Long documents
Prompts that approach the documented context limit
Kimi K3.1 Pro
Kimi K3.1 Pro has the larger documented context window.
Confidence: documented
Claude Opus 5.5 vs Kimi K3.1 Pro
Updated October 23, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Share or export
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 10
- Claude Opus 5.5 only
- 53
- Kimi K3.1 Pro only
- 32
- Like-for-like categories
- 0 / 8
Supported: Claude Opus 5.5How the comparison works
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.
Chat turn cost
1K fresh input + 500 output tokens
Kimi K3.1 Pro
Kimi K3.1 Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Kimi K3.1 Pro
Kimi K3.1 Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Kimi K3.1 Pro
Kimi K3.1 Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Kimi K3.1 Pro is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Kimi K3.1 Pro is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Which one for a specific job
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.
82.7Claude Opus 5.5—Kimi K3.1 Pro
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
53 Claude Opus 5.5 only
10 Shared
32 Kimi K3.1 Pro only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
OSWorld 2.0Agentic
Normalized gap 30.9Claude Opus 5.5:48.7%Kimi K3.1 Pro:79.6%HLE w/o toolsKnowledge
Normalized gap 13.3Claude Opus 5.5:64.4%Kimi K3.1 Pro:51.1%OfficeQA ProMultimodal
Normalized gap 0.3Claude Opus 5.5:67.7%Kimi K3.1 Pro:67.4%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
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.
Multimodal
Directional only- Claude Opus 5.5
- 91.8
- #3/56
- Kimi K3.1 Pro
- 98.0
- #1/56
- Basis
- Provisional lane · 1 vs 3 weighted rows
- Reading
- Directional only
Agentic
Not comparable- Claude Opus 5.5
- 90.4
- Supported · #1/132
- Kimi K3.1 Pro
- Not ranked
- Basis
- BenchAlign v5.8 lane · 15 vs 16 public rows
- Reading
- Not comparable
Coding
Not comparable- Claude Opus 5.5
- 82.7
- Supported · #2/153
- Kimi K3.1 Pro
- Not ranked
- Basis
- BenchAlign v5.8 lane · 11 vs 6 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Opus 5.5
- 86.1
- #4/27
- Kimi K3.1 Pro
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Opus 5.5
- 89.8
- Supported · #1/187
- Kimi K3.1 Pro
- Not ranked
- Basis
- BenchAlign v5.8 lane · 16 vs 5 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 5.5
- Not ranked
- Kimi K3.1 Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 5.5
- Not ranked
- Kimi K3.1 Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 5.5
- 97.3
- Unranked · 4 rankable rows
- Kimi K3.1 Pro
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 5.5 | Kimi K3.1 Pro | Basis | Reading |
|---|---|---|---|---|
| Multimodal | 91.8#3/56 | 98.0#1/56 | Directional onlyProvisional lane · 1 vs 3 weighted rows | Directional only |
| Agentic | 90.4Supported · #1/132 | Not ranked | Not comparableBenchAlign v5.8 lane · 15 vs 16 public rows | Not comparable |
| Coding | 82.7Supported · #2/153 | Not ranked | Not comparableBenchAlign v5.8 lane · 11 vs 6 public rows | Not comparable |
| Reasoning | 86.1#4/27 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 89.8Supported · #1/187 | Not ranked | Not comparableBenchAlign v5.8 lane · 16 vs 5 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 97.3Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 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.
Supported evidence per lane · bars run 0–100Methodology
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 5.5
- $0.014
- Fits in one request
- Kimi K3.1 Pro
- $0.007
- Fits in one request
Kimi K3.1 Pro has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 5.5
- $0.26
- Fits in one request
- Kimi K3.1 Pro
- $0.13
- Fits in one request
Kimi K3.1 Pro 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 5.5
- $0.32
- Fits in one request
- Kimi K3.1 Pro
- $0.18
- Fits in one request
Kimi K3.1 Pro has the lower modeled cost
Costs use the listed standard API rates.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Opus 5.5Kimi K3.1 Pro
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 5.5
Kimi K3.1 Pro
1.05M
API model ID
Claude Opus 5.5
claude-opus-5-5
Anthropic Claude Opus 5.5 model documentationKimi K3.1 Pro
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 5.5
$0.2 per 1M cached input tokens
Claude Opus 5.5 model documentationKimi K3.1 Pro
$0.2 per 1M cached input tokens
Documented inputs
Claude Opus 5.5
Kimi K3.1 Pro
Not sourced
Documented outputs
Claude Opus 5.5
Kimi K3.1 Pro
Not sourced
Provider availability
Claude Opus 5.5
Generally Available · Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, Claude Platform on AWS
Anthropic Claude Opus 5.5 model documentationKimi K3.1 Pro
Not sourced
Reasoning profile
Claude Opus 5.5
Reasoning
Kimi K3.1 Pro
Reasoning
Weight access
Claude Opus 5.5
Proprietary
Kimi K3.1 Pro
Pending
License
Claude Opus 5.5
Proprietary
Kimi K3.1 Pro
Pending
Release date
Claude Opus 5.5
2026-09-22
Kimi K3.1 Pro
2026-10-23
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.26 vs $0.13. Cache-heavy agent loop: $0.32 vs $0.18.
