Long documents
Prompts that approach the documented context limit
Kimi K2.5 (Reasoning)
Kimi K2.5 (Reasoning) has the larger documented context window.
Updated October 17, 2026. Rank says Kimi K2.5 (Reasoning) is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Kimi K2.5 (Reasoning) has the higher public point estimate, 56.09 versus 55.34. Their conditional score ranges overlap. These ranges do not establish rank confidence. 13 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Moonshot AI
56.09/100
Estimated · Public rank #63
Conditional range 41.7–70.4
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.
Prompts that approach the documented context limit
Kimi K2.5 (Reasoning)
Kimi K2.5 (Reasoning) has the larger documented context window.
1K fresh input + 500 output tokens
Kimi K2.5 (Reasoning)
Kimi K2.5 (Reasoning) has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Kimi K2.5 (Reasoning)
Kimi K2.5 (Reasoning) 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
Kimi K2.5 (Reasoning) and Qwen3.5 397B are not ranked on the public lane for coding, so no winner is named for coding.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Kimi K2.5 (Reasoning) and Qwen3.5 397B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Qwen3.5 397B does not fit this workload in one request. Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate.
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.
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.
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.
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 8.4CharXivMultimodal
Normalized gap 3.3LongBench v2Reasoning
Normalized gap 2.2Terminal-Bench 2.0Agentic
Normalized gap 1.7OSWorld-VerifiedAgentic
Normalized gap 1.1Each 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 | Kimi K2.5 (Reasoning) | Qwen3.5 397B | Basis | Reading |
|---|---|---|---|---|
| Multimodal | 69.0#33/54 | 73.6#29/54 | Like-for-likeProvisional lane · 2 vs 2 weighted rows | Qwen3.5 397B leads |
| Agentic | 38.3Estimated · #71/128 | 43.8Estimated · #61/128 | Directional onlyBenchAlign v5.8 lane · 4 vs 14 public rows | Directional only |
| Knowledge | 48.8Estimated · #85/184 | 53.1Estimated · #68/184 | Directional onlyBenchAlign v5.8 lane · 2 vs 6 public rows | Directional only |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 6 public rows | Not comparable |
| Reasoning | 67.1Unranked · 1 rankable row | 74.9Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | 93.8#5/17 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Instruction following | Not ranked | 79.9#59/148 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | 81.7Unranked · 5 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 K2.5 (Reasoning) has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Kimi K2.5 (Reasoning) has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Qwen3.5 397B does not fit this workload in one request. Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 397B 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.
Kimi K2.5 (Reasoning)
256K
Qwen3.5 397B
128K
Kimi K2.5 (Reasoning)
Not sourced
Qwen3.5 397B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Kimi K2.5 (Reasoning)
Not published
Qwen3.5 397B
Not published
Kimi K2.5 (Reasoning)
Not sourced
Qwen3.5 397B
Not sourced
Kimi K2.5 (Reasoning)
Not sourced
Qwen3.5 397B
Not sourced
Kimi K2.5 (Reasoning)
Not sourced
Qwen3.5 397B
Not sourced
Kimi K2.5 (Reasoning)
Reasoning
Qwen3.5 397B
Non-Reasoning
Kimi K2.5 (Reasoning)
Proprietary
Qwen3.5 397B
Open Weight
Kimi K2.5 (Reasoning)
Proprietary
Qwen3.5 397B
Open Weight
Kimi K2.5 (Reasoning)
2026-01-27
Qwen3.5 397B
2026-02-16
Kimi K2.5 (Reasoning) has the higher public point estimate, 56.09 versus 55.34. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Kimi K2.5 (Reasoning) and Qwen3.5 397B are not ranked on the public lane for coding, so no winner is named for coding.
Qwen3.5 397B scores higher for agentic tasks on the public lane, 43.8 to 38.3. Kimi K2.5 (Reasoning) and Qwen3.5 397B are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
For the stated presets, chat costs $0.0021 on Kimi K2.5 (Reasoning) and $0.0024 on Qwen3.5 397B; repository review costs $0.039 and $0.0408; the cache-heavy agent loop costs $0.162 and $0.168. Qwen3.5 397B does not fit this workload in one request. Kimi K2.5 (Reasoning) has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 397B has no published cached-input rate, so cached tokens use its listed input rate.
Kimi K2.5 (Reasoning) has the larger documented context window: 256K, compared with 128K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Qwen3.5 397B leads this result
BrowseComp
Qwen3.5 397B leads this result
OSWorld-Verified
Kimi K2.5 (Reasoning) leads this result
Gert Labs
Shared sourceQwen3.5 397B leads this result
Claw-Eval
Not directly comparable
QwenClawBench
Not directly comparable
τ³-bench results
Not directly comparable
VITA-Bench
Not directly comparable
DeepPlanning
Not directly comparable
Toolathlon
Not directly comparable
MCP Atlas
Not directly comparable
MCP-Tasks
Not directly comparable
WideResearch
Not directly comparable
ResearchClawBench
Not directly comparable
SWE-bench Verified
Kimi K2.5 (Reasoning) leads this result
LiveCodeBench v6
Kimi K2.5 (Reasoning) leads this result
Terminal-Bench 2.0
Qwen3.5 397B leads this result
Vibe Code Bench
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
NL2Repo
Not directly comparable
MMMU-Pro
Qwen3.5 397B leads this result
CharXiv
Qwen3.5 397B leads this result
MMMU
Not directly comparable
RealWorldQA
Not directly comparable
OmniDocBench 1.5
Not directly comparable
Video-MME (with subtitle)
Not directly comparable
Video-MME (w/o subtitle)
Not directly comparable
MathVision
Not directly comparable
We-Math
Not directly comparable
DynaMath
Not directly comparable
SimpleVQA
Not directly comparable
MMLongBench-Doc
Not directly comparable
CC-OCR
Not directly comparable
AI2D_TEST
Not directly comparable
CountBench
Not directly comparable
RefCOCO (avg)
Not directly comparable
ODINW13
Not directly comparable
ERQA
Not directly comparable
VideoMMMU
Not directly comparable
MLVU (M-Avg)
Not directly comparable
ScreenSpot Pro
Not directly comparable
V*
Not directly comparable
GPQA
Qwen3.5 397B leads this result
MMLU-Pro
Qwen3.5 397B leads this result
SuperGPQA
Not directly comparable
MMLU-Redux
Not directly comparable
C-Eval
Not directly comparable
HLE
Not directly comparable
MMLU-ProX
Not directly comparable
NOVA-63
Not directly comparable
INCLUDE
Not directly comparable
PolyMath
Not directly comparable
MAXIFE
Not directly comparable
AIME 2025
Not directly comparable
HMMT Feb 2025
Kimi K2.5 (Reasoning) leads this result
AIME26
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
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Last updated October 17, 2026