Math
Directional only- Claude Opus 4.7
- 38.6
- Kimi K2.6
- 67.1
- Weighted basis
- 2 vs 4 rows
- Reading
- Directional only
Claude Opus 4.7 vs Kimi K2.6
Updated August 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Opus 4.7 has the higher public score estimate, 72.33 versus 60.11, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
6 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
Which one for your work
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.
Long documents
Prompts that approach the documented context limit
Claude Opus 4.7
Claude Opus 4.7 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Kimi K2.6
Kimi K2.6 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Kimi K2.6
Kimi K2.6 has the lower estimated token cost for this stated workload. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.6 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Kimi K2.6
Kimi K2.6 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 Claude Opus 4.7 only
6 Shared
26 Kimi K2.6 only
- Shared results
- 6
- Claude Opus 4.7 only
- 2
- Kimi K2.6 only
- 26
- Like-for-like categories
- 0 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Agentic
Not comparable- Claude Opus 4.7
- Not measured
- Kimi K2.6
- 73.5
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
Coding
Not comparable- Claude Opus 4.7
- Not measured
- Kimi K2.6
- 64.4
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Opus 4.7
- Not measured
- Kimi K2.6
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Opus 4.7
- Not measured
- Kimi K2.6
- 42.2
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.7
- Not measured
- Kimi K2.6
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.7
- Not measured
- Kimi K2.6
- 79.8
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.7
- Not measured
- Kimi K2.6
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Claude Opus 4.7 | Kimi K2.6 | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 38.6 | 67.1 | Directional only2 vs 4 rows | Directional only |
| Agentic | Not measured | 73.5 | Not comparable0 vs 3 rows | Not comparable |
| Coding | Not measured | 64.4 | Not comparable0 vs 3 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 42.2 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 79.8 | Not comparable0 vs 2 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
FrontierMath v2 (Tier 4)
Math
FrontierMath v2 (Tiers 1-3)
Math
What each workload costs
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.
Chat turn
1K fresh input + 500 output tokens
- Claude Opus 4.7
- $0.0175
- Fits in one request
- Kimi K2.6
- $0.00295
- Fits in one request
Kimi K2.6 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.7
- $0.325
- Fits in one request
- Kimi K2.6
- $0.0595
- Fits in one request
Kimi K2.6 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Opus 4.7
- $1.35
- Fits in one request
- Cached input priced at the published list-input rate
- Kimi K2.6
- $0.249
- Fits in one request
- Cached input priced at the published list-input rate
Kimi K2.6 has the lower modeled cost
Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.6 has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Opus 4.7Kimi K2.6
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.7
Kimi K2.6
256K
API model ID
Claude Opus 4.7
claude-opus-4-7
Anthropic model ID documentationKimi K2.6
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.7
Not published
Kimi K2.6
Not published
Documented inputs
Claude Opus 4.7
text, image
Anthropic model overviewKimi K2.6
Not sourced
Documented outputs
Claude Opus 4.7
Kimi K2.6
Not sourced
Provider availability
Claude Opus 4.7
Generally Available · Claude API
Anthropic model overviewKimi K2.6
Not sourced
Reasoning profile
Claude Opus 4.7
Non-Reasoning
Kimi K2.6
Reasoning
Weight access
Claude Opus 4.7
Proprietary
Kimi K2.6
Open Weight
License
Claude Opus 4.7
Proprietary
Kimi K2.6
Open Weight
Release date
Claude Opus 4.7
2026-04-16
Kimi K2.6
2026-04-20
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- Claude Opus 4.7 has the higher public score estimate, 72.33 versus 60.11, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.325 vs $0.0595. Cache-heavy agent loop: $1.35 vs $0.249.
