even matchupLuma UNI 1 Max: 0W (50%)

← FLUX.2 [flex]Luma UNI 1 Max →

Tied (0/5)

Intelligence & Reasoning

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

Preference Elo

—vs—

Coding Elo

—vs—

SWE-bench

—vs—

LiveBench

—vs—

GPQA Diamond

—vs—

FLUX.2 [flex]: 0WLuma UNI 1 Max: 0W

Tied (0/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs—

Time to first token

—vs—

FLUX.2 [flex]: 0WLuma UNI 1 Max: 0W

Tied (0/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

—vs—

Input price

—vs—

Context window

—vs—

FLUX.2 [flex]: 0WLuma UNI 1 Max: 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 [flex] or Luma UNI 1 Max?

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 [flex] or Luma UNI 1 Max?

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 [flex] or Luma UNI 1 Max?

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 [flex] and Luma UNI 1 Max Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.