LLM Comparison

Muse Spark 1.1 vs GPT-5.6 Luna: benchmark scores, pricing & comparison.

Side-by-side Muse Spark 1.1 vs GPT-5.6 Luna comparison across SWE-bench, GPQA, HLE, Terminal-Bench, coding agent scores, token pricing, context window, and AskClash RWT. Green marks the winner on each benchmark.

Rank #6 vs #13AskClash overall scores 77.5 vs 67.9.
Pricing $1.25/$4.25 vs $1.00/$6.00Input and output token prices per 1M tokens when published.
Proprietary vs ProprietaryMeta vs OpenAI.

Muse Spark 1.1 vs GPT-5.6 Luna benchmark comparison

Green cells highlight the winning model for each metric. Scores are cached from the AskClash LLM leaderboard snapshot.

MetricMuse Spark 1.1GPT-5.6 Luna
Overall Score77.567.9
Leaderboard Rank#6#13
RWT7.59.5
Coding Agent Index75.0
HLE62.1
GPQA92.3
SWE-Pro61.562.7
SWE-Atlas81.0
Terminal-Bench80.084.7
DeepSWE67.2
OSWorld80.8
MCP Atlas88.1
Finance Agent57.255.0
CharXiv88.4
MMMU-Pro78.4
ARC-AGI 259.5
MRCR54.141.3
Input Price (per 1M tokens)$1.25$1.00
Output Price (per 1M tokens)$4.25$6.00
Context Window1M1M
Benchmarks Published912

Muse Spark 1.1 vs GPT-5.6 Luna head-to-head charts

Muse Spark 1.1 leads 3 and GPT-5.6 Luna leads 3 of 6 shared benchmarks. Muse Spark 1.1 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Muse Spark 1.1GPT-5.6 Luna
Overall
77.5Muse Spark 1.1
67.9GPT-5.6 Luna
RWT
7.5Muse Spark 1.1
9.5GPT-5.6 Luna
SWE-Pro
61.5Muse Spark 1.1
62.7GPT-5.6 Luna
Terminal-Bench
80.0Muse Spark 1.1
84.7GPT-5.6 Luna
Finance Agent
57.2Muse Spark 1.1
55.0GPT-5.6 Luna
MRCR
54.1Muse Spark 1.1
41.3GPT-5.6 Luna
Muse Spark 1.1
Input$1.25
Output$4.25
Workload$2.10
Context1M
GPT-5.6 Luna
Input$1.00
Output$6.00
Workload$2.20
Context1M

Workload = published cost of 1M input + 200K output tokens. Open the live leaderboard for interactive compare charts.

More Muse Spark 1.1 and GPT-5.6 Luna comparisons

Explore how Muse Spark 1.1 and GPT-5.6 Luna stack up against other top-ranked LLMs.

How to read this comparison

Benchmark scores

Higher is better for all benchmark scores (SWE-bench, GPQA, HLE, Terminal-Bench, etc.). Green marks the model with the higher score.

Token pricing

Lower is better for input and output prices. Green marks the cheaper model per 1M tokens.

Coverage matters

Models with fewer disclosed benchmark cells may have inflated percentile scores. Check the benchmark cell count for context.

This comparison page is generated from the AskClash LLM leaderboard cache. Open the live leaderboard for real-time scores and interactive filtering.