- Context tradeoff
- Claude Opus 4.7 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Claude Opus 4.7
API / mo$22,500
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Kimi K2.6
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence34 rows
Agentic
Gert Labs
Shared sourceClaude Opus 4.765.59%Kimi K2.656.82%Claude Opus 4.7 leads this result
ResearchClawBench
Shared sourceClaude Opus 4.720.7%Kimi K2.618.0%Claude Opus 4.7 leads this result
OSWorld 2.0
Shared sourceClaude Opus 4.713.9%Kimi K2.64.6%Claude Opus 4.7 leads this result
Terminal-Bench 2.0
Claude Opus 4.7—Kimi K2.666.7%SourceNot directly comparable
BrowseComp
Claude Opus 4.7—Kimi K2.683.2%SourceNot directly comparable
OSWorld-Verified
Claude Opus 4.7—Kimi K2.673.1%SourceNot directly comparable
Toolathlon
Claude Opus 4.7—Kimi K2.650%SourceNot directly comparable
MCP Atlas
Claude Opus 4.7—Kimi K2.655.9%SourceNot directly comparable
Claw-Eval
Claude Opus 4.7—Kimi K2.662.3%SourceNot directly comparable
DeepSearchQA
Claude Opus 4.7—Kimi K2.692.5%SourceNot directly comparable
WideResearch
Claude Opus 4.7—Kimi K2.680.8%SourceNot directly comparable
Coding
Vibe Code Bench
Shared sourceClaude Opus 4.771.00%Kimi K2.637.89%Claude Opus 4.7 leads this result
React Native Evals
Claude Opus 4.782.8%SourceKimi K2.6—Not directly comparable
FrontierCode 1.1 Main
Claude Opus 4.738.5%SourceKimi K2.6—Not directly comparable
SWE-bench Verified
Claude Opus 4.7—Kimi K2.680.2%SourceNot directly comparable
LiveCodeBench v6
Claude Opus 4.7—Kimi K2.689.6%SourceNot directly comparable
SWE-bench Pro
Claude Opus 4.7—Kimi K2.658.6%SourceNot directly comparable
SWE Multilingual
Claude Opus 4.7—Kimi K2.676.7%SourceNot directly comparable
SciCode
Claude Opus 4.7—Kimi K2.652.2%SourceNot directly comparable
Terminal-Bench 2.0
Claude Opus 4.7—Kimi K2.666.7%SourceNot directly comparable
cursorBench31
Claude Opus 4.7—Kimi K2.647.6%SourceNot directly comparable
Knowledge
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Opus 4.743.793%Kimi K2.638.966%Claude Opus 4.7 leads this result
FrontierMath v2 (Tier 4)
Shared sourceClaude Opus 4.722.917%Kimi K2.614.580%Claude Opus 4.7 leads this result
AIME26
Claude Opus 4.7—Kimi K2.696.4%SourceNot directly comparable
HMMT Feb 2026
Claude Opus 4.7—Kimi K2.692.7%SourceNot directly comparable
MMAnswerBench
Claude Opus 4.7—Kimi K2.686.0%SourceNot directly comparable
Multimodal
MMMU-Pro
Claude Opus 4.7—Kimi K2.679.4%SourceNot directly comparable
MMMU-Pro w/ Python
Claude Opus 4.7—Kimi K2.680.1%SourceNot directly comparable
CharXiv
Claude Opus 4.7—Kimi K2.680.4%SourceNot directly comparable
MathVision
Claude Opus 4.7—Kimi K2.687.4%SourceNot directly comparable
V*
Claude Opus 4.7—Kimi K2.696.9%SourceNot directly comparable
Frequently asked questions
Which is better, Claude Opus 4.7 or Kimi K2.6?
Claude Opus 4.7 has the higher public score estimate, 72.33 versus 60.11, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Claude Opus 4.7 or Kimi K2.6?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, Claude Opus 4.7 or Kimi K2.6?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, Claude Opus 4.7 or Kimi K2.6?
For the stated presets, chat costs $0.0175 on Claude Opus 4.7 and $0.00295 on Kimi K2.6; repository review costs $0.325 and $0.0595; the cache-heavy agent loop costs $1.35 and $0.249. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate. Kimi K2.6 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Opus 4.7 or Kimi K2.6?
Claude Opus 4.7 has the larger documented context window: 1M, compared with 256K.
Related comparisons
Last updated August 29, 2026
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