Kimi K2 0905 preview
Moonshot AI chat Deprecated 163d agomodel fallback routing to swap models without code changes.
Kimi K2 0905 preview is a Moonshot AI chat model.It supports a 262,144-token context windowwith up to 262,144 output tokens.Input is priced at $0.600/M tokens and output at $2.50/M tokens. Capabilities include function calling. Route Kimi K2 0905 preview via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Kimi K2 0905 preview spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
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
- 262,144 tokens
- Max input
- 262,144 tokens
- Max output
- 262,144 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 0905 preview
Data-driven from Kimi K2 0905 preview's percentile within chat peers.
Where it's strong
- +long-form generation — 262,144-token max output, top-5% of peers
Watch out for
- !strict structured output — no JSON-schema enforcement, expect retry loops
- !already deprecated — provider stopped accepting new traffic 163 days ago
Benchmark scores
Reported public benchmark numbers. Each row links to the source.
Call Kimi K2 0905 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.
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 0905 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-k2-0905-preview",
messages=[{"role": "user", "content": "Hello, Kimi K2 0905 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-k2-0905-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"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Advanced: fallback + cache config (YAML) ▸
strategy: cost-optimized
targets:
- model: kimi-k2-0905-preview
provider: moonshot
weight: 80
fallbacks:
- model: gpt-5-5-2026-04-23
provider: openai
- model: gemini-3-5-flash
provider: vertex-ai
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true } Explore similar models
Grouped by Chatbot Arena tier (Kimi K2 0905 preview sits at 1418 ELO). Side-by-side links appear where a comparison page exists; otherwise, open the model profile.
- GPT-5.5 (2026-04-23)OpenAI · $5.00/M in · $30.00/M out · 1,050,000 ctxView model →
- Gemini 3.5 FlashGoogle Vertex AI · $1.50/M in · $9.00/M out · 1,048,576 ctxView model →
- GPT-5.5OpenAI · $5.00/M in · $30.00/M out · 1,050,000 ctxView model →
- Claude Sonnet 5.5Anthropic · $2.00/M in · $10.00/M out · 1,000,000 ctxView model →
≥30 ELO higher
- Gemini 4 ArgonGoogle AI · $2.00/M in · $10.00/M out · 1,048,576 ctxView model →
- Claude Opus 5.5Azure AI Foundry · $4.00/M in · $20.00/M out · 1,000,000 ctxView model →
- Claude Fable 5Azure AI Foundry · $10.00/M in · $50.00/M out · 1,000,000 ctxView model →
- Claude Fable 5.1Azure AI Foundry · $10.00/M in · $50.00/M out · 1,000,000 ctxView model →
25–100 ELO lower, ≤50% of price
- GPT-5 nanoAzure OpenAI · $0.0500/M in · $0.400/M out · 272,000 ctxView model →
- GPT-5 nano (2025-08-07)Azure OpenAI · $0.0500/M in · $0.400/M out · 272,000 ctxView model →
- GPT-4.1 nano (2025-04-14)Azure OpenAI · $0.1000/M in · $0.400/M out · 1,047,576 ctxView model →
- GPT-4o miniAzure OpenAI · $0.150/M in · $0.600/M out · 128,000 ctxView model →
FAQ
How much does Kimi K2 0905 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 0905 preview?
Kimi K2 0905 preview supports a 262,144-token context window with up to 262,144 output tokens.
Does Kimi K2 0905 preview support function calling?
Yes — Kimi K2 0905 preview supports function (tool) calling.
Is Kimi K2 0905 preview good for production?
Kimi K2 0905 preview is well-suited for long-form generation — 262,144-token max output, top-5% of peers. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.
How can I route to Kimi K2 0905 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 0905 preview
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Start here — vendor docs are the only authoritative spec.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.