Updated October 29, 2026. Rank says Claude Opus 4.7 (Adaptive) is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Claude Opus 4.7 (Adaptive) has the higher public point estimate, 68.11 versus 53.59. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Anthropic
68.11/100
Estimated · Public rank #21
Conditional range 53.8–82.5
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Tool use, computer use, and multi-step task completion
Claude Opus 4.7 (Adaptive)
Claude Opus 4.7 (Adaptive) has the higher public agentic point estimate, 61.2 to 39.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Claude Opus 4.7 (Adaptive)
Claude Opus 4.7 (Adaptive) has the larger documented context window.
1K fresh input + 500 output tokens
Kimi K2.7 Code
Kimi K2.7 Code has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Kimi K2.7 Code
Kimi K2.7 Code has the lower estimated token cost for this stated workload. Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.
50K fresh input + 3K output tokens
Kimi K2.7 Code
Kimi K2.7 Code has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Claude Opus 4.7 (Adaptive) is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Directional only · BenchAlign v5.8
Claude Opus 4.7 (Adaptive) has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
| Category | Claude Opus 4.7 (Adaptive) | Kimi K2.7 Code | Basis | Reading |
|---|---|---|---|---|
| Agentic | 61.2Supported · #24/135 | 39.9Supported · #74/135 | Like-for-likeBenchAlign v5.8 lane · 7 vs 4 public rows | Claude Opus 4.7 (Adaptive) leads |
| Coding | 55.3Estimated · #30/156 | 42.0Supported · #65/156 | Directional onlyBenchAlign v5.8 lane · 3 vs 7 public rows | Directional only |
| Knowledge | 65.5Estimated · #27/179 | 54.7Estimated · #59/179 | Directional onlyBenchAlign v5.8 lane · 4 vs 0 public rows | Directional only |
| Reasoning | 57.4Unranked · 3 rankable rows | 79.5Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 51.2#52/58 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 76.6#68/157 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 58.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
1K fresh input + 500 output tokens
Kimi K2.7 Code has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Kimi K2.7 Code has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Kimi K2.7 Code has the lower modeled cost
Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Claude Opus 4.7 (Adaptive)
1M
Kimi K2.7 Code
256K
Claude Opus 4.7 (Adaptive)
Not sourced
Kimi K2.7 Code
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.7 (Adaptive)
Not published
Kimi K2.7 Code
$0.19 per 1M cached input tokens
Claude Opus 4.7 (Adaptive)
Not sourced
Kimi K2.7 Code
Not sourced
Claude Opus 4.7 (Adaptive)
Not sourced
Kimi K2.7 Code
Not sourced
Claude Opus 4.7 (Adaptive)
Not sourced
Kimi K2.7 Code
Not sourced
Claude Opus 4.7 (Adaptive)
Reasoning
Kimi K2.7 Code
Reasoning
Claude Opus 4.7 (Adaptive)
Proprietary
Kimi K2.7 Code
Open Weight
Claude Opus 4.7 (Adaptive)
Proprietary
Kimi K2.7 Code
Open Weight
Claude Opus 4.7 (Adaptive)
2026-04-16
Kimi K2.7 Code
2026-06-12
Claude Opus 4.7 (Adaptive) has the higher public point estimate, 68.11 versus 53.59. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Claude Opus 4.7 (Adaptive) scores higher for coding on the public lane, 55.3 to 42. Claude Opus 4.7 (Adaptive) is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Claude Opus 4.7 (Adaptive) has the higher public agentic tasks point estimate, 61.2 to 39.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.0175 on Claude Opus 4.7 (Adaptive) and $0.00295 on Kimi K2.7 Code; repository review costs $0.325 and $0.0595; the cache-heavy agent loop costs $1.35 and $0.097. Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.
Claude Opus 4.7 (Adaptive) has the larger documented context window: 1M, compared with 256K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
MCP Atlas
Claude Opus 4.7 (Adaptive) leads this result
OSWorld-Verified
Not directly comparable
CyberGym
Not directly comparable
OSWorld 2.0
Not directly comparable
JobBench
Not directly comparable
Kimi Claw 24/7
Not directly comparable
MCP Mark Verified
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Kimi Code Bench v2
Not directly comparable
ProgramBench
Not directly comparable
MLS-Bench Lite
Not directly comparable
CursorBench 3.2
Not directly comparable
OpenHarmony Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
FrontierMath (legacy)
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
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Last updated October 29, 2026
