Math
Like-for-like- Kimi K2
- 16.1
- Qwen3.5 Plus
- 16.3
- Weighted basis
- 2 vs 2 rows
- Reading
- Qwen3.5 Plus leads
Kimi K2 vs Qwen3.5 Plus
Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Qwen3.5 Plus has the higher public score estimate, 46.73 versus 26.18, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
2 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
Qwen3.5 Plus
Qwen3.5 Plus has the larger documented context window.
Confidence: documented
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-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
Cache-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
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
- Shared results
- 2
- Kimi K2 only
- 0
- Qwen3.5 Plus only
- 2
- Like-for-like categories
- 1 / 8
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- Kimi K2
- Not measured
- Qwen3.5 Plus
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- Kimi K2
- Not measured
- Qwen3.5 Plus
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Kimi K2
- Not measured
- Qwen3.5 Plus
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Kimi K2
- Not measured
- Qwen3.5 Plus
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Kimi K2
- Not measured
- Qwen3.5 Plus
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Kimi K2
- Not measured
- Qwen3.5 Plus
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Kimi K2
- Not measured
- Qwen3.5 Plus
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Kimi K2 | Qwen3.5 Plus | Weighted basis | Reading |
|---|---|---|---|---|
| Math | 16.1 | 16.3 | Like-for-like2 vs 2 rows | Qwen3.5 Plus leads |
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 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
- 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.
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 estimate, 46.73 versus 26.18, but the 90% score intervals 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.
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
Frequently asked questions
Which is better, Kimi K2 or Qwen3.5 Plus?
Qwen3.5 Plus has the higher public score estimate, 46.73 versus 26.18, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Kimi K2 or Qwen3.5 Plus?
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, Kimi K2 or Qwen3.5 Plus?
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, 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.
Related comparisons
Last updated August 13, 2026
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