Kimi K2 Thinking 251104

Volcano Engine chat

Kimi K2 Thinking 251104 is a Volcano Engine chat model.It supports a 229,376-token context windowwith up to 32,768 output tokens. Capabilities include function calling, reasoning, prompt caching. Route Kimi K2 Thinking 251104 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: unknown Last verified: May 12, 2026 View source ↗
Pricing not yet public

We don't have verified per-token pricing for Kimi K2 Thinking 251104 yet. If you have a source from Volcano Engine's documentation, help us add it — your submission gets reviewed within 48 hours.

Pricing

Per-token rates, expressed in USD per 1M tokens. Verified May 12, 2026.

Input
Output

Limits

Context window
229,376 tokens
Max input
229,376 tokens
Max output
32,768 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 ✓ supported
  • Reasoning ✓ supported

Where it's strong

  • +prompt caching — only 23% of chat models on Future AGI advertise this

Watch out for

  • !strict structured output — no JSON-schema enforcement, expect retry loops

Benchmarks pending

We haven't logged public benchmark scores for Kimi K2 Thinking 251104 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Kimi K2 Thinking 251104 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 Thinking 251104 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="volcengine/kimi-k2-thinking-251104",
    messages=[{"role": "user", "content": "Hello, Kimi K2 Thinking 251104!"}],
)

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="volcengine/kimi-k2-thinking-251104",
    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 ↗

FAQ

How much does Kimi K2 Thinking 251104 cost?

Public per-token pricing for Kimi K2 Thinking 251104 is not yet published. Submit a source on this page to help us add it.

What is the context window of Kimi K2 Thinking 251104?

Kimi K2 Thinking 251104 supports a 229,376-token context window with up to 32,768 output tokens.

Does Kimi K2 Thinking 251104 support function calling?

Yes — Kimi K2 Thinking 251104 supports function (tool) calling.

Is Kimi K2 Thinking 251104 good for production?

Kimi K2 Thinking 251104 is well-suited for prompt caching — only 23% of chat models on Future AGI advertise this. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.

How can I route to Kimi K2 Thinking 251104 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 Thinking 251104

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