Kimi K2.0711 preview

Moonshot AI chat Deprecated 77d ago
Heads-up: Moonshot AI has scheduled Kimi K2.0711 preview for deprecation on May 25, 2026. Plan a migration. Use Agent Command Center's model fallback routing to swap models without code changes.

Kimi K2.0711 preview is a Moonshot AI chat model.It supports a 131,072-token context windowwith up to 131,072 output tokens.Input is priced at $0.600/M tokens and output at $2.50/M tokens. Capabilities include function calling. Route Kimi K2.0711 preview via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: Aug 6, 2026 View source ↗
Cost calculator

Estimate Kimi K2.0711 preview spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
0131,072
400
0131,072
5,000
01,000,000
cached @ $0.1500/M
Per request
$0.002800
in $0.001800 · out $0.001000
Per day
$14.00
5,000 requests
Per month
$426
152,188 requests

Estimate uses $0.6000/M input · $2.50/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.

Pricing

Per-token rates, expressed in USD per 1M tokens. Verified Aug 6, 2026.

Input $0.600/M
Output $2.50/M
Blended (3:1 in:out) $1.08/M
Cross-model comparable price
Cached input $0.150/M

Context & limits

Context window
131,072 tokens
Max input
131,072 tokens
Max output
131,072 tokens
Modalities
text

Capabilities

  • Function calling ✓ supported
  • Parallel tool calls — not advertised
  • Vision input — not advertised
  • Audio input — not advertised
  • Audio output — not advertised
  • PDF input — not advertised
  • Streaming ✓ supported
  • Structured output — not advertised
  • Prompt caching — not advertised
  • Reasoning — not advertised

Strengths & caveats — when to pick Kimi K2.0711 preview

Data-driven from Kimi K2.0711 preview's percentile within chat peers.

Where it's strong

  • +long-form generation — 131,072-token max output, top-10% of peers

Watch out for

  • !strict structured output — no JSON-schema enforcement, expect retry loops
  • !already deprecated — provider stopped accepting new traffic 77 days ago

Benchmark scores

Reported public benchmark numbers. Each row links to the source.

Chatbot Arena ELOgeneral· overall
Captured Aug 8, 2026
Try it

Call Kimi K2.0711 preview via Agent Command Center

One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.

SDK
Native Future AGI client (agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.
# Kimi K2.0711 preview via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC

client = AgentCC(
    api_key=os.environ["AGENTCC_API_KEY"],   # from app.futureagi.com → Settings → API Keys
    base_url="https://gateway.futureagi.com/v1",
)

resp = client.chat.completions.create(
    model="moonshot/kimi-k2-0711-preview",
    messages=[{"role": "user", "content": "Hello, Kimi K2.0711 preview!"}],
)

print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")

# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
    model="moonshot/kimi-k2-0711-preview",
    messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost:   ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache:  ", raw.headers.get("x-agentcc-cache"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗
Advanced: fallback + cache config (YAML)
strategy: cost-optimized
targets:
  - model: kimi-k2-0711-preview
    provider: moonshot
    weight: 80
fallbacks:
  - model: gpt-5-2-2025-12-11
    provider: openai
  - model: gpt-5-2-chat-latest
    provider: openai
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true }

Compare with similar models

Grouped by Chatbot Arena tier (Kimi K2.0711 preview sits at 1418 ELO).

FAQ

How much does Kimi K2.0711 preview cost?

Input is priced at $0.600 per 1M tokens and output at $2.50 per 1M tokens (Moonshot AI, last verified Aug 6, 2026).

What is the context window of Kimi K2.0711 preview?

Kimi K2.0711 preview supports a 131,072-token context window with up to 131,072 output tokens.

Does Kimi K2.0711 preview support function calling?

Yes — Kimi K2.0711 preview supports function (tool) calling.

Is Kimi K2.0711 preview good for production?

Kimi K2.0711 preview is well-suited for long-form generation — 131,072-token max output, top-10% of peers. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.

How can I route to Kimi K2.0711 preview with fallback?

Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.

Useful links for Kimi K2.0711 preview

Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.