- Context tradeoff
- Kimi K3.1 Pro has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Opus 5.5 or Kimi K3.1 Pro?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Claude Opus 5.5 or Kimi K3.1 Pro?
Kimi K3.1 Pro is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Claude Opus 5.5 or Kimi K3.1 Pro?
Kimi K3.1 Pro is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Claude Opus 5.5 or Kimi K3.1 Pro?
For the stated presets, chat costs $0.014 on Claude Opus 5.5 and $0.007 on Kimi K3.1 Pro; repository review costs $0.26 and $0.13; the cache-heavy agent loop costs $0.32 and $0.18. Costs use the listed standard API rates.
Which has the larger context window, Claude Opus 5.5 or Kimi K3.1 Pro?
Kimi K3.1 Pro has the larger documented context window: 1.05M, compared with 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence95 rows
Agentic
Terminal-Bench 4.0
Claude Opus 5.566.40%SourceKimi K3.1 Pro65.70%SourceClaude Opus 5.5 leads this result
Terminal-Bench-Science 0.1
Claude Opus 5.558.7%SourceKimi K3.1 Pro38.1%SourceClaude Opus 5.5 leads this result
AutomationBench
Claude Opus 5.540.0%SourceKimi K3.1 Pro—Not directly comparable
HLE w/ tools
Claude Opus 5.567.7%SourceKimi K3.1 Pro70.0%SourceKimi K3.1 Pro leads this result
OSWorld 2.0
Claude Opus 5.548.7%SourceKimi K3.1 Pro79.6%SourceKimi K3.1 Pro leads this result
LAB all-pass (Harvey held-out)
Claude Opus 5.58.3%SourceKimi K3.1 Pro—Not directly comparable
LAB criterion-pass (Harvey held-out)
Claude Opus 5.591.2%SourceKimi K3.1 Pro—Not directly comparable
Toolathlon-Verified
Claude Opus 5.577.8%SourceKimi K3.1 Pro—Not directly comparable
Toolathlon Verified Pass@3
Claude Opus 5.582.4%SourceKimi K3.1 Pro—Not directly comparable
Toolathlon Verified Pass³
Claude Opus 5.572.2%SourceKimi K3.1 Pro—Not directly comparable
Toolathlon Verified avg. turns
Claude Opus 5.526.9 turnsSourceKimi K3.1 Pro—Not directly comparable
CWE-bench v1
Claude Opus 5.567.0%SourceKimi K3.1 Pro—Not directly comparable
Hermes Index
Claude Opus 5.563.3%SourceKimi K3.1 Pro—Not directly comparable
Hermes Bench
Claude Opus 5.576.7%SourceKimi K3.1 Pro—Not directly comparable
MazeBench
Claude Opus 5.513%SourceKimi K3.1 Pro—Not directly comparable
BrowseComp
Claude Opus 5.5—Kimi K3.1 Pro92.1%SourceNot directly comparable
BioMysteryBench
Claude Opus 5.5—Kimi K3.1 Pro80.6%SourceNot directly comparable
Agents' Last Exam
Claude Opus 5.5—Kimi K3.1 Pro43.2%SourceNot directly comparable
Agents' Last Exam (CLI)
Claude Opus 5.5—Kimi K3.1 Pro44.2%SourceNot directly comparable
APEX-Agents
Claude Opus 5.5—Kimi K3.1 Pro67.2%SourceNot directly comparable
Frontier Finance
Claude Opus 5.5—Kimi K3.1 Pro58.6%SourceNot directly comparable
HANDBOOK.md
Claude Opus 5.5—Kimi K3.1 Pro44.6%SourceNot directly comparable
Business Operation
Claude Opus 5.5—Kimi K3.1 Pro37.8%SourceNot directly comparable
Financial Modeling
Claude Opus 5.5—Kimi K3.1 Pro57.6%SourceNot directly comparable
Financial Research
Claude Opus 5.5—Kimi K3.1 Pro70.4%SourceNot directly comparable
