Long documents
Prompts that approach the documented context limit
Qwen3.5 Plus
Qwen3.5 Plus has the larger documented context window.
Confidence: documented
Kimi K2 vs Qwen3.5 Plus
Updated September 28, 2026. Rank says Qwen3.5 Plus is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
Qwen3.5 Plus has the higher public score, 48.92 versus 26.44, and the 90% score intervals do not overlap. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- Kimi K2 only
- 0
- Qwen3.5 Plus only
- 2
- Like-for-like categories
- 0 / 8
Estimated: Kimi K2 and Qwen3.5 PlusHow 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
Qwen3.5 Plus
Qwen3.5 Plus 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
Qwen3.5 Plus
Qwen3.5 Plus 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 and Qwen3.5 Plus 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
Kimi K2 and Qwen3.5 Plus are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedCache-heavy agent loop cost
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. Kimi K2 does not fit this workload in one request. Kimi K2 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 Plus has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
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—Qwen3.5 Plus
Not comparable · BenchAlign v5.7
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.
2 Shared
2 Qwen3.5 Plus 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.
FrontierMath v2 (Tier 4)Math
Normalized gap 2.1Kimi K2:0.000%Qwen3.5 Plus:2.083%FrontierMath v2 (Tiers 1-3)Math
Normalized gap 0.4Kimi K2:21.404%Qwen3.5 Plus:21.034%
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 v5.7 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.
Agentic
Not comparable- Kimi K2
- Not ranked
- Qwen3.5 Plus
- Not ranked
- Basis
- BenchAlign v5.7 lane · 0 vs 1 public rows
- Reading
- Not comparable
Coding
Not comparable- Kimi K2
- Not ranked
- Qwen3.5 Plus
- Not ranked
- Basis
- BenchAlign v5.7 lane · 0 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Kimi K2
- 58.7
- Unranked · 2 rankable rows
- Qwen3.5 Plus
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Kimi K2
- Not ranked
- Qwen3.5 Plus
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Kimi K2
- 37.4
- Estimated · #105/168
- Qwen3.5 Plus
- Not ranked
- Basis
- BenchAlign v5.7 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Kimi K2
- Not ranked
- Qwen3.5 Plus
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Kimi K2
- 47.0
- #85/124
- Qwen3.5 Plus
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Kimi K2
- 39.0
- Unranked · 2 rankable rows
- Qwen3.5 Plus
- 39.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
| Category | Kimi K2 | Qwen3.5 Plus | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.7 lane · 0 vs 1 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign v5.7 lane · 0 vs 1 public rows | Not comparable |
| Reasoning | 58.7Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 37.4Estimated · #105/168 | Not ranked | Not comparableBenchAlign v5.7 lane · 0 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 47.0#85/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 39.0Unranked · 2 rankable rows | 39.3Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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
- $0.00185
- Fits in one request
- Qwen3.5 Plus
- $0.0016
- Fits in one request
Qwen3.5 Plus has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Kimi K2
- $0.0375
- Fits in one request
- Qwen3.5 Plus
- $0.0272
- Fits in one request
Qwen3.5 Plus 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
- $0.157
- Does not fit in one request
- Cached input priced at the published list-input rate
- Qwen3.5 Plus
- $0.112
- Fits in one request
- Cached input priced at the published list-input rate
Kimi K2 does not fit this workload in one request. Kimi K2 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 Plus 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 K2Qwen3.5 Plus
Context window
Maximum documented context; output-token limits may be lower.
Kimi K2
128K
Qwen3.5 Plus
1M
API model ID
Kimi K2
Not sourced
Qwen3.5 Plus
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Kimi K2
Not published
Qwen3.5 Plus
Not published
Documented inputs
Kimi K2
Not sourced
Qwen3.5 Plus
Not sourced
Documented outputs
Kimi K2
Not sourced
Qwen3.5 Plus
Not sourced
Provider availability
Kimi K2
Not sourced
Qwen3.5 Plus
Not sourced
Reasoning profile
Kimi K2
Non-Reasoning
Qwen3.5 Plus
Reasoning
Weight access
Kimi K2
Proprietary
Qwen3.5 Plus
Proprietary
License
Kimi K2
Proprietary
Qwen3.5 Plus
Proprietary
Release date
Kimi K2
2025-07-01
Qwen3.5 Plus
2026-03-04
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
- Qwen3.5 Plus has the higher public score, 48.92 versus 26.44, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.0375 vs $0.0272. Cache-heavy agent loop: $0.157 vs $0.112.
- Context tradeoff
- Qwen3.5 Plus has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Kimi K2 or Qwen3.5 Plus?
Qwen3.5 Plus has the higher public score, 48.92 versus 26.44, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Kimi K2 or Qwen3.5 Plus?
Kimi K2 and Qwen3.5 Plus are not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Kimi K2 or Qwen3.5 Plus?
Kimi K2 and Qwen3.5 Plus are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Kimi K2 or Qwen3.5 Plus?
For the stated presets, chat costs $0.00185 on Kimi K2 and $0.0016 on Qwen3.5 Plus; repository review costs $0.0375 and $0.0272; the cache-heavy agent loop costs $0.157 and $0.112. Kimi K2 does not fit this workload in one request. Kimi K2 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.5 Plus has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Kimi K2 or Qwen3.5 Plus?
Qwen3.5 Plus has the larger documented context window: 1M, compared with 128K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence4 rows
Agentic
JobBench
Kimi K2—Qwen3.5 Plus18.5%SourceNot directly comparable
Coding
Vibe Code Bench
Kimi K2—Qwen3.5 Plus15.74%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceKimi K221.404%Qwen3.5 Plus21.034%Kimi K2 leads this result
FrontierMath v2 (Tier 4)
Shared sourceKimi K20.000%Qwen3.5 Plus2.083%Qwen3.5 Plus leads this result
4 public results · 2 shared
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Last updated September 28, 2026