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

Llama 3.1 405B vs Meta: Muse Spark 1.1 benchmark

Name: Llama 3.1 405B vs Meta: Muse Spark 1.1 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

In this head-to-head showdown, Llama 3.1 405B is more budget-friendly at $3.5/1M per 1M output tokens. Review the complete breakdown below to determine which model best fits your performance and budget requirements.

Share Analysis:Share:

WhatsApp Telegram X LinkedIn

Executive Verdict Summary

Llama 3.1 405B holds empirical advantage (1 of 2 metrics)

Coding / SWESWE-bench

Llama 3.1 405BTop code solve rate

ReasoningGPQA / Live

Llama 3.1 405BTop logical accuracy

Best Value$/1M Tok

Llama 3.1 405BLowest API billing cost

Speed / TokTok/Sec

Llama 3.1 405BFastest stream rate

Verified Advantage Breakdown

2 benchmarks evaluated across capability, speed, and cost

Llama 3.1 405B (1)Meta: Muse Spark 1.1 (1)

Llama 3.1 405B

1 of 2 Wins

✓Output price

Meta: Muse Spark 1.1

1 of 2 Wins

✓Context window

Meta

Leads 1 of 2

Llama 3.1 405B

Meta flagship open-weight 405B dense foundation model with 128k context window.

Elo 1,370In: $1.25 · Out: $3.5/1M

Meta

Leads 1 of 2

Meta: Muse Spark 1.1

Auto-discovered from OpenRouter (meta/muse-spark-1.1). Preview until a second source matches.

In: $1.25 · Out: $4.25/1M

Top Rival Showdowns for Llama 3.1 405B

All Matchups

Compare vs:vs Claude Opus 4.5 vs GPT-5 vs Gemini 3 Pro vs Grok 4.6 vs DeepSeek V4 Pro

Preference Leader

No shared data

Single model data

Throughput Leader

No shared data

No latency data

Value per Dollar LeaderLlama 3.1 405B Wins

Llama 3.1 405B

$3.5 / 1M output

Capability Percentiles

Relative percentile scores computed across all active models in the benchmark catalog.

Multi-Dimensional Capability Radar

Leaders Matchup Capability Radar

Comparing top standard models across 6 key capability dimensions. Tap any spoke or dot to inspect.

Tap any node to inspect

0–100 %ile

Overall Matchup Breakdown

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

Llama 3.1 405B (8)vsMeta: Muse Spark 1.1 (1)

Llama 3.1 405B: 8W (89%)Meta: Muse Spark 1.1: 1W (11%)

← Llama 3.1 405B1 tiesMeta: Muse Spark 1.1 →

🏆Llama 3.1 405B Leads(5/5)

Intelligence & Reasoning

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

Benchmark

Llama 3.1 405BvsMeta: Muse Spark 1.1

Preference Elo

1,370vs—

Coding Elo

1,385vs—

SWE-bench

58.4%vs—

LiveBench

61.2%vs—

GPQA Diamond

75.2%vs—

Llama 3.1 405B: 5WMeta: Muse Spark 1.1: 0W

🏆Llama 3.1 405B Leads(2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Benchmark

Llama 3.1 405BvsMeta: Muse Spark 1.1

Output speed

42 tok/svs—

Time to first token

360 msvs—

Llama 3.1 405B: 2WMeta: Muse Spark 1.1: 0W

Tied (1-1)