Kimi Hallu Bench Hard (tools)
Claude Opus 5.5—Kimi K3.1 Pro8.3%SourceNot directly comparable
ExploitGym
Claude Opus 5.5—Kimi K3.1 Pro20.7%SourceNot directly comparable
Coding
FrontierCode 1.1 Main
Claude Opus 5.554.4%SourceKimi K3.1 Pro—Not directly comparable
CursorBench 4.0
Claude Opus 5.557.8%SourceKimi K3.1 Pro—Not directly comparable
SWE-bench Pro
Claude Opus 5.589.9%SourceKimi K3.1 Pro—Not directly comparable
SWE Multilingual
Claude Opus 5.593.9%SourceKimi K3.1 Pro—Not directly comparable
SWE Multimodal
Claude Opus 5.561.4%SourceKimi K3.1 Pro—Not directly comparable
DeepSWE
Claude Opus 5.574.2%SourceKimi K3.1 Pro75.5%SourceKimi K3.1 Pro leads this result
FrontierCode 1.1 Extended
Claude Opus 5.563.6%SourceKimi K3.1 Pro—Not directly comparable
FrontierSWE v2
Claude Opus 5.562.3%SourceKimi K3.1 Pro—Not directly comparable
ProgramBench
Claude Opus 5.591.2%SourceKimi K3.1 Pro—Not directly comparable
PostTrainBench v1.1
Claude Opus 5.549.3%SourceKimi K3.1 Pro—Not directly comparable
Bug Hunt Bench
Claude Opus 5.541.7 fixesSourceKimi K3.1 Pro—Not directly comparable
ProgramBench (binary)
Claude Opus 5.5—Kimi K3.1 Pro31.5%SourceNot directly comparable
Kimi Code Bench v2
Claude Opus 5.5—Kimi K3.1 Pro77.1%SourceNot directly comparable
Kimi Code Bench 3.0 Long-horizon
Claude Opus 5.5—Kimi K3.1 Pro61.2%SourceNot directly comparable
sweMarathon
Claude Opus 5.5—Kimi K3.1 Pro51.7%SourceNot directly comparable
PostTrainBench v1.2
Claude Opus 5.5—Kimi K3.1 Pro36.5%SourceNot directly comparable
Reasoning
STEMBench
Claude Opus 5.52.62SourceKimi K3.1 Pro—Not directly comparable
ARC-AGI-1
Claude Opus 5.597.50%SourceKimi K3.1 Pro—Not directly comparable
ARC-AGI-2
Claude Opus 5.591.7%SourceKimi K3.1 Pro—Not directly comparable
GraphWalks BFS 256K–1M
Claude Opus 5.566.8%SourceKimi K3.1 Pro—Not directly comparable
Multimodal
ExtractBench Pro
Claude Opus 5.551%SourceKimi K3.1 Pro—Not directly comparable
ExtractBench (LlamaIndex)
Claude Opus 5.586.06%SourceKimi K3.1 Pro—Not directly comparable
ParseBench
Claude Opus 5.579.85%SourceKimi K3.1 Pro—Not directly comparable
Chartography (tools)
Claude Opus 5.589.0%SourceKimi K3.1 Pro90.2%SourceKimi K3.1 Pro leads this result
Chartography (no tools)
Claude Opus 5.564.4%SourceKimi K3.1 Pro—Not directly comparable
BenchCAD Vision2Code (no tools)
Claude Opus 5.50.730SourceKimi K3.1 Pro0.728SourceClaude Opus 5.5 leads this result
BenchCAD Vision2Code (tools)
Claude Opus 5.50.962SourceKimi K3.1 Pro0.927SourceClaude Opus 5.5 leads this result
Biomedical image analysis
Claude Opus 5.571.4%SourceKimi K3.1 Pro—Not directly comparable
OfficeQA
Claude Opus 5.578.9%SourceKimi K3.1 Pro—Not directly comparable
OfficeQA Pro
Claude Opus 5.567.7%SourceKimi K3.1 Pro67.4%SourceClaude Opus 5.5 leads this result
OfficeQA Pro v2
Claude Opus 5.5—Kimi K3.1 Pro64.4%SourceNot directly comparable
MMMU-Pro
Claude Opus 5.5—Kimi K3.1 Pro84.1%SourceNot directly comparable
MMMU-Pro w/ Python
Claude Opus 5.5—Kimi K3.1 Pro90%SourceNot directly comparable
CharXiv
