Moonshot · Open weights
Kimi K2.6 Thinking
Kimi K2.6 Thinking is the reasoning-optimized K2.6 variant for deeper multi-step planning and execution. It is tuned for long-horizon coding and design workflows, including complex orchestration across many specialized sub-agents and autonomous end-to-end output generation.
- Reasoning
- Tools
- Vision
- Parallel tools
- Input
- Text, Images
- Output
- Text
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.45
- Output
- $1.89
- Cache read
- $0.0757
Reference prices as published by the model provider. API example
Specifications
- Context window
- 262.1K
- Max output
- 65.5K
- Average output (7 days)
- 4K tokens
- Longer than 98% of 465 models
Side by side
Kimi K2.6 Thinking compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Kimi K2.6 ThinkingMoonshotThis model | Solar Pro 4upstageRemove | |
|---|---|---|
| Provider | Moonshot | upstage |
| Discovery tier | Open weights | Efficient |
| Added | Apr 16, 2026 | Aug 11, 2026 |
| Limits | ||
| Context window | 262.14K | 524.29KBest |
| Max output | 65.54K | 131.07KBest |
| Average output | 4K tokens | 546 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.45 | $0.03Best |
| Output | $1.89 | $0.12Best |
| Cache read | $0.0757 | $0.006Best |
| Performance | ||
| Throughput | 40.8 tok/s | 80.3 tok/sBest |
| Time to first token | 2.9 s | 2.4 sBest |
| Request success (24h) | 99.8% | 99.9%Best |
| Benchmark indexes | ||
| Intelligence Index | 27.0Best | 26.6 |
| Coding Index | 61.8 | — |
| Capabilities | ||
| Reasoning | Yes | No |
| Tools | Yes | Yes |
| Vision | Yes | No |
| Structured output | No | Yes |
| Parallel tools | Yes | No |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Kimi K2.6 Thinking
- Solar Pro 4
Intelligence Index
- Kimi K2.6 Thinking27.0
- Solar Pro 426.6
Kimi K2.6 Thinking: Better than 68% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Kimi K2.6 Thinking: 61.8
- Solar Pro 4: —
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Kimi K2.6 Thinking: 13.0%
- Solar Pro 4: 13.6%Top
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Kimi K2.6 Thinking: 1.8%
- Solar Pro 4: 2.4%Top
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Kimi K2.6 Thinking: 819 Elo
- Solar Pro 4: 1,093 EloTop
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Kimi K2.6 Thinking: 1,038 Elo
- Solar Pro 4: 1,106 EloTop
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Kimi K2.6 Thinking: 13.0%Top
- Solar Pro 4: 7.0%
AA-LCRv1.1
Reasoning across long documents
- Kimi K2.6 Thinking: 81.0%Top
- Solar Pro 4: 70.7%
MLCR-AA
Long-context reasoning over medical records
- Kimi K2.6 Thinking: 11.1%
- Solar Pro 4: —
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Kimi K2.6 Thinking: 37.5%Top
- Solar Pro 4: 29.8%
GPQA Diamond
Graduate-level science questions
- Kimi K2.6 Thinking: 91.1%Top
- Solar Pro 4: 89.1%
IFBench
Following precise output instructions
- Kimi K2.6 Thinking: 76.0%
- Solar Pro 4: —
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Kimi K2.6 Thinking: 0.5%Top
- Solar Pro 4: 0.0%
SciCode
Scientific computing in Python
- Kimi K2.6 Thinking: 51.5%Top
- Solar Pro 4: 48.0%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Kimi K2.6 Thinking: 43.9%
- Solar Pro 4: —
τ²-Bench Telecom
Tool use in a customer-support simulation
- Kimi K2.6 Thinking: 95.9%
- Solar Pro 4: —
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Kimi K2.6 Thinking: 81.0%Top
- Solar Pro 4: 70.7%
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.
- Throughput
- 40.8 tok/s
- Faster than 30% of 250 models
- Time to first token
- 2.9 s
- Quicker than 58% of 250 models
- Prompt cache hits
- 52.3%
- About $0.25 per 1M input tokens with caching
- Request success (24h)
- 99.8%
- 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": "moonshotai/kimi-k2.6:thinking",
"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="moonshotai/kimi-k2.6:thinking",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Moonshot
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