Model comparison
Kimi K2.7 Code vs Muse Spark
Head-to-head evidence from 15 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Kimi K2.7 Code #92 (Estimated); Muse Spark #16 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Kimi K2.7 Code and Muse Spark share 15 comparable benchmark results. 0 of 8 categories are comparable. 8 results are unique to Kimi K2.7 Code; 24 to Muse Spark.
Updated July 27, 2026- Shared results
- 15
- Kimi K2.7 Code only
- 8
- Muse Spark only
- 24
- Comparable categories
- 0 / 8
Benchmark data for Kimi K2.7 Code and Muse Spark is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 5 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Muse Spark has the larger context window at 262K, compared with 256K for Kimi K2.7 Code.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Kimi K2.7 Code | Δ | Muse Spark |
|---|---|---|---|
| Agentic | Kimi K2.7 CodeNot measured | MarginNo overlap | Muse Spark59.0 |
| Coding | Kimi K2.7 CodeNot measured | MarginNo overlap | Muse Spark67.8 |
| Reasoning | Kimi K2.7 CodeNot measured | MarginNo overlap | Muse Spark42.5 |
| Knowledge | Kimi K2.7 CodeNot measured | MarginNo overlap | Muse Spark50.4 |
| Math | Kimi K2.7 CodeNot measured | MarginNo overlap | Muse Spark32.9 |
| Multimodal | Kimi K2.7 CodeNot measured | MarginNo overlap | Muse Spark82.5 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Kimi K2.7 Code | Muse Spark | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K2.7 Code$0.95 input / $4 output | Muse SparkNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Kimi K2.7 CodeNot available | Muse SparkNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Kimi K2.7 CodeNot available | Muse SparkNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Kimi K2.7 Code256K | Muse Spark262K | Muse Spark lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | Kimi K2.7 Code | Muse Spark | Result |
|---|---|---|---|
| Kimi Claw 24/7Source | 46.9% | — | Not comparable |
| MCP AtlasSource | 76% | — | Not comparable |
| MCP Mark VerifiedSource | 81.1% | — | Not comparable |
| AA Agentic IndexSource | 29.6% | 28.7% | Kimi K2.7 Code leads |
| τ²-bench resultsSource | 90.1% | 91.5% | Muse Spark leads |
| GDPval-AASource | 34.3% | 32.2% | Kimi K2.7 Code leads |
| GDPval-AASource | 1186 | 1143 | Kimi K2.7 Code leads |
| Terminal-Bench 2.0Source | — | 59% | Not comparable |
| DeepSearchQASource | — | 74.8% | Not comparable |
| CyberGymSource | — | 43.5% | Not comparable |
| Claw-EvalSource | — | 63.8% | Not comparable |
Coding10 benchmarks
| Benchmark | Kimi K2.7 Code | Muse Spark | Result |
|---|---|---|---|
| Kimi Code Bench v2Source | 62.0% | — | Not comparable |
| ProgramBenchSource | 53.6% | — | Not comparable |
| MLS-Bench LiteSource | 35.1% | — | Not comparable |
| cursorBench32Source | 49.7% | — | Not comparable |
| AA Coding IndexSource | 60.8% | 58.6% | Kimi K2.7 Code leads |
| AA-SciCodeSource | 47.5% | 51.5% | Muse Spark leads |
| SWE-bench VerifiedSource | — | 77.4% | Not comparable |
| SWE-bench ProSource | — | 52.4% | Not comparable |
| LiveCodeBench ProSource | — | 80.0% | Not comparable |
| Vibe Code BenchSource | — | 19.67% | Not comparable |
Reasoning3 benchmarks
Knowledge11 benchmarks
| Benchmark | Kimi K2.7 Code | Muse Spark | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 42.0% | 43.1% | Muse Spark leads |
| AA-GPQA DiamondSource | 89.6% | 88.4% | Kimi K2.7 Code leads |
| AA-HLESource | 32.8% | 39.9% | Muse Spark leads |
| AA-Omniscience IndexSource | -10.7% | 4.1% | Muse Spark leads |
| AA-Omniscience AccuracySource | 38.6% | 44.6% | Muse Spark leads |
| AA-Omniscience Hallucination RateSource | 80.3% | 73.2% | Muse Spark leads |
| GPQA-DSource | — | 89.5% | Not comparable |
| HLESource | — | 50.4% | Not comparable |
| HLE w/o toolsSource | — | 42.8% | Not comparable |
| HealthBench HardSource | — | 42.8% | Not comparable |
| MedXpertQA (Text)Source | — | 52.6% | Not comparable |
Math2 benchmarks
Multimodal9 benchmarks
| Benchmark | Kimi K2.7 Code | Muse Spark | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1300 | — | Not comparable |
| CharXivSource | — | 86.4% | Not comparable |
| MMMU-ProSource | — | 80.4% | Not comparable |
| ERQASource | — | 64.7% | Not comparable |
| SimpleVQASource | — | 71.3% | Not comparable |
| ScreenSpot ProSource | — | 84.1% | Not comparable |
| ZeroBenchSource | — | 33.0% | Not comparable |
| MedXpertQA (MM)Source | — | 78.4% | Not comparable |
| AA-MMMU-ProSource | — | 80.5% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Kimi K2.7 Code | Muse Spark | Result |
|---|---|---|---|
| AA-IFBenchSource | 63.1% | 75.9% | Muse Spark leads |
Frequently Asked Questions (3)
Can I compare Kimi K2.7 Code and Muse Spark on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Kimi K2.7 Code and Muse Spark today?
Kimi K2.7 Code: $0.95 input / $4.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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The model to choose, the cheaper alternative, and the release we would wait on.
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