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

FLUX.1 [schnell] vs Meta: Llama 3.2 1B Instruct benchmark

Name: FLUX.1 [schnell] vs Meta: Llama 3.2 1B Instruct AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

Comparing verified benchmark accuracy, speed, and token pricing between FLUX.1 [schnell] and Meta: Llama 3.2 1B Instruct. Review the complete breakdown below to determine which model best fits your performance and budget requirements.

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Executive Verdict Summary

FLUX.1 [schnell] holds empirical advantage (0 of 0 metrics)

Coding / SWESWE-bench

FLUX.1 [schnell]Top code solve rate

ReasoningGPQA / Live

FLUX.1 [schnell]Top logical accuracy

Best Value$/1M Tok

Meta: Llama 3.2 1B InstructLowest API billing cost

Speed / TokTok/Sec

FLUX.1 [schnell]Fastest stream rate

Black Forest Labs

FLUX.1 [schnell]

Apache 2.0 4-step distilled open weights model optimized for local inference and instant preview generation.

Schnell 4-StepImg Elo 1,142$15/1k imgs

FLUX.1 Series Lineage & Sibling Variants

3 models

Currently comparing FLUX.1 [schnell] vs Meta: Llama 3.2 1B Instruct. Alternative family tiers available.

Active: FLUX.1 [schnell]

FLUX.1.1 [pro]$50/1k FLUX.1 [dev]$30/1k

Top Rival Showdowns for FLUX.1 [schnell]

All Matchups

Compare vs:vs GPT Image 2 (High)vs Reve 2.1 vs Nano Banana Pro (Gemini 3 Pro Image)vs Ideogram 4.0 (Quality)vs Recraft V4.1 Utility Pro

Image Quality Leader

No shared data

Single model data

Generation Speed Leader

No shared data

No latency data

Value per Dollar Leader

No shared data

Unpriced

Overall Matchup Breakdown

Category wins across reasoning intelligence, generation speed, and token cost.

FLUX.1 [schnell] (5)vsMeta: Llama 3.2 1B Instruct (0)

FLUX.1 [schnell]: 5W (100%)Meta: Llama 3.2 1B Instruct: 0W (0%)

← FLUX.1 [schnell]Meta: Llama 3.2 1B Instruct →

🏆FLUX.1 [schnell] Leads(3/3)

Visual Quality & Adherence

Arena Preference Elo, prompt fidelity & typography score

Benchmark

FLUX.1 [schnell]vsMeta: Llama 3.2 1B Instruct

Image Elo

1,142vs—

Prompt adherence

84%vs—

Text rendering

82%vs—

FLUX.1 [schnell]: 3WMeta: Llama 3.2 1B Instruct: 0W

🏆FLUX.1 [schnell] Leads(1/1)

Generation Latency

Time to render full-resolution image snapshot (seconds)

Benchmark

FLUX.1 [schnell]vsMeta: Llama 3.2 1B Instruct

Generation time

1.2svs—

FLUX.1 [schnell]: 1WMeta: Llama 3.2 1B Instruct: 0W

🏆FLUX.1 [schnell] Leads(1/1)

Cost Efficiency

API inference cost per 1,000 generated images

Benchmark

FLUX.1 [schnell]vsMeta: Llama 3.2 1B Instruct

Price per 1k images

$15/1kvs—

FLUX.1 [schnell]: 1WMeta: Llama 3.2 1B Instruct: 0W

Side-by-Side Benchmark Matrix

FLUX.1 [schnell]Meta: Llama 3.2 1B Instruct

Image Elo

FLUX.1 [schnell]1,142

Meta: Llama 3.2 1B Instruct—

Generation time

FLUX.1 [schnell]1.2s

Meta: Llama 3.2 1B Instruct—

Price per 1k images

FLUX.1 [schnell]$15/1k

Meta: Llama 3.2 1B Instruct—

Prompt adherence

FLUX.1 [schnell]84%

Meta: Llama 3.2 1B Instruct—

Text rendering

FLUX.1 [schnell]82%

Meta: Llama 3.2 1B Instruct—

Benchmark	FLUX.1 [schnell]	Meta: Llama 3.2 1B Instruct	Advantage Delta
Image Elo	1,142 seed-bootstrap · Aug 1, 2026	—	—
Generation time	1.2s seed-bootstrap · Aug 1, 2026	—	—
Price per 1k images	$15/1k seed-bootstrap · Aug 1, 2026	—	—
Prompt adherence	84% seed-bootstrap · Aug 1, 2026	—	—
Text rendering	82% seed-bootstrap · Aug 1, 2026	—	—

