Kimi K2.7 Code APITESTEDOPEN WEIGHTS262K context
MoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts.
Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go). Coding score is our own measured result.
✅ First-party tested — we ran Kimi K2.7 Code on our 9-task coding suite
Executed against hidden tests at real billed cost — full results · how we test.
What is Kimi K2.7 Code?
MoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts. It uses a native multimodal mixture-of-experts...
Kimi K2.7 Code on the release timeline
Kimi K2.7 Code pricing
Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):
| Model | Input / 1M | Output / 1M | Cache read | Cache write | Context |
|---|---|---|---|---|---|
| Kimi K2.7 Code | $0.74 | $3.50 | $0.15 | — | 262K |
On output price, Kimi K2.7 Code is cheaper than 32% of the 309 models in the catalog.
What Kimi K2.7 Code costs per month
| Workload | Tokens in / out (monthly) | Est. cost |
|---|---|---|
| Support chatbot | 40M / 12M | $71.60 |
| RAG / knowledge base | 200M / 20M | $218 |
| Coding agent | 80M / 25M | $147 |
| Batch extraction | 150M / 8M | $139 |
| Content generation | 20M / 40M | $155 |
Estimate your own monthly cost
Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.
Kimi K2.7 Code vs alternatives
| Model | Input / 1M | Output / 1M | Context | Our test |
|---|---|---|---|---|
| Kimi K2.7 Code | $0.74 | $3.50 | 262K | 9/9 |
| Kimi K2.6 | $0.55 | $3.20 | 262K | — |
| Kimi K2 0905 | $0.60 | $2.50 | 262K | — |
| Kimi K2 Thinking | $0.60 | $2.50 | 262K | — |
| Switchpoint Router | $0.85 | $3.40 | 131K | — |
| Qwen3 Coder Plus | $0.65 | $3.25 | 1M | — |
Specs
| Model ID | moonshotai/kimi-k2.7-code |
| Modality | text+image->text (input: text, image) |
| Context window | 262,144 tokens |
| Max output | 16,384 tokens |
| Tool / function calling | ✅ Yes |
| Structured output (JSON) | ✅ Yes |
| Released | 2026-06-12 |
| Open weights | ✅ moonshotai/Kimi-K2.7-Code |
How to call Kimi K2.7 Code
from openai import OpenAI
client = OpenAI(base_url="https://api.datallmlab.com/v1", api_key="YOUR_DATALLM_LAB_KEY")
resp = client.chat.completions.create(
model="moonshotai/kimi-k2.7-code",
messages=[{"role": "user", "content": "Hello"}],
# supports tools=[...] and tool_choice
)
print(resp.choices[0].message.content)curl https://api.datallmlab.com/v1/chat/completions \
-H "Authorization: Bearer YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"moonshotai/kimi-k2.7-code","messages":[{"role":"user","content":"Hello"}]}'import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.datallmlab.com/v1", apiKey: process.env.DATALLM_KEY });
const r = await client.chat.completions.create({
model: "moonshotai/kimi-k2.7-code",
messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);The same key routes Kimi K2.7 Code and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.
Prompting tips for Kimi K2.7 Code
- State the goal, not every step. Kimi K2.7 Code reasons internally — give it the objective, constraints and success criteria and let it plan the approach; over-scripting each step tends to lower quality. Turn effort up for hard problems.
- Huge 262K context — but anchor the ask. You can paste whole documents or codebases; models attend most to the start and end, so put the key instruction at the top and restate it after long inputs.
- It reads images. Send image parts alongside your text and ask for specific outputs (named JSON fields, a table, "what changed") rather than "describe this".
- Give it real tools. Pass a
tools=[...]schema instead of asking it to "pretend" — let Kimi K2.7 Code emit tool calls, execute them, and feed results back for the next turn. - Constrain JSON with a schema. Use
response_format/ structured outputs rather than "reply in JSON" to get valid, parseable objects every time.
Tips are derived from Kimi K2.7 Code's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.
Open-source tools for Kimi K2.7 Code
Popular open-source projects for running and building with Kimi K2.7 Code — star counts pulled from GitHub (July 2026).
Listed by GitHub stars; inclusion is by ecosystem relevance (inference engines, agent frameworks and SDKs), not affiliation. Stars change — see each repo for current numbers.
Migrating to Kimi K2.7 Code
Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → moonshotai/kimi-k2.7-code. Messages, streaming, tool calls and the rest of your code stay the same. Routing & failover guide.
Self-host or use the API?
Kimi K2.7 Code ships open weights (moonshotai/Kimi-K2.7-Code), so you can run it yourself. Most teams still use the API: no GPU to provision or keep warm, no inference ops, and instant access across multiple providers with automatic failover. Self-host when data residency or fixed per-token economics matter most.
Rate limits & reliability
Rate limits here are the DataLLM Lab gateway's, not the upstream vendor's. On 429 (rate limited) or 503 (provider busy), retry with exponential backoff. See the error-code guide and failover setup.
Related reading
Call Kimi K2.7 Code with one key
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Get an API keyCompare pricingFrequently asked questions
What is Kimi K2.7 Code?
MoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts. It accepts text, image input with a 262K-token context window and was released on 2026-06-12. On DataLLM Lab it is callable through one OpenAI-compatible endpoint.
How much does Kimi K2.7 Code cost?
On DataLLM Lab it is $0.74 per 1M input tokens and $3.50 per 1M output tokens, with cached input at $0.15/1M — cheaper than about 32% of the 309-model catalog on output price. Pay-as-you-go, no subscription.
What is the context window of Kimi K2.7 Code?
262K tokens, with up to 16K max output tokens.
Is Kimi K2.7 Code open source?
Yes — open weights are published on Hugging Face (moonshotai/Kimi-K2.7-Code). You can self-host it or call it via DataLLM Lab.
How do I call the Kimi K2.7 Code API?
Point any OpenAI SDK at https://api.datallmlab.com/v1 and set model to "moonshotai/kimi-k2.7-code". One DataLLM Lab key routes this model and 300+ others; no code changes beyond the base URL and model string.
How did Kimi K2.7 Code score in real testing?
In our executed 9-task coding benchmark it scored 9/9, averaging 11.2s per task at ~$2.23 per 1,000 tasks (real billed cost), generating 3,606 reasoning tokens. See our methodology for scope and limits.