Long documents
Prompts that approach the documented context limit
LongCat-2.5
LongCat-2.5 has the larger documented context window.
Confidence: documented
Kimi K2.5 (Reasoning) vs LongCat-2.5
Updated November 9, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
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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. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Model A
Moonshot AI
55.88/100
Estimated · Public rank #67
Conditional range 41.5–70.2
- Shared results
- 2
- Kimi K2.5 (Reasoning) only
- 13
- LongCat-2.5 only
- 20
- Like-for-like categories
- 0 / 8
Estimated: Kimi K2.5 (Reasoning). Conditional ranges do not establish rank confidence.How 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
LongCat-2.5
LongCat-2.5 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
LongCat-2.5
LongCat-2.5 has the lower estimated token cost for this stated workload. Kimi K2.5 (Reasoning) 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
LongCat-2.5
LongCat-2.5 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 K2.5 (Reasoning) and LongCat-2.5 are 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
LongCat-2.5 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.
—Kimi K2.5 (Reasoning)—LongCat-2.5
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.
13 Kimi K2.5 (Reasoning) only
2 Shared
20 LongCat-2.5 only
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.
BrowseCompAgentic
Normalized gap 31.3Kimi K2.5 (Reasoning):60.6%LongCat-2.5:91.9%CharXivMultimodal
Normalized gap 12.7Kimi K2.5 (Reasoning):77.5%LongCat-2.5:90.2%
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, reasoning, multimodal, 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.
Agentic
Not comparable- Kimi K2.5 (Reasoning)
- 40.5
- Estimated · #71/132
- LongCat-2.5
- Not ranked
- Basis
- BenchAlign v5.8 lane · 4 vs 11 public rows
- Reading
- Not comparable
Coding
Not comparable- Kimi K2.5 (Reasoning)
- Not ranked
- LongCat-2.5
- Not ranked
- Basis
- BenchAlign v5.8 lane · 4 vs 6 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Kimi K2.5 (Reasoning)
- Not ranked
- LongCat-2.5
- Not ranked
- Basis
- BenchAlign v5.8 lane · 1 vs 0 public rows
- Reading
- Not comparable
Multimodal
Not comparable- Kimi K2.5 (Reasoning)
- 51.8
- Estimated · #50/67
- LongCat-2.5
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 4 public rows
- Reading
- Not comparable
Knowledge
Not comparable- Kimi K2.5 (Reasoning)
- 48.5
- Estimated · #86/179
- LongCat-2.5
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Kimi K2.5 (Reasoning)
- Not ranked
- LongCat-2.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Kimi K2.5 (Reasoning)
- Not ranked
- LongCat-2.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Kimi K2.5 (Reasoning)
- Not ranked
- LongCat-2.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Kimi K2.5 (Reasoning) | LongCat-2.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 40.5Estimated · #71/132 | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 11 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 6 public rows | Not comparable |
| Reasoning | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 1 vs 0 public rows | Not comparable |
| Multimodal | 51.8Estimated · #50/67 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 4 public rows | Not comparable |
| Knowledge | 48.5Estimated · #86/179 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 1 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 | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, 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.
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
- Kimi K2.5 (Reasoning)
- $0.0021
- Fits in one request
- LongCat-2.5
- $0.0009
- Fits in one request
LongCat-2.5 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Kimi K2.5 (Reasoning)
- $0.039
- Fits in one request
- LongCat-2.5
- $0.0186
- Fits in one request
LongCat-2.5 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Kimi K2.5 (Reasoning)
- $0.162
- Fits in one request
- Cached input priced at the published list-input rate
- LongCat-2.5
- $0.0192
- Fits in one request
LongCat-2.5 has the lower modeled cost
Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate.
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.
SpecificationKimi K2.5 (Reasoning)LongCat-2.5
Context window
Maximum documented context; output-token limits may be lower.
Kimi K2.5 (Reasoning)
256K
LongCat-2.5
API model ID
Kimi K2.5 (Reasoning)
Not sourced
LongCat-2.5
LongCat-2.5
LongCat API Platform pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Kimi K2.5 (Reasoning)
Not published
LongCat-2.5
$0.006 per 1M cached input tokens
LongCat API Platform pricingDocumented inputs
Kimi K2.5 (Reasoning)
Not sourced
LongCat-2.5
Not sourced
Documented outputs
Kimi K2.5 (Reasoning)
Not sourced
LongCat-2.5
Not sourced
Provider availability
Kimi K2.5 (Reasoning)
Not sourced
LongCat-2.5
Not sourced
Reasoning profile
Kimi K2.5 (Reasoning)
Reasoning
LongCat-2.5
Hybrid
Weight access
Kimi K2.5 (Reasoning)
Proprietary
LongCat-2.5
Open Weight
License
Kimi K2.5 (Reasoning)
Proprietary
LongCat-2.5
Open Weight
Release date
Kimi K2.5 (Reasoning)
2026-01-27
LongCat-2.5
2026-11-02
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: $0.039 vs $0.0186. Cache-heavy agent loop: $0.162 vs $0.0192.
