Model A

Moonshot AI

65.49/100

Estimated · Public rank #36

90% interval 49.0–71.2

Kimi K2.7 Code vs Qwen3.6-35B-A3B

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Qwen3.6-35B-A3B

Alibaba

43.82/100

Estimated · Public rank #162

90% interval 37.6–50.0

Decision reading

Kimi K2.7 Code has the higher public score estimate, 65.49 versus 43.82, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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
Qwen3.6-35B-A3B
Qwen3.6-35B-A3B has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Qwen3.6-35B-A3B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Kimi K2.7 Code and Qwen3.6-35B-A3B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

7 Kimi K2.7 Code only

1 Shared

40 Qwen3.6-35B-A3B only

Shared results: 1
Kimi K2.7 Code only: 7
Qwen3.6-35B-A3B only: 40
Like-for-like categories: 0 / 8

4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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

Directional only

Kimi K2.7 Code: 46.8; Estimated · #72/152
Qwen3.6-35B-A3B: 40.1; Estimated · #115/152
Basis: BenchAlign lane · 3 vs 11 public rows
Reading: Directional only

Coding

Directional only

Kimi K2.7 Code: 50.9; Supported · #51/151
Qwen3.6-35B-A3B: 44.9; Estimated · #90/151
Basis: BenchAlign lane · 5 vs 6 public rows
Reading: Directional only

Knowledge

Directional only

Kimi K2.7 Code: 61.6; Estimated · #31/183
Qwen3.6-35B-A3B: 44.2; Estimated · #116/183
Basis: BenchAlign lane · 0 vs 5 public rows
Reading: Directional only

Instruction following

Directional only

Kimi K2.7 Code: 76.6; #58/123
Qwen3.6-35B-A3B: 78.3; #57/123
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

Kimi K2.7 Code: 75.5; Unranked · 2 rankable rows
Qwen3.6-35B-A3B: 70.3; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Kimi K2.7 Code: Not ranked
Qwen3.6-35B-A3B: 71.5; Unranked · 5 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Kimi K2.7 Code: Not ranked
Qwen3.6-35B-A3B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Kimi K2.7 Code: Not ranked
Qwen3.6-35B-A3B: 50.1; #37/48
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Kimi K2.7 Code and Qwen3.6-35B-A3B
Category	Kimi K2.7 Code	Qwen3.6-35B-A3B	Basis	Reading
Agentic	46.8Estimated · #72/152	40.1Estimated · #115/152	Directional onlyBenchAlign lane · 3 vs 11 public rows	Directional only
Coding	50.9Supported · #51/151	44.9Estimated · #90/151	Directional onlyBenchAlign lane · 5 vs 6 public rows	Directional only
Knowledge	61.6Estimated · #31/183	44.2Estimated · #116/183	Directional onlyBenchAlign lane · 0 vs 5 public rows	Directional only
Instruction following	76.6#58/123	78.3#57/123	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	75.5Unranked · 2 rankable rows	70.3Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	71.5Unranked · 5 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	50.1#37/48	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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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 K2.7 Code: $0.00295; Fits in one request
Qwen3.6-35B-A3B: API rate not published; Fits in one request

Qwen3.6-35B-A3B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Kimi K2.7 Code: $0.0595; Fits in one request
Qwen3.6-35B-A3B: API rate not published; Fits in one request

Qwen3.6-35B-A3B has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Kimi K2.7 Code: $0.249; Fits in one request; Cached input priced at the published list-input rate
Qwen3.6-35B-A3B: API rate not published; Fits in one request; Cached-input rate unavailable

Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.6-35B-A3B has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationKimi K2.7 CodeQwen3.6-35B-A3B

Context window

Maximum documented context; output-token limits may be lower.

