Model comparison
Kimi K2.7 Code vs Nova Pro
Head-to-head evidence from 11 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); Nova Pro #206 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Kimi K2.7 Code and Nova Pro share 11 comparable benchmark results. 0 of 8 categories are comparable. 12 results are unique to Kimi K2.7 Code; 1 to Nova Pro.
Updated July 27, 2026- Shared results
- 11
- Kimi K2.7 Code only
- 12
- Nova Pro only
- 1
- Comparable categories
- 0 / 8
Benchmark data for Kimi K2.7 Code and Nova Pro is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 11 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.
Kimi K2.7 Code has the larger context window at 256K, compared with 128K for Nova Pro.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Kimi K2.7 Code | Nova Pro | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K2.7 Code$0.95 input / $4 output | Nova ProNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Kimi K2.7 CodeNot available | Nova Pro141 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Kimi K2.7 CodeNot available | Nova Pro0.81 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Kimi K2.7 Code256K | Nova Pro128K | Kimi K2.7 Code lists the larger context window. |
Benchmark Deep Dive
Agentic7 benchmarks
| Benchmark | Kimi K2.7 Code | Nova Pro | 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% | — | Not comparable |
| τ²-bench resultsSource | 90.1% | 14% | Kimi K2.7 Code leads |
| GDPval-AASource | 34.3% | — | Not comparable |
| GDPval-AASource | 1186 | — | Not comparable |
Coding6 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Kimi K2.7 Code | Nova Pro | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 42.0% | 7.7% | Kimi K2.7 Code leads |
| AA-GPQA DiamondSource | 89.6% | 49.9% | Kimi K2.7 Code leads |
| AA-HLESource | 32.8% | 3.4% | Kimi K2.7 Code leads |
| AA-Omniscience IndexSource | -10.7% | -47.6% | Kimi K2.7 Code leads |
| AA-Omniscience AccuracySource | 38.6% | 17.0% | Kimi K2.7 Code leads |
| AA-Omniscience Hallucination RateSource | 80.3% | 77.9% | Nova Pro leads |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | Kimi K2.7 Code | Nova Pro | Result |
|---|---|---|---|
| AA-IFBenchSource | 63.1% | 38.1% | Kimi K2.7 Code leads |
Frequently Asked Questions (3)
Can I compare Kimi K2.7 Code and Nova Pro 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 Nova Pro today?
Kimi K2.7 Code: $0.95 input / $4.00 output per 1M tokens Nova Pro: Pricing unavailable 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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Know when it’s worth switching models
The model to choose, the cheaper alternative, and the release we would wait on.
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