Model Comparison

Kimi K2.5 vs Qwen3.6-27B

Kimi K2.5 shows notably better performance in the majority of benchmarks. Kimi K2.5 is 1.1x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

16 benchmarks

Kimi K2.5 outperforms in 11 benchmarks (HMMT 2025, Humanity's Last Exam, IMO-AnswerBench, LiveCodeBench v6, MathVista-Mini, MMLU-Pro, MMMU-Pro, OCRBench, SimpleVQA, SWE-bench Multilingual, VideoMMMU), while Qwen3.6-27B is better at 5 benchmarks (CharXiv-R, GPQA, SWE-Bench Pro, SWE-Bench Verified, Terminal-Bench 2.0).

Kimi K2.5 shows notably better performance in the majority of benchmarks.

Wed May 13 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Kimi K2.5 costs less

For input processing, Kimi K2.5 ($0.60/1M tokens) costs the same as Qwen3.6-27B ($0.60/1M tokens).

For output processing, Kimi K2.5 ($3.00/1M tokens) is 1.2x cheaper than Qwen3.6-27B ($3.60/1M tokens).

In conclusion, Qwen3.6-27B is more expensive than Kimi K2.5.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers

Wed May 13 2026 • llm-stats.com

Kimi K2.5

Input tokens$0.60

Output tokens$3.00

Best providerFireworks

Qwen3.6-27B

Input tokens$0.60

Output tokens$3.60

Best providerNovita

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Model Size

Parameter count comparison

972.2B diff

Kimi K2.5 has 972.2B more parameters than Qwen3.6-27B, making it 3499.5% larger.

Kimi K2.5

1.0Tparameters

Qwen3.6-27B

27.8Bparameters

1000.0B

Kimi K2.5

27.8B

Qwen3.6-27B

Context Window

Maximum input and output token capacity

Qwen3.6-27B accepts 262,144 input tokens compared to Kimi K2.5's 262,100 tokens. Kimi K2.5 can generate longer responses up to 262,100 tokens, while Qwen3.6-27B is limited to 65,536 tokens.

Kimi K2.5

Input262,100 tokens

Output262,100 tokens

Qwen3.6-27B

Input262,144 tokens

Output65,536 tokens

Wed May 13 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Kimi K2.5 and Qwen3.6-27B support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Kimi K2.5

Text

Images

Audio

Video

Qwen3.6-27B

Text

Images

Audio

Video

License

Usage and distribution terms

Kimi K2.5 is licensed under MIT, while Qwen3.6-27B uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

Kimi K2.5

MIT

Open weights

Qwen3.6-27B

Apache 2.0

Open weights

Release Timeline

When each model was launched

Kimi K2.5 was released on 2026-01-27, while Qwen3.6-27B was released on 2026-04-21.

Qwen3.6-27B is 3 months newer than Kimi K2.5.

Kimi K2.5

Jan 27, 2026

3 months ago

Qwen3.6-27B

Apr 21, 2026

3 weeks ago

2mo newer

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

Kimi K2.5 is available from Fireworks, Moonshot AI. Qwen3.6-27B is available from Novita.

Kimi K2.5

Fireworks

Input Price:Input: $0.60/1MOutput Price:Output: $3.00/1M

Unknown Organization

Input Price:Input: $0.60/1MOutput Price:Output: $3.00/1M

Qwen3.6-27B

Novita

Input Price:Input: $0.60/1MOutput Price:Output: $3.60/1M

* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Kimi K2.5

View details

Moonshot AI

Less expensive output tokens

Higher HMMT 2025 score (95.4% vs 93.8%)

Higher Humanity's Last Exam score (50.2% vs 24.0%)

Higher IMO-AnswerBench score (81.8% vs 80.8%)

Higher LiveCodeBench v6 score (85.0% vs 83.9%)

Higher MathVista-Mini score (90.1% vs 87.4%)

Higher MMLU-Pro score (87.1% vs 86.2%)

Higher MMMU-Pro score (78.5% vs 75.8%)

Higher OCRBench score (92.3% vs 89.4%)

Higher SimpleVQA score (71.2% vs 56.1%)

Higher SWE-bench Multilingual score (73.0% vs 71.3%)

Higher VideoMMMU score (86.6% vs 84.4%)

Qwen3.6-27B

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Higher CharXiv-R score (78.4% vs 77.5%)

Higher GPQA score (87.8% vs 87.6%)

Higher SWE-Bench Pro score (53.5% vs 50.7%)

Higher SWE-Bench Verified score (77.2% vs 76.8%)

Higher Terminal-Bench 2.0 score (59.3% vs 50.8%)

Detailed Comparison

AI Model Comparison Table
Feature	Kimi K2.5	Qwen3.6-27B

FAQ

Common questions about Kimi K2.5 vs Qwen3.6-27B.

Which is better, Kimi K2.5 or Qwen3.6-27B?

Kimi K2.5 shows notably better performance in the majority of benchmarks. Kimi K2.5 is made by Moonshot AI and Qwen3.6-27B is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Kimi K2.5 compare to Qwen3.6-27B in benchmarks?

Kimi K2.5 scores AIME 2025: 96.1%, HMMT 2025: 95.4%, InfoVQAtest: 92.6%, OCRBench: 92.3%, MathVista-Mini: 90.1%. Qwen3.6-27B scores CountBench: 97.8%, VLMsAreBlind: 97.0%, V*: 94.7%, AIME 2026: 94.1%, HMMT 2025: 93.8%.

Is Kimi K2.5 cheaper than Qwen3.6-27B?

Both models cost $0.60 per million input tokens.

What are the context window sizes for Kimi K2.5 and Qwen3.6-27B?

Kimi K2.5 supports 262K tokens and Qwen3.6-27B supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Kimi K2.5 and Qwen3.6-27B?

Key differences include context window (262K vs 262K), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Kimi K2.5 and Qwen3.6-27B?

Kimi K2.5 is developed by Moonshot AI and Qwen3.6-27B is developed by Alibaba Cloud / Qwen Team.