Workload Cost & Savings Calculator

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

Save 100% with Meta: Llama 3.2 1B Instruct

Monthly Image Production Volume25k images / month (25000 imgs)

Quick Presets:

FLUX.1 [schnell]$375.00 / mo

Rate: $15/1k

Meta: Llama 3.2 1B Instruct$0.00 / mo

Rate: $0/1k

Estimated Cost Delta

Meta: Llama 3.2 1B Instruct is estimated to save $375.00/month ($4,500/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 / visionFLUX.1 [schnell]

FLUX.1 [schnell] 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	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	FLUX.1 [schnell]	FLUX.1 [schnell] is the side marked multimodal in the catalog.

Cast Your Matchup Vote

0 total votes

Community Discussions (0)

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CompareLLM AI Matrix

Verified Head-to-Head Telemetry

LIVE BENCHMARK

Black Forest Labs

FLUX.1 [schnell]

Elo n/a

SWE-bench—

Speed (tok/s)—

Input / 1M Tokens$15/1k

Output / 1M Tokens$15/1k

Meta: Llama 3.2 1B Instruct

Elo n/a

SWE-bench—

Speed (tok/s)60k

Input / 1M Tokens$0.027/1M

Output / 1M Tokens$0.201/1M

Frequently Asked Questions

Which is better overall, FLUX.1 [schnell] or Meta: Llama 3.2 1B Instruct?: 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.1 [schnell] or Meta: Llama 3.2 1B Instruct?: 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.1 [schnell] or Meta: Llama 3.2 1B Instruct?: 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?: FLUX.1 [schnell] is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.
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.1 [schnell] and Meta: Llama 3.2 1B Instruct Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All FLUX.1 [schnell] matchups →|All Meta: Llama 3.2 1B Instruct matchups →

FLUX.1 [schnell] vs FLUX.1 [dev] (previous flux)FLUX.1 [schnell] vs FLUX.1.1 [pro] (next flux)

Side-by-Side Benchmark Matrix

FLUX.1 [schnell]Meta: Llama 3.2 1B Instruct

Image Elo

FLUX.1 [schnell]1,142

Meta: Llama 3.2 1B Instruct—

Generation time

FLUX.1 [schnell]1.2s

Meta: Llama 3.2 1B Instruct—

Price per 1k images

FLUX.1 [schnell]$15/1k

Meta: Llama 3.2 1B Instruct—

Prompt adherence

FLUX.1 [schnell]84%

Meta: Llama 3.2 1B Instruct—

Text rendering

FLUX.1 [schnell]82%

Meta: Llama 3.2 1B Instruct—

Benchmark	FLUX.1 [schnell]	Meta: Llama 3.2 1B Instruct	Advantage Delta
Image Elo	1,142 seed-bootstrap · Aug 1, 2026	—	—
Generation time	1.2s seed-bootstrap · Aug 1, 2026	—	—
Price per 1k images	$15/1k seed-bootstrap · Aug 1, 2026	—	—
Prompt adherence	84% seed-bootstrap · Aug 1, 2026	—	—
Text rendering	82% seed-bootstrap · Aug 1, 2026	—	—

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 / visionFLUX.1 [schnell]

FLUX.1 [schnell] 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	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	FLUX.1 [schnell]	FLUX.1 [schnell] is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, FLUX.1 [schnell] or Meta: Llama 3.2 1B Instruct?

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.1 [schnell] or Meta: Llama 3.2 1B Instruct?

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.1 [schnell] or Meta: Llama 3.2 1B Instruct?

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?

FLUX.1 [schnell] is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.

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.1 [schnell] and Meta: Llama 3.2 1B Instruct Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.