Qwen · Open weights
Qwen 3.8 27B Kimiko
Qwen 3.8 27B Kimiko is a creative finetune served by Aoru in NVFP4 with a 262,144-token context window. It supports image understanding, tool calling, and optional reasoning.
- Reasoning
- Tools
- Vision
- Input
- Text, Images
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.10
- Output
- $0.60
- Cache read
- $0.05
Reference prices as published by the model provider. API example
Specifications
- Context window
- 262.1K
- Max output
- 32.8K
- Average output (7 days)
- 210 tokens
- Longer than 22% of 465 models
Side by side
Qwen 3.8 27B Kimiko compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Qwen 3.8 27B KimikoQwenThis model | LFM2.5 2.6BliquidRemove | |
|---|---|---|
| Provider | Qwen | liquid |
| Discovery tier | Open weights | Open weights |
| Added | Oct 5, 2026 | Aug 12, 2026 |
| Limits | ||
| Context window | 262.14KBest | 128K |
| Max output | 32.77K | 32.77K |
| Average output | 210 tokens | 459 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.10 | $0.10 |
| Output | $0.60 | $0.20Best |
| Cache read | $0.05 | — |
| Performance | ||
| Request success (24h) | 100.0% | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | — | 8.4 |
| Coding Index | — | 7.7 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | Yes |
| Vision | Yes | No |
| Structured output | No | Yes |
| Reasoning effort | — | minimal, low, medium, high, xhigh |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Qwen 3.8 27B Kimiko
- LFM2.5 2.6B
Intelligence Index
- Qwen 3.8 27B Kimiko—
- LFM2.5 2.6B8.4
Indexes
Coding Index
Composite of the coding evaluations
- Qwen 3.8 27B Kimiko: —
- LFM2.5 2.6B: 7.7
Document reasoning
AA-LCRv1.1
Reasoning across long documents
- Qwen 3.8 27B Kimiko: —
- LFM2.5 2.6B: 5.7%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Qwen 3.8 27B Kimiko: —
- LFM2.5 2.6B: 6.2%
GPQA Diamond
Graduate-level science questions
- Qwen 3.8 27B Kimiko: —
- LFM2.5 2.6B: 55.8%
Coding
SciCode
Scientific computing in Python
- Qwen 3.8 27B Kimiko: —
- LFM2.5 2.6B: 14.4%
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Qwen 3.8 27B Kimiko: —
- LFM2.5 2.6B: 5.7%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Prompt cache hits
- 79.7%
- About $0.0602 per 1M input tokens with caching
- Request success (24h)
- 100.0%
- Reliable
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
- Healthy (98% or more)
- Degraded (90–98%)
- Down (under 90%)
- No requests
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen/qwen3.8-27b-kimiko",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="qwen/qwen3.8-27b-kimiko",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Qwen
Related models
Other Qwen models, starting with the same family.