LLM Comparison

Muse Spark 1.1 vs Kimi K2.7: benchmark scores, pricing & comparison.

Side-by-side Muse Spark 1.1 vs Kimi K2.7 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 #19AskClash overall scores 77.5 vs 56.8.
Pricing $1.25/$4.25 vs $0.95/$4.00Input and output token prices per 1M tokens when published.
Proprietary vs Open WeightMeta vs Moonshot AI.

Muse Spark 1.1 vs Kimi K2.7 benchmark comparison

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

MetricMuse Spark 1.1Kimi K2.7
Overall Score77.556.8
Leaderboard Rank#6#19
RWT7.57.5
HLE62.154.0
GPQA90.5
SWE-bench80.2
SWE-Pro61.558.6
Terminal-Bench80.066.7
DeepSWE30.5
OSWorld80.873.1
MCP Atlas88.176.0
Finance Agent57.244.9
CharXiv88.480.4
MMMU-Pro79.4
Tau290.1
MRCR54.1
Input Price (per 1M tokens)$1.25$0.95
Output Price (per 1M tokens)$4.25$4.00
Context Window1M256K
Benchmarks Published913

Muse Spark 1.1 vs Kimi K2.7 head-to-head charts

Muse Spark 1.1 leads 8 and Kimi K2.7 leads 0 of 9 shared benchmarks. Kimi K2.7 is cheaper on a defined 1M-input / 200K-output workload. Charts show only benchmarks both models publish.

Muse Spark 1.1Kimi K2.7
Overall
77.5Muse Spark 1.1
56.8Kimi K2.7
RWT
7.5Muse Spark 1.1
7.5Kimi K2.7
HLE
62.1Muse Spark 1.1
54.0Kimi K2.7
SWE-Pro
61.5Muse Spark 1.1
58.6Kimi K2.7
Terminal-Bench
80.0Muse Spark 1.1
66.7Kimi K2.7
OSWorld
80.8Muse Spark 1.1
73.1Kimi K2.7
MCP Atlas
88.1Muse Spark 1.1
76.0Kimi K2.7
Finance Agent
57.2Muse Spark 1.1
44.9Kimi K2.7
CharXiv
88.4Muse Spark 1.1
80.4Kimi K2.7
Muse Spark 1.1
Input$1.25
Output$4.25
Workload$2.10
Context1M
Kimi K2.7
Input$0.95
Output$4.00
Workload$1.75
Context256K

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

More Muse Spark 1.1 and Kimi K2.7 comparisons

Explore how Muse Spark 1.1 and Kimi K2.7 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.