GPT-4o-mini vs Muse Spark 1.2

OpenAI

69#172

meta

81#84

Signal-by-Signal Comparison

Signal	GPT-4o-mini	Delta	Muse Spark 1.2
Capabilities	67	-33	100
Benchmarks	76	+76	0
Pricing	99	+4	96
Context window size	81	-14	96
Recency	0	-100	100
Output Capacity	67	-28	96
Overall Result	2 wins	of 6	4 wins

Muse Spark 1.2 wins 4 of 6 signals

Score History

Score History (32 data points)

GPT-4o-miniMuse Spark 1.2

GPT-4o-mini

69.3

current score

Leader

Muse Spark 1.2

right now

Muse Spark 1.2

80.9

current score

LMMarketCap.com

Interactive Price Comparison

Quick presets

Monthly API calls

100Kcalls/month

Avg. input tokens/call

1,000tokens (~1,333 chars)

Avg. output tokens/call

500tokens (~667 chars)

GPT-4o-mini

OpenAI

Best Value

Per request$0.000450

Daily$1.50

Monthly$45.00

Annual$540.00

Muse Spark 1.2

meta

Per request$0.003375

Daily$11.25

Monthly$337.50

Annual$4050.00

GPT-4o-mini saves you $292.50/month

That's $3510.00/year compared to Muse Spark 1.2 at your current usage level of 100K calls/month.

87% cheaper

Choose GPT-4o-mini for cost optimization

GPT-4o-mini pricing:

Input:$0.15/M tokens

Output:$0.60/M tokens

Muse Spark 1.2 pricing:

Input:$1.25/M tokens

Output:$4.25/M tokens

GPT-4o-mini

OpenAI

Composite Score

Winner

Muse Spark 1.2

meta

Composite Score

Signal-by-Signal Comparison

Metric	GPT-4o-mini	Muse Spark 1.2	Winner
Overall Score	69	81	Muse Spark 1.2
Rank	#172	#84	Muse Spark 1.2
Quality Rank	#172	#84	Muse Spark 1.2
Adoption Rank	#172	#84	Muse Spark 1.2
Parameters	--	--	--
Context Window	128K	1049K	Muse Spark 1.2
Pricing	$0.15/$0.60/M	$1.25/$4.25/M	--
Signal Scores
Capabilities	67	100	Muse Spark 1.2
Benchmarks	76	--	GPT-4o-mini
Pricing	99	96	GPT-4o-mini
Context window size	81	96	Muse Spark 1.2
Recency	0	100	Muse Spark 1.2
Output Capacity	67	96	Muse Spark 1.2

Benchmark Head-to-Head(11 benchmarks)

GPT-4o-mini: 0Muse Spark: 0

GPT-4o-mini

Muse Spark

Normalized 0-100%

MMLU

82%-

MMLU-Pro

63.1%-

GPQA Diamond

40.2%-

MATH-500

70.2%-

HumanEval

87.2%-

GSM8K

93.2%-

IFEval

80.4%-

BBH

80.4%-

ARC-Challenge

96.4%-

HellaSwag

93%-

Arena Elo

1222-

Benchmark Interpretation

Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.

GPT-4o-miniCompetitive

Scores 69/100 (rank #172), placing it in the top 41% of all 290 models tracked.

Raw Quality0/100

Cost Efficiency0/100

Speed0/100

Muse Spark 1.2Strong Performer

Scores 81/100 (rank #84), placing it in the top 71% of all 290 models tracked.

Raw Quality0/100

Cost Efficiency0/100

Speed0/100

Muse Spark 1.2 has a 12-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.

When to Use Each Model

Choose GPT-4o-mini when you need:

High-volume production workloads where API costs must be minimized

Choose Muse Spark 1.2 when you need:

Processing long documents or large codebases (1049K token context)
Step-by-step reasoning and chain-of-thought problem solving

Cost-Performance Analysis

GPT-4o-miniBest Value

Input cost$0.15/M tokens

Output cost$0.60/M tokens

Cost per quality point$0.011

Est. monthly (1M tokens/day)$11.25

Muse Spark 1.2

Input cost$1.25/M tokens

Output cost$4.25/M tokens

Cost per quality point$0.068

Est. monthly (1M tokens/day)$82.50

GPT-4o-mini offers 86% better value per quality point. At 1M tokens/day, you'd spend $11.25/month with GPT-4o-mini vs $82.50/month with Muse Spark 1.2 - a $71.25 monthly difference.

Latency & Speed

GPT-4o-miniFaster

Speed score0/100

Muse Spark 1.2

Speed score0/100

Both models have comparable response speeds. For most applications, the latency difference is negligible.

When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.

Example Use Cases

Code generation & review

Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring

GPT-4o-mini

Customer support chatbot

Suitable for user-facing chat with competitive response times. GPT-4o-mini also offers lower per-token costs for high-volume support

GPT-4o-mini

Long document analysis

Larger context window (1049K tokens) can process longer documents, contracts, and research papers in a single pass

Muse Spark 1.2

Batch data extraction

Lower output pricing ($0.60/M) reduces costs when processing thousands of records daily

GPT-4o-mini

Creative writing & content

Higher overall composite score (81/100) correlates with better nuance, coherence, and style in long-form content

Muse Spark 1.2

Image understanding & OCR

Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly

GPT-4o-mini

Which Should You Choose?

Our recommendation:

Muse Spark 1.2

Muse Spark 1.2 clearly outperforms GPT-4o-mini with a significant 11.600000000000009-point lead. For most general use cases, Muse Spark 1.2 is the stronger choice. However, GPT-4o-mini may still excel in niche scenarios.

By Use Case

Best for Quality

GPT-4o-mini

Marginally better benchmark scores; both are excellent

Best for Cost

GPT-4o-mini

86% lower pricing; better value at scale

Best for Reliability

GPT-4o-mini

Higher uptime and faster response speeds

Best for Prototyping

GPT-4o-mini

Stronger community support and better developer experience

Best for Production

GPT-4o-mini

Wider enterprise adoption and proven at scale

GPT-4o-mini

by OpenAI

Choose for Quality - Marginally better benchmark scores; both are excellent
Choose for Cost - 86% lower pricing; better value at scale
Choose for Reliability - Higher uptime and faster response speeds
Choose for Prototyping - Stronger community support and better developer experience
Choose for Production - Wider enterprise adoption and proven at scale