Moonshotai Kimi K2 Instruct
Together AI chatMoonshotai Kimi K2 Instruct is a Together AI chat model.Input is priced at $1.00/M tokens and output at $3.00/M tokens. Capabilities include function calling. Route Moonshotai Kimi K2 Instruct via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Moonshotai Kimi K2 Instruct spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $1.00/M input · $3.00/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 May 12, 2026.
| Input | $1.00/M | |
| Output | $3.00/M |
Limits
- Context window
- —
- Max input
- —
- Max output
- —
- Modalities
- text
Capabilities
- Function calling ✓ supported
- Parallel tool calls ✓ supported
- Vision input — not advertised
- Audio input — not advertised
- Audio output — not advertised
- PDF input — not advertised
- Streaming ✓ supported
- Structured output ✓ supported
- Prompt caching — not advertised
- Reasoning — not advertised
Where it's strong
- +parallel tool calls — only 21% of chat models on Future AGI advertise this
Watch out for
- No major caveats flagged from public spec.
Benchmarks pending
We haven't logged public benchmark scores for Moonshotai Kimi K2 Instruct yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Moonshotai Kimi K2 Instruct 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.# Moonshotai Kimi K2 Instruct 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="together-ai/moonshotai-kimi-k2-instruct",
messages=[{"role": "user", "content": "Hello, Moonshotai Kimi K2 Instruct!"}],
)
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="together-ai/moonshotai-kimi-k2-instruct",
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 ↗Same model on other providers
moonshotai-kimi-k2-instruct is also available via 4 other routes. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| Hyperbolic | $2.00/M | $2.00/M | May 12, 2026 |
| Novita AI | $0.570/M | $2.30/M | May 12, 2026 |
| W&B Inference | $0.600/M | $2.50/M | May 12, 2026 |
| DeepInfra | $0.500/M | $2.00/M | May 12, 2026 |
Compare with similar models
Moonshotai Kimi K2 Instruct doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.
FAQ
How much does Moonshotai Kimi K2 Instruct cost?
Input is priced at $1.00 per 1M tokens and output at $3.00 per 1M tokens (Together AI, last verified May 12, 2026).
What is the context window of Moonshotai Kimi K2 Instruct?
Context window for Moonshotai Kimi K2 Instruct is not currently public.
Does Moonshotai Kimi K2 Instruct support function calling?
Yes — Moonshotai Kimi K2 Instruct supports function (tool) calling, including parallel tool calls.
Is Moonshotai Kimi K2 Instruct good for production?
Moonshotai Kimi K2 Instruct is well-suited for parallel tool calls — only 21% of chat models on Future AGI advertise this.
How can I route to Moonshotai Kimi K2 Instruct 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 Moonshotai Kimi K2 Instruct
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.