Pricing & Capacity

Cost per million tokens and max context window length

Benchmark

Llama 3.1 405BvsMeta: Muse Spark 1.1

Output price

$3.5/1Mvs$4.25/1M+$0.75/1M

Input price

$1.25/1Mvs$1.25/1M

Context window

128kvs1M+921k

Llama 3.1 405B: 1WMeta: Muse Spark 1.1: 1W

Side-by-Side Benchmark Matrix

Llama 3.1 405BMeta: Muse Spark 1.1

Preference Elo

Llama 3.1 405B1,370

Meta: Muse Spark 1.1—

Coding Elo

Llama 3.1 405B1,385

Meta: Muse Spark 1.1—

LiveBench

Llama 3.1 405B61.2%

Meta: Muse Spark 1.1—

SWE-bench

Llama 3.1 405B58.4%

Meta: Muse Spark 1.1—

GPQA Diamond

Llama 3.1 405B75.2%

Meta: Muse Spark 1.1—

Time to first token

Llama 3.1 405B360 ms

Meta: Muse Spark 1.1—

Output speed

Llama 3.1 405B42 tok/s

Meta: Muse Spark 1.1—

Input price

Llama 3.1 405B$1.25/1M

Meta: Muse Spark 1.1$1.25/1M

Output priceLlama 3.1 405B +$0.75/1M

Llama 3.1 405B$3.5/1M

Meta: Muse Spark 1.1$4.25/1M

Context windowMeta: Muse Spark 1.1 +921k

Llama 3.1 405B128k

Meta: Muse Spark 1.11M

Benchmark	Llama 3.1 405B	Meta: Muse Spark 1.1	Advantage Delta
Preference Elo	1,370 lmarena · Aug 17, 2026	—	—
Coding Elo	1,385 lmarena · Aug 17, 2026	—	—
LiveBench	61.2% livebench · Aug 17, 2026	—	—
SWE-bench	58.4% swebench · Aug 17, 2026	—	—
GPQA Diamond	75.2% seed-bootstrap · Aug 1, 2026	—	—
Time to first token	360 ms seed-bootstrap · Aug 1, 2026	—	—
Output speed	42 tok/s seed-bootstrap · Aug 1, 2026	—	—
Input price	$1.25/1M seed-bootstrap · Aug 1, 2026	$1.25/1M openrouter · Aug 17, 2026	—
Output price	$3.5/1M seed-bootstrap · Aug 1, 2026	$4.25/1M openrouter · Aug 17, 2026	+$0.75/1M
Context window	128k seed-bootstrap · Aug 1, 2026	1M openrouter · Aug 17, 2026	+921k

Workload Cost & Savings Calculator

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

Save 10% with Llama 3.1 405B

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

Llama 3.1 405B$96.25 / mo

In: $43.75Out: $52.5

Meta: Muse Spark 1.1$107.50 / mo

In: $43.75Out: $63.75

Estimated Cost Delta

Llama 3.1 405B is estimated to save $11.25/month ($135/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Llama 3.1 405B:Pick Llama 3.1 405B when you are optimizing output cost.
2
Meta: Muse Spark 1.1:Pick Meta: Muse Spark 1.1 for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatLlama 3.1 405B

Lower output list price ($3.5/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGMeta: Muse Spark 1.1

Larger window (1M).

Screenshots / visionMeta: Muse Spark 1.1

Meta: Muse Spark 1.1 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	Llama 3.1 405B	Lower output list price ($3.5/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Meta: Muse Spark 1.1	Larger window (1M).
Screenshots / vision	Meta: Muse Spark 1.1	Meta: Muse Spark 1.1 is the side marked multimodal in the catalog.

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!

Social Share Card Preview

Dynamic OpenGraph banner generated at /compare/llama-3-1-405b-vs-muse-spark-1-1/opengraph-image

Save PNG

CompareLLM AI Matrix

Verified Head-to-Head Telemetry

LIVE BENCHMARK

Meta

Llama 3.1 405B

Elo 1,370

SWE-bench58.4%

Speed (tok/s)42 tok/s

Input / 1M Tokens$1.25/1M

Output / 1M Tokens$3.5/1M

Meta

Meta: Muse Spark 1.1

Elo n/a

SWE-bench—

Speed (tok/s)1M

Input / 1M Tokens$1.25/1M

Output / 1M Tokens$4.25/1M

Frequently Asked Questions

Which is better overall, Llama 3.1 405B or Meta: Muse Spark 1.1?: 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, Llama 3.1 405B or Meta: Muse Spark 1.1?: 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?: Llama 3.1 405B output tokens are $3.5/1M versus $4.25/1M. Input prices and retry rates still move the real bill.
Which is faster, Llama 3.1 405B or Meta: Muse Spark 1.1?: We do not have TTFT for both models.
Which has the larger context window?: Meta: Muse Spark 1.1 accepts 1M tokens versus 128k.
Can I self-host either model?: Llama 3.1 405B 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. Llama 3.1 405B and Meta: Muse Spark 1.1 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Llama 3.1 405B matchups →|All Meta: Muse Spark 1.1 matchups →

