Kimi K3
GitHub Copilot chatKimi K3 is a GitHub Copilot chat model.It supports a 917,504-token context windowwith up to 131,072 output tokens.Input is priced at $3.00/M tokens and output at $15.00/M tokens. Capabilities include function calling, vision. Route Kimi K3 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Kimi K3 spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $3.00/M input · $15.00/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 Oct 22, 2026.
| Input | $3.00/M | |
| Output | $15.00/M | |
| Blended (3:1 in:out) | $6.00/M Cross-model comparable price | |
| Cached input | $0.300/M |
Context & limits
- Context window
- 917,504 tokens
- Max input
- 917,504 tokens
- Max output
- 131,072 tokens
- Modalities
- vision, text
Capabilities
- Function calling ✓ supported
- Parallel tool calls ✓ supported
- Vision input ✓ supported
- Audio input — not advertised
- Audio output — not advertised
- PDF input — not advertised
- Streaming ✓ supported
- Structured output ✓ supported
- Prompt caching — not advertised
- Reasoning — not advertised
Strengths & caveats — when to pick Kimi K3
Data-driven from Kimi K3's percentile within chat peers.
Where it's strong
- +pricing — cheaper than 87% of priced chat models on Future AGI
- +long-form generation — 131,072-token max output, top-12% of peers
- +parallel tool calls — only 18% of chat models on Future AGI advertise this
Watch out for
- No major caveats flagged from public spec.
Benchmark scores
Reported public benchmark numbers. Each row links to the source. Faded bar shows 6-peer average for context.
Call Kimi K3 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 K3 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="github-copilot/kimi-k3",
messages=[{"role": "user", "content": "Hello, Kimi K3!"}],
)
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="github-copilot/kimi-k3",
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-k3
provider: github-copilot
weight: 80
fallbacks:
- model: gemini-4-argon
provider: google
- model: claude-opus-5-5
provider: azure-ai-foundry
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true } Same model on other providers
kimi-k3 is also available via 2 other routes. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| Fireworks AI | $3.00/M | $15.00/M | Oct 22, 2026 |
| Moonshot AI | $3.00/M | $15.00/M | Oct 22, 2026 |
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FAQ
How much does Kimi K3 cost?
Input is priced at $3.00 per 1M tokens and output at $15.00 per 1M tokens (GitHub Copilot, last verified Oct 22, 2026).
What is the context window of Kimi K3?
Kimi K3 supports a 917,504-token context window with up to 131,072 output tokens.
Does Kimi K3 support function calling?
Yes — Kimi K3 supports function (tool) calling, including parallel tool calls.
Is Kimi K3 good for production?
Kimi K3 is well-suited for pricing — cheaper than 87% of priced chat models on Future AGI and long-form generation — 131,072-token max output, top-12% of peers.
How can I route to Kimi K3 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 K3
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.