Last refreshed 2026-06-30. Next refresh: weekly.
Why use Kimi K2.6 on Together AI?
Together AI offers Kimi K2.6 with pay-as-you-go pricing at $1.20/1M input tokens. Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.
Compare Kimi K2.6 across 9 providers to find the best fit for your use caseSetup recipe
Python + curlpip install togetherexport TOGETHER_API_KEY=...from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="moonshotai/Kimi-K2.6",moonshotai/Kimi-K2.6Request example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="moonshotai/Kimi-K2.6",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "moonshotai/Kimi-K2.6", not the LLMReference slug "kimi-k2-6".
- Together uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
- The examples expect TOGETHER_API_KEY; rename it only if your application config maps the new variable.
Compare Kimi K2.6 Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Cloudflare Workers AI | $0.95 | $4.00 |
| NVIDIA NIM | — | — |
| Moonshot AI Kimi | $0.95 | $4.00 |
| Fireworks AI | $0.95 | $4.00 |
| OpenRouter | $0.73 | $3.49 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $1.20 |
| Output tokens | $4.50 |
Capabilities
About Kimi K2.6
Kimi K2.6 is Moonshot AI's multimodal agentic coding model, released April 20 2026 under a Modified MIT license. Built on a 1-trillion-parameter MoE architecture (32B active, 384 experts with 8 selected per token plus 1 shared expert, 61 layers), it features a 262K context window and up to 65,536 output tokens. Supports native image and video inputs (screenshots, PDFs, spreadsheets). Designed for long-horizon coding with agent swarms of up to 300 sub-agents and 4,000 coordinated steps; Moonshot AI cites 200–300 sequential tool calls without task drift.