Accounts Fireworks Models Kimi K2 Instruct 0905

Fireworks AI chat

Accounts Fireworks Models Kimi K2 Instruct 0905 is a Fireworks AI chat model.It supports a 262,144-token context windowwith up to 32,768 output tokens.Input is priced at $0.600/M tokens and output at $2.50/M tokens. Capabilities include function calling. Route Accounts Fireworks Models Kimi K2 Instruct 0905 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: May 12, 2026 View source ↗
Cost calculator

Estimate Accounts Fireworks Models Kimi K2 Instruct 0905 spend

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

~3K in / ~400 out · 5K req/day
3,000
0262,144
400
032,768
5,000
01,000,000
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 May 12, 2026.

Input $0.600/M
Output $2.50/M

Limits

Context window
262,144 tokens
Max input
262,144 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 ✓ supported
  • Prompt caching — not advertised
  • Reasoning — not advertised

Where it's strong

  • +agentic workflows that depend on reliable tool calls

Watch out for

  • No major caveats flagged from public spec.

Benchmarks pending

We haven't logged public benchmark scores for Accounts Fireworks Models Kimi K2 Instruct 0905 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Accounts Fireworks Models Kimi K2 Instruct 0905 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.
# Accounts Fireworks Models Kimi K2 Instruct 0905 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="fireworks-ai/accounts-fireworks-models-kimi-k2-instruct-0905",
    messages=[{"role": "user", "content": "Hello, Accounts Fireworks Models Kimi K2 Instruct 0905!"}],
)

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="fireworks-ai/accounts-fireworks-models-kimi-k2-instruct-0905",
    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 ↗

Compare with similar models

Accounts Fireworks Models Kimi K2 Instruct 0905 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 Accounts Fireworks Models Kimi K2 Instruct 0905 cost?

Input is priced at $0.600 per 1M tokens and output at $2.50 per 1M tokens (Fireworks AI, last verified May 12, 2026).

What is the context window of Accounts Fireworks Models Kimi K2 Instruct 0905?

Accounts Fireworks Models Kimi K2 Instruct 0905 supports a 262,144-token context window with up to 32,768 output tokens.

Does Accounts Fireworks Models Kimi K2 Instruct 0905 support function calling?

Yes — Accounts Fireworks Models Kimi K2 Instruct 0905 supports function (tool) calling.

Is Accounts Fireworks Models Kimi K2 Instruct 0905 good for production?

Accounts Fireworks Models Kimi K2 Instruct 0905 is well-suited for agentic workflows that depend on reliable tool calls.

How can I route to Accounts Fireworks Models Kimi K2 Instruct 0905 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 Accounts Fireworks Models Kimi K2 Instruct 0905

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