Kimi K2.7 Code vs Qwen3.8-27B
Kimi K2.7 Code and Qwen3.8-27B are closely matched at 39.4 and 45.1 on the LLM Stats Score. Qwen3.8-27B is 1.3x cheaper per token.
Moonshot AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Kimi K2.7 Code and Qwen3.8-27B are closely matched on the overall LLM Stats Score at 39.4 and 45.1.
In the 1 individual benchmarks reported for both models, Qwen3.8-27B wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, Qwen3.8-27B is roughly 1.3x 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 K2.7 Code
- you want predictable pricing at $0.68/M input and $3.40/M output
Choose Qwen3.8-27B
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 1.3x 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
9 reported for Kimi K2.7 Code · 26 for Qwen3.8-27B
Kimi K2.7 Code outperforms in 0 benchmarks, while Qwen3.8-27B is better at 1 benchmark (DeepSWE 1.1).
Qwen3.8-27B significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Kimi K2.7 Code ($0.68/1M tokens) is 1.7x more expensive than Qwen3.8-27B ($0.40/1M tokens).
For output processing, Kimi K2.7 Code ($3.40/1M tokens) is 1.1x more expensive than Qwen3.8-27B ($3.00/1M tokens).
In conclusion, Kimi K2.7 Code is more expensive than Qwen3.8-27B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2.7 Code 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. Both models can generate responses up to 262,144 tokens.
Input capabilities
Documented input modalities across available providers
Both Kimi K2.7 Code and Qwen3.8-27B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Kimi K2.7 Code
Qwen3.8-27B
License
Usage and distribution terms
Kimi K2.7 Code 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 K2.7 Code was released on 2026-06-12, while Qwen3.8-27B was released on 2026-08-14.
Qwen3.8-27B is 2 months newer than Kimi K2.7 Code.
Jun 12, 2026
3 months ago
Aug 14, 2026
1 months ago
2mo 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 K2.7 Code is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together. Qwen3.8-27B is available from DeepInfra, FriendliAI.
Kimi K2.7 Code
Qwen3.8-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2.7 Code and Qwen3.8-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2.7 Code vs Qwen3.8-27B.