Claude Opus 5.5—Kimi K3.1 Pro93.4%SourceNot directly comparable
CharXiv w/o tools
Claude Opus 5.5—Kimi K3.1 Pro89%SourceNot directly comparable
BabyVision w/ Python
Claude Opus 5.5—Kimi K3.1 Pro95.4%SourceNot directly comparable
ZeroBench
Claude Opus 5.5—Kimi K3.1 Pro39.0%SourceNot directly comparable
ZeroBench w/ Python
Claude Opus 5.5—Kimi K3.1 Pro63.0%SourceNot directly comparable
GDP.pdf (Gemini judge)
Claude Opus 5.5—Kimi K3.1 Pro34.6%SourceNot directly comparable
GDP.pdf w/ Python (Gemini judge)
Claude Opus 5.5—Kimi K3.1 Pro85.7%SourceNot directly comparable
PerceptionBench
Claude Opus 5.5—Kimi K3.1 Pro68.3%SourceNot directly comparable
Knowledge
HLE w/o tools
Claude Opus 5.564.4%SourceKimi K3.1 Pro51.1%SourceClaude Opus 5.5 leads this result
HealthBench (raw)
Claude Opus 5.568.1%SourceKimi K3.1 Pro—Not directly comparable
HealthBench (length-adjusted)
Claude Opus 5.560.6%SourceKimi K3.1 Pro—Not directly comparable
HealthBench Professional
Claude Opus 5.565.6%SourceKimi K3.1 Pro—Not directly comparable
HealthBench Professional (raw)
Claude Opus 5.577.1%SourceKimi K3.1 Pro—Not directly comparable
BioMysteryBench (human-solvable)
Claude Opus 5.589.3%SourceKimi K3.1 Pro—Not directly comparable
BioMysteryBench (human-difficult)
Claude Opus 5.550.0%SourceKimi K3.1 Pro—Not directly comparable
SpatialBench Verified
Claude Opus 5.572.0%SourceKimi K3.1 Pro—Not directly comparable
SingleCellBench
Claude Opus 5.561.2%SourceKimi K3.1 Pro—Not directly comparable
Morphology-to-molecule matching
Claude Opus 5.534.0%SourceKimi K3.1 Pro—Not directly comparable
Medicinal chemistry
Claude Opus 5.563.5%SourceKimi K3.1 Pro—Not directly comparable
Protein Design
Claude Opus 5.560.2%SourceKimi K3.1 Pro—Not directly comparable
Protein Design library ranking
Claude Opus 5.556.0%SourceKimi K3.1 Pro—Not directly comparable
De novo protein-binder design
Claude Opus 5.582.6%SourceKimi K3.1 Pro—Not directly comparable
Protocols (troubleshooting)
Claude Opus 5.573.7%SourceKimi K3.1 Pro—Not directly comparable
Protocols (understanding)
Claude Opus 5.569.0%SourceKimi K3.1 Pro—Not directly comparable
GPQA
Claude Opus 5.5—Kimi K3.1 Pro94.2%SourceNot directly comparable
GPQA-D
Claude Opus 5.5—Kimi K3.1 Pro94.2%SourceNot directly comparable
HLE
Claude Opus 5.5—Kimi K3.1 Pro70%SourceNot directly comparable
Kimi Hallu Bench
Claude Opus 5.5—Kimi K3.1 Pro5.4%SourceNot directly comparable
Multilingual
Instruction following
Gray Swan IPI (15 attempts)
Claude Opus 5.51.0%SourceKimi K3.1 Pro—Not directly comparable
Math
ArXivMath Aug. 2026 (no tools)
Claude Opus 5.591.2%SourceKimi K3.1 Pro—Not directly comparable
ArXivMath Aug. 2026 (tools)
Claude Opus 5.596.9%SourceKimi K3.1 Pro—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Opus 5.591.228%SourceKimi K3.1 Pro—Not directly comparable
FrontierMath v2 (Tier 4)
Claude Opus 5.595.000%SourceKimi K3.1 Pro—Not directly comparable
95 public results · 10 shared
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Last updated October 23, 2026