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  3. DeepSeek R1 vs Meta: Muse Spark 1.1

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

DeepSeek R1 vs Meta: Muse Spark 1.1 benchmark

This page compares DeepSeek R1 (DeepSeek) and Meta: Muse Spark 1.1 (Meta) using the latest snapshots we have as of Aug 16, 2026. DeepSeek R1 wins on output price at $2.5/1M. Meta: Muse Spark 1.1 has the larger context window (1M tokens). 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.

Quick answer

DeepSeek R1 vs Meta: Muse Spark 1.1 is a dated snapshot, not a lab score. DeepSeek R1 is the cheaper output token. Elo is crowd preference, not an exam. Pick the column that matches the job.
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DeepSeek

DeepSeek R1

Open-weights reasoning model trained with large-scale RL.

Elo 1,358$2.5/1M out

Meta

Meta: Muse Spark 1.1

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

$4.25/1M out

Swap or Add Any Model to this Matchup

Instant pairwise benchmark recalculation across 400+ LLMs

Model A
Model B
Quick vs:vs Claude Opus 4.5vs Claude Opus 4.6vs Claude Sonnet 4.5vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek R1 (8)vsMeta: Muse Spark 1.1 (1)
DeepSeek R1: 8W (89%)Overall: DeepSeek R1Meta: Muse Spark 1.1: 1W (11%)
← DeepSeek R1Meta: Muse Spark 1.1 →
DeepSeek R1 (4/5)

Intelligence & Reasoning

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

Preference Elo
1,358vs—
Coding Elo
—vs—
SWE-bench
65.2%vs—
LiveBench
59%vs—
GPQA Diamond
79.8%vs—
DeepSeek R1: 4WMeta: Muse Spark 1.1: 0W
DeepSeek R1 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed
62 tok/svs—
Time to first token
420 msvs—
DeepSeek R1: 2WMeta: Muse Spark 1.1: 0W
DeepSeek R1 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price
$2.5/1Mvs$4.25/1M
Input price
$0.7/1Mvs$1.25/1M
Context window
64kvs1M
DeepSeek R1: 2WMeta: Muse Spark 1.1: 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 AdvantageDeepSeek R1 5/6
Preference Elo

No shared data

No shared data

Throughput Speed

No shared data

No shared data

Price Efficiency

DeepSeek R1

$1.75/1M cheaper

Full Benchmark Score Matrix

—
Elo
High is better
1,358
EloHigh is better
—
LiveBench
High is better
59%
LiveBenchHigh is better
—
SWE-bench
High is better
65.2%
SWE-benchHigh is better
—
GPQA
High is better
79.8%
GPQAHigh is better
—
TTFT
Low is better
420 ms
TTFTLow is better
—
Speed
High is better
62 tok/s
SpeedHigh is better
—
In $
Low is better
+79%$0.7/1M
In $Low is better
$1.25/1M
Out $
Low is better
+70%$2.5/1M
Out $Low is better
$4.25/1M
Context
High is better
64k
ContextHigh is better
1M+1538%
Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 42% with DeepSeek R1
Monthly Volume50M tokens/mo
Quick Presets:
Token Ratio (In vs Out)70% In / 30% Out
RAG / Search (10% out)Coding / Chat (50% out)
DeepSeek R1$62.00 / mo
In: $24.50Out: $37.50
Meta: Muse Spark 1.1$107.50 / mo
In: $43.75Out: $63.75
Estimated Cost Delta

DeepSeek R1 is estimated to save $45.50/month ($546/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

  • 1
    DeepSeek R1:Pick DeepSeek R1 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 chatDeepSeek R1

Lower output list price ($2.5/1M).

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 WorkloadRecommended PickEvaluation Rationale
Repo / coding agentsinsufficient dataNeed SWE-bench on both sides.
High-volume chatDeepSeek R1Lower output list price ($2.5/1M).
Voice / low-latency UIinsufficient dataNeed TTFT on both sides.
Long-document RAGMeta: Muse Spark 1.1Larger window (1M).
Screenshots / visionMeta: Muse Spark 1.1Meta: Muse Spark 1.1 is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news
launchJan 20, 2025

DeepSeek R1 stays as the open-weights RL baseline

Jan 2025 reasoning model. Still searched. V4 Pro is the newer DeepSeek buy.

Community Sentiment

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CompareLLM Matrix

Verified head-to-head card

LIVE
VS
DeepSeek

DeepSeek R1

Elo 1,358
LiveBench59%
SWE-bench65.2%
Speed62 tok/s
Output cost$2.5/1M
Meta

Meta: Muse Spark 1.1

Elo n/a
LiveBench—
SWE-bench—
Speed—
Output cost$4.25/1M

Frequently Asked Questions

Which is better overall, DeepSeek R1 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, DeepSeek R1 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?
DeepSeek R1 output tokens are $2.5/1M versus $4.25/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek R1 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 64k.
Can I self-host either model?
DeepSeek R1 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. DeepSeek R1 and Meta: Muse Spark 1.1 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Related news

  • launch · Jan 20, 2025

    DeepSeek R1 stays as the open-weights RL baseline

Similar Head-to-Head Comparisons

All DeepSeek R1 matchups →|All Meta: Muse Spark 1.1 matchups →
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