Kimi K2.6 vs Qwen3.8-27B
Kimi K2.6 and Qwen3.8-27B are closely matched at 43.6 and 44.8 on the LLM Stats Score. Qwen3.8-27B is 1.4x cheaper per token.
Moonshot AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Kimi K2.6 and Qwen3.8-27B are closely matched on the overall LLM Stats Score at 43.6 and 44.8.
In the 8 individual benchmarks reported for both models, Qwen3.8-27B wins 6; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3.8-27B is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Kimi K2.6
- you want predictable pricing at $0.75/M input and $3.50/M output
Choose Qwen3.8-27B
- you value its reported benchmark strengths — it wins 6 of 8 exact shared results
- cost matters — it's about 1.4x cheaper per token
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
28 reported for Kimi K2.6 · 26 for Qwen3.8-27B
Kimi K2.6 outperforms in 2 benchmarks (GPQA, Humanity's Last Exam), while Qwen3.8-27B is better at 6 benchmarks (BabyVision, CharXiv-R, LiveCodeBench v6, MathVision, OSWorld-Verified, SWE-Bench Pro).
Qwen3.8-27B shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Kimi K2.6 ($0.75/1M tokens) is 1.9x more expensive than Qwen3.8-27B ($0.40/1M tokens).
For output processing, Kimi K2.6 ($3.50/1M tokens) is 1.2x more expensive than Qwen3.8-27B ($3.00/1M tokens).
In conclusion, Kimi K2.6 is more expensive than Qwen3.8-27B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2.6 has 972.2B more parameters than Qwen3.8-27B, making it 3499.5% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 262,144 tokens. Qwen3.8-27B can generate longer responses up to 262,144 tokens, while Kimi K2.6 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both Kimi K2.6 and Qwen3.8-27B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Kimi K2.6
Qwen3.8-27B
License
Usage and distribution terms
Kimi K2.6 is licensed under Modified MIT License, while Qwen3.8-27B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Modified MIT License
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Kimi K2.6 was released on 2026-04-20, while Qwen3.8-27B was released on 2026-08-14.
Qwen3.8-27B is 4 months newer than Kimi K2.6.
Apr 20, 2026
5 months ago
Aug 14, 2026
1 months ago
3mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Kimi K2.6 is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together. Qwen3.8-27B is available from DeepInfra, FriendliAI.
Kimi K2.6
Qwen3.8-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2.6 and Qwen3.8-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2.6 vs Qwen3.8-27B.