Kimi K2.7 Code

256K

Qwen3.6-35B-A3B

262K

API model ID

Kimi K2.7 Code

Not sourced

Qwen3.6-35B-A3B

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Kimi K2.7 Code

Not published

Qwen3.6-35B-A3B

No comparable hosted API rate

Documented inputs

Kimi K2.7 Code

Not sourced

Qwen3.6-35B-A3B

Not sourced

Documented outputs

Kimi K2.7 Code

Not sourced

Qwen3.6-35B-A3B

Not sourced

Provider availability

Kimi K2.7 Code

Not sourced

Qwen3.6-35B-A3B

Not sourced

Reasoning profile

Kimi K2.7 Code

Reasoning

Qwen3.6-35B-A3B

Reasoning

Weight access

Kimi K2.7 Code

Open Weight

Qwen3.6-35B-A3B

Open Weight

License

Kimi K2.7 Code

Open Weight

Qwen3.6-35B-A3B

Open Weight

Release date

Kimi K2.7 Code

2026-06-12

Qwen3.6-35B-A3B

2026-04-15

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: Kimi K2.7 Code has the higher public score estimate, 65.49 versus 43.82, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.6-35B-A3B has the larger documented window (262K).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Kimi K2.7 Code

API / mo$3,713

Self-host / mo$18,221

Break-even326M/day

Qwen3.6-35B-A3B

API / mo$0

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence48 rows

Agentic

Kimi Claw 24/7
Kimi K2.7 Code46.9%
Source
Qwen3.6-35B-A3B—
Not directly comparable
MCP Atlas
Kimi K2.7 Code76%
Source
Qwen3.6-35B-A3B62.8%
Source
Kimi K2.7 Code leads this result
MCP Mark Verified
Kimi K2.7 Code81.1%
Source
Qwen3.6-35B-A3B—
Not directly comparable
Terminal-Bench 2.0
Kimi K2.7 Code—
Qwen3.6-35B-A3B51.5%
Source
Not directly comparable
Claw-Eval
Kimi K2.7 Code—
Qwen3.6-35B-A3B68.7%
Source
Not directly comparable
QwenClawBench
Kimi K2.7 Code—
Qwen3.6-35B-A3B52.6%
Source
Not directly comparable
QwenWebBench
Kimi K2.7 Code—
Qwen3.6-35B-A3B1397
Source
Not directly comparable
τ³-bench results
Kimi K2.7 Code—
Qwen3.6-35B-A3B67.2%
Source
Not directly comparable
VITA-Bench
Kimi K2.7 Code—
Qwen3.6-35B-A3B35.6%
Source
Not directly comparable
DeepPlanning
Kimi K2.7 Code—
Qwen3.6-35B-A3B25.9%
Source
Not directly comparable
Toolathlon
Kimi K2.7 Code—
Qwen3.6-35B-A3B26.9%
Source
Not directly comparable
WideResearch
Kimi K2.7 Code—
Qwen3.6-35B-A3B60.1%
Source
Not directly comparable
Gert Labs
Kimi K2.7 Code—
Qwen3.6-35B-A3B42.65%
Source
Not directly comparable

Coding

Kimi Code Bench v2
Kimi K2.7 Code62.0%
Source
Qwen3.6-35B-A3B—
Not directly comparable
ProgramBench
Kimi K2.7 Code53.6%
Source
Qwen3.6-35B-A3B—
Not directly comparable
MLS-Bench Lite
Kimi K2.7 Code35.1%
Source
Qwen3.6-35B-A3B—
Not directly comparable
cursorBench32
Kimi K2.7 Code49.7%
Source
Qwen3.6-35B-A3B—
Not directly comparable
OpenHarmony Bench
Kimi K2.7 Code52.1%
Source
Qwen3.6-35B-A3B—
Not directly comparable
SWE-bench Verified
Kimi K2.7 Code—
Qwen3.6-35B-A3B73.4%
Source
Not directly comparable
SWE Multilingual
Kimi K2.7 Code—
Qwen3.6-35B-A3B67.2%
Source
Not directly comparable
SWE-bench Pro
Kimi K2.7 Code—
Qwen3.6-35B-A3B49.5%
Source
Not directly comparable
Terminal-Bench 2.0
Kimi K2.7 Code—
Qwen3.6-35B-A3B51.5%
Source
Not directly comparable
LiveCodeBench
Kimi K2.7 Code—
Qwen3.6-35B-A3B80.4%
Source
Not directly comparable
NL2Repo
Kimi K2.7 Code—
Qwen3.6-35B-A3B29.4%
Source
Not directly comparable

Knowledge

MMLU-Pro
Kimi K2.7 Code—
Qwen3.6-35B-A3B85.2%
Source
Not directly comparable
SuperGPQA
Kimi K2.7 Code—
Qwen3.6-35B-A3B64.7%
Source
Not directly comparable
C-Eval
Kimi K2.7 Code—
Qwen3.6-35B-A3B90%
Source
Not directly comparable
GPQA
Kimi K2.7 Code—
Qwen3.6-35B-A3B86%
Source
Not directly comparable
HLE
Kimi K2.7 Code—
Qwen3.6-35B-A3B21.4%
Source
Not directly comparable