- Context tradeoff
- LongCat-2.5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Kimi K2.5 (Reasoning) or LongCat-2.5?
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, Kimi K2.5 (Reasoning) or LongCat-2.5?
Kimi K2.5 (Reasoning) and LongCat-2.5 are not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Kimi K2.5 (Reasoning) or LongCat-2.5?
LongCat-2.5 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Kimi K2.5 (Reasoning) or LongCat-2.5?
For the stated presets, chat costs $0.0021 on Kimi K2.5 (Reasoning) and $0.0009 on LongCat-2.5; repository review costs $0.039 and $0.0186; the cache-heavy agent loop costs $0.162 and $0.0192. Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Kimi K2.5 (Reasoning) or LongCat-2.5?
LongCat-2.5 has the larger documented context window: 1M, compared with 256K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Kimi K2.5 (Reasoning)
API / mo$2,700
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
LongCat-2.5
API / mo$1,125
Self-host / mo$57,606
Break-even3.4B/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 evidence35 rows
Agentic
Terminal-Bench 2.0
Kimi K2.5 (Reasoning)50.8%SourceLongCat-2.5—Not directly comparable
BrowseComp
Kimi K2.5 (Reasoning)60.6%SourceLongCat-2.591.9%SourceLongCat-2.5 leads this result
OSWorld-Verified
Kimi K2.5 (Reasoning)63.3%SourceLongCat-2.5—Not directly comparable
Gert Labs
Kimi K2.5 (Reasoning)32.58%SourceLongCat-2.5—Not directly comparable
Terminal-Bench 2.1
Kimi K2.5 (Reasoning)—LongCat-2.588.4%SourceNot directly comparable
Terminal-Bench 4.0
Kimi K2.5 (Reasoning)—LongCat-2.538.50%SourceNot directly comparable
AutomationBench
Kimi K2.5 (Reasoning)—LongCat-2.551.8%SourceNot directly comparable
HLE w/ tools
Kimi K2.5 (Reasoning)—LongCat-2.563.1%SourceNot directly comparable
Agents' Last Exam
Kimi K2.5 (Reasoning)—LongCat-2.538.5%SourceNot directly comparable
MCP Atlas
Kimi K2.5 (Reasoning)—LongCat-2.583.1%SourceNot directly comparable
JobBench
Kimi K2.5 (Reasoning)—LongCat-2.565.4%SourceNot directly comparable
skillsBench
Kimi K2.5 (Reasoning)—LongCat-2.563.3%SourceNot directly comparable
SpreadsheetBench 2
Kimi K2.5 (Reasoning)—LongCat-2.534.9%SourceNot directly comparable
Claw-Eval
Kimi K2.5 (Reasoning)—LongCat-2.578.3%SourceNot directly comparable
Coding
SWE-bench Verified
Kimi K2.5 (Reasoning)76.8%SourceLongCat-2.5—Not directly comparable
LiveCodeBench v6
Kimi K2.5 (Reasoning)85.0%SourceLongCat-2.5—Not directly comparable
Terminal-Bench 2.0
Kimi K2.5 (Reasoning)50.8%SourceLongCat-2.5—Not directly comparable
Vibe Code Bench
Kimi K2.5 (Reasoning)17.54%SourceLongCat-2.5—Not directly comparable
Terminal-Bench 2.1
Kimi K2.5 (Reasoning)—LongCat-2.588.4%SourceNot directly comparable
ProgramBench
Kimi K2.5 (Reasoning)—LongCat-2.525.5%SourceNot directly comparable
DeepSWE
Kimi K2.5 (Reasoning)—LongCat-2.564.6%SourceNot directly comparable
PaperBench
Kimi K2.5 (Reasoning)—LongCat-2.582.1%SourceNot directly comparable
SWE-Atlas Codebase QnA
Kimi K2.5 (Reasoning)—LongCat-2.558.3%SourceNot directly comparable
NL2Repo
Kimi K2.5 (Reasoning)—LongCat-2.560.5%SourceNot directly comparable
Reasoning
LongBench v2
Kimi K2.5 (Reasoning)61%SourceLongCat-2.5—Not directly comparable
Multimodal
MMMU-Pro
Kimi K2.5 (Reasoning)78.5%SourceLongCat-2.5—Not directly comparable
CharXiv
Kimi K2.5 (Reasoning)77.5%SourceLongCat-2.590.2%SourceLongCat-2.5 leads this result
Vision2Web
Kimi K2.5 (Reasoning)—LongCat-2.578.6%SourceNot directly comparable
MathVision w/ Python
Kimi K2.5 (Reasoning)—LongCat-2.596.5%SourceNot directly comparable
Video-MME
Kimi K2.5 (Reasoning)—LongCat-2.585.2%SourceNot directly comparable
Knowledge
Math
35 public results · 2 shared
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Last updated November 9, 2026