Llama 3.1 405B vs Llama 3.1 70B (next llama)Llama 4 Maverick vs Llama 3.1 405B Llama 4 Scout vs Llama 3.1 405B Llama 3.3 70B vs Llama 3.1 405B Mistral Large 3 vs Llama 3.1 405B Doubao Pro 1.5 vs Llama 3.1 405B

Capability Percentiles

Relative percentile scores computed across all active models in the benchmark catalog.

Multi-Dimensional Capability Radar

Leaders Matchup Capability Radar

Comparing top standard models across 6 key capability dimensions. Tap any spoke or dot to inspect.

Tap any node to inspect

0–100 %ile

Side-by-Side Benchmark Matrix

Llama 3.1 405BMeta: Muse Spark 1.1

Preference Elo

Llama 3.1 405B1,370

Meta: Muse Spark 1.1—

Coding Elo

Llama 3.1 405B1,385

Meta: Muse Spark 1.1—

LiveBench

Llama 3.1 405B61.2%

Meta: Muse Spark 1.1—

SWE-bench

Llama 3.1 405B58.4%

Meta: Muse Spark 1.1—

GPQA Diamond

Llama 3.1 405B75.2%

Meta: Muse Spark 1.1—

Time to first token

Llama 3.1 405B360 ms

Meta: Muse Spark 1.1—

Output speed

Llama 3.1 405B42 tok/s

Meta: Muse Spark 1.1—

Input price

Llama 3.1 405B$1.25/1M

Meta: Muse Spark 1.1$1.25/1M

Output priceLlama 3.1 405B +$0.75/1M

Llama 3.1 405B$3.5/1M

Meta: Muse Spark 1.1$4.25/1M

Context windowMeta: Muse Spark 1.1 +921k

Llama 3.1 405B128k

Meta: Muse Spark 1.11M

Benchmark

Llama 3.1 405B

Meta: Muse Spark 1.1

Advantage Delta

Preference Elo

1,370

lmarena · Aug 17, 2026

—

Coding Elo

1,385

lmarena · Aug 17, 2026

—

LiveBench

61.2%

livebench · Aug 17, 2026

—

SWE-bench

58.4%

swebench · Aug 17, 2026

—

GPQA Diamond

75.2%

seed-bootstrap · Aug 1, 2026

—

Time to first token

360 ms

seed-bootstrap · Aug 1, 2026

—

Output speed

42 tok/s

seed-bootstrap · Aug 1, 2026

—

Input price

$1.25/1M

seed-bootstrap · Aug 1, 2026

$1.25/1M

openrouter · Aug 17, 2026

—

Output price

$3.5/1M

seed-bootstrap · Aug 1, 2026

$4.25/1M

openrouter · Aug 17, 2026

+$0.75/1M

Context window

128k

seed-bootstrap · Aug 1, 2026

openrouter · Aug 17, 2026

+921k

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatLlama 3.1 405B

Lower output list price ($3.5/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGMeta: Muse Spark 1.1

Larger window (1M).

Screenshots / visionMeta: Muse Spark 1.1

Meta: Muse Spark 1.1 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	Llama 3.1 405B	Lower output list price ($3.5/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Meta: Muse Spark 1.1	Larger window (1M).
Screenshots / vision	Meta: Muse Spark 1.1	Meta: Muse Spark 1.1 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Llama 3.1 405B or Meta: Muse Spark 1.1?

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, Llama 3.1 405B or Meta: Muse Spark 1.1?

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?

Llama 3.1 405B output tokens are $3.5/1M versus $4.25/1M. Input prices and retry rates still move the real bill.

Which is faster, Llama 3.1 405B or Meta: Muse Spark 1.1?

We do not have TTFT for both models.

Which has the larger context window?

Meta: Muse Spark 1.1 accepts 1M tokens versus 128k.

Can I self-host either model?

Llama 3.1 405B 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. Llama 3.1 405B and Meta: Muse Spark 1.1 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.