Math

HMMT Feb 2025
Kimi K2.7 Code—
Qwen3.6-35B-A3B90.7%
Source
Not directly comparable
HMMT Nov 2025
Kimi K2.7 Code—
Qwen3.6-35B-A3B89.1%
Source
Not directly comparable
HMMT Feb 2026
Kimi K2.7 Code—
Qwen3.6-35B-A3B83.6%
Source
Not directly comparable
MMAnswerBench
Kimi K2.7 Code—
Qwen3.6-35B-A3B78.9%
Source
Not directly comparable
AIME26
Kimi K2.7 Code—
Qwen3.6-35B-A3B92.7%
Source
Not directly comparable

Multimodal

MMMU
Kimi K2.7 Code—
Qwen3.6-35B-A3B81.7%
Source
Not directly comparable
MMMU-Pro
Kimi K2.7 Code—
Qwen3.6-35B-A3B75.3%
Source
Not directly comparable
RealWorldQA
Kimi K2.7 Code—
Qwen3.6-35B-A3B85.3%
Source
Not directly comparable
OmniDocBench 1.5
Kimi K2.7 Code—
Qwen3.6-35B-A3B89.9%
Source
Not directly comparable
CharXiv
Kimi K2.7 Code—
Qwen3.6-35B-A3B78%
Source
Not directly comparable
SimpleVQA
Kimi K2.7 Code—
Qwen3.6-35B-A3B58.9%
Source
Not directly comparable
CC-OCR
Kimi K2.7 Code—
Qwen3.6-35B-A3B81.9%
Source
Not directly comparable
AI2D_TEST
Kimi K2.7 Code—
Qwen3.6-35B-A3B92.7%
Source
Not directly comparable
RefCOCO (avg)
Kimi K2.7 Code—
Qwen3.6-35B-A3B92.0%
Source
Not directly comparable
ODINW13
Kimi K2.7 Code—
Qwen3.6-35B-A3B50.8%
Source
Not directly comparable
Video-MME (with subtitle)
Kimi K2.7 Code—
Qwen3.6-35B-A3B86.6%
Source
Not directly comparable
Video-MME (w/o subtitle)
Kimi K2.7 Code—
Qwen3.6-35B-A3B82.5%
Source
Not directly comparable
VideoMMMU
Kimi K2.7 Code—
Qwen3.6-35B-A3B83.7%
Source
Not directly comparable
MLVU (M-Avg)
Kimi K2.7 Code—
Qwen3.6-35B-A3B86.2%
Source
Not directly comparable

Frequently asked questions

Which is better, Kimi K2.7 Code or Qwen3.6-35B-A3B?

Kimi K2.7 Code has the higher public score estimate, 65.49 versus 43.82, 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 K2.7 Code or Qwen3.6-35B-A3B?

Kimi K2.7 Code scores higher for coding on the public lane, 50.9 to 44.9. Qwen3.6-35B-A3B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Kimi K2.7 Code or Qwen3.6-35B-A3B?

Kimi K2.7 Code scores higher for agentic tasks on the public lane, 46.8 to 40.1. Kimi K2.7 Code and Qwen3.6-35B-A3B 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.

Which costs less, Kimi K2.7 Code or Qwen3.6-35B-A3B?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Kimi K2.7 Code or Qwen3.6-35B-A3B?

Qwen3.6-35B-A3B has the larger documented context window: 262K, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 10, 2026

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Kimi K2.7 Code vs Qwen3.6-35B-A3B

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Instruction following

Reasoning

Math

Multilingual

Multimodal

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, Kimi K2.7 Code or Qwen3.6-35B-A3B?

Which is better for coding, Kimi K2.7 Code or Qwen3.6-35B-A3B?

Which is better for agentic tasks, Kimi K2.7 Code or Qwen3.6-35B-A3B?

Which costs less, Kimi K2.7 Code or Qwen3.6-35B-A3B?

Which has the larger context window, Kimi K2.7 Code or Qwen3.6-35B-A3B?

Related comparisons

Watch Kimi K2.7 Code vs Qwen3.6-35B-A3B