Model comparison
Claude Opus 4.7 (Adaptive) vs Kimi K2.7 Code
Head-to-head evidence from 17 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Opus 4.7 (Adaptive) #14 (Estimated); Kimi K2.7 Code #92 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Opus 4.7 (Adaptive) and Kimi K2.7 Code share 17 comparable benchmark results. 0 of 8 categories are comparable. 22 results are unique to Claude Opus 4.7 (Adaptive); 6 to Kimi K2.7 Code.
Updated July 27, 2026- Shared results
- 17
- Claude Opus 4.7 (Adaptive) only
- 22
- Kimi K2.7 Code only
- 6
- Comparable categories
- 0 / 8
Benchmark data for Claude Opus 4.7 (Adaptive) and Kimi K2.7 Code is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 17 shared benchmark results across 6 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.
Claude Opus 4.7 (Adaptive) is priced at $5.00 input / $25.00 output per 1M tokens, versus $0.95 input / $4.00 output per 1M tokens for Kimi K2.7 Code. Claude Opus 4.7 (Adaptive) has the larger context window at 1M, 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 | Claude Opus 4.7 (Adaptive) | Δ | Kimi K2.7 Code |
|---|---|---|---|
| Agentic | Claude Opus 4.7 (Adaptive)75.1 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Coding | Claude Opus 4.7 (Adaptive)78.6 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Reasoning | Claude Opus 4.7 (Adaptive)75.8 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Knowledge | Claude Opus 4.7 (Adaptive)60.0 | MarginNo overlap | Kimi K2.7 CodeNot measured |
| Multimodal | Claude Opus 4.7 (Adaptive)65.1 | MarginNo overlap | Kimi K2.7 CodeNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Opus 4.7 (Adaptive) | Kimi K2.7 Code | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.7 (Adaptive)$5 input / $25 output | Kimi K2.7 Code$0.95 input / $4 output | Kimi K2.7 Code has the lower combined listed price. |
| Generation speedtokens per second | Claude Opus 4.7 (Adaptive)Not available | Kimi K2.7 CodeNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Opus 4.7 (Adaptive)Not available | Kimi K2.7 CodeNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Opus 4.7 (Adaptive)1M | Kimi K2.7 Code256K | Claude Opus 4.7 (Adaptive) lists the larger context window. |
Benchmark Deep Dive
Agentic14 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | Kimi K2.7 Code | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 69.4% | — | Not comparable |
| BrowseCompSource | 79.3% | — | Not comparable |
| MCP AtlasSource | 77.3% | 76% | Claude Opus 4.7 (Adaptive) leads |
| OSWorld-VerifiedSource | 78% | — | Not comparable |
| CyberGymSource | 73.1% | — | Not comparable |
| AA Agentic IndexSource | 44.4% | 29.6% | Claude Opus 4.7 (Adaptive) leads |
| τ²-bench resultsSource | 88.6% | 90.1% | Kimi K2.7 Code leads |
| GDPval-AASource | 49.7% | 34.3% | Claude Opus 4.7 (Adaptive) leads |
| GDPval-AASource | 1493 | 1186 | Claude Opus 4.7 (Adaptive) leads |
| OSWorld 2.0Source | 18.2% | — | Not comparable |
| JobBenchSource | 45.9% | — | Not comparable |
| AA ITBenchSource | 46.7% | — | Not comparable |
| Kimi Claw 24/7Source | — | 46.9% | Not comparable |
| MCP Mark VerifiedSource | — | 81.1% | Not comparable |
Coding9 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | Kimi K2.7 Code | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 87.6% | — | Not comparable |
| SWE-bench ProSource | 64.3% | — | Not comparable |
| Terminal-Bench 2.0Source | 69.4% | — | Not comparable |
| AA Coding IndexSource | 73.6% | 60.8% | Claude Opus 4.7 (Adaptive) leads |
| AA-SciCodeSource | 54.5% | 47.5% | Claude Opus 4.7 (Adaptive) leads |
| 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 |
Reasoning5 benchmarks
Knowledge10 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | Kimi K2.7 Code | Result |
|---|---|---|---|
| GPQASource | 94.2% | — | Not comparable |
| GPQA-DSource | 94.2% | — | Not comparable |
| HLESource | 54.7% | — | Not comparable |
| HLE w/o toolsSource | 46.9% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 53.5% | 42.0% | Claude Opus 4.7 (Adaptive) leads |
| AA-GPQA DiamondSource | 91.4% | 89.6% | Claude Opus 4.7 (Adaptive) leads |
| AA-HLESource | 39.6% | 32.8% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience IndexSource | 26.2% | -10.7% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience AccuracySource | 45.8% | 38.6% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience Hallucination RateSource | 36.2% | 80.3% | Claude Opus 4.7 (Adaptive) leads |
Math1 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | Kimi K2.7 Code | Result |
|---|---|---|---|
| FrontierMath (legacy)Source | 43.8% | — | Not comparable |
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | Kimi K2.7 Code | Result |
|---|---|---|---|
| AA-IFBenchSource | 58.6% | 63.1% | Kimi K2.7 Code leads |
Frequently Asked Questions (3)
Can I compare Claude Opus 4.7 (Adaptive) and Kimi K2.7 Code 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 Claude Opus 4.7 (Adaptive) and Kimi K2.7 Code today?
Claude Opus 4.7 (Adaptive): $5.00 input / $25.00 output per 1M tokens 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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