Accounts Fireworks Models DeepSeek R1 Distill Qwen 32B

Fireworks AI chat

Accounts Fireworks Models DeepSeek R1 Distill Qwen 32B is a Fireworks AI chat model.It supports a 131,072-token context windowwith up to 131,072 output tokens.Input is priced at $0.900/M tokens and output at $0.900/M tokens. Route Accounts Fireworks Models DeepSeek R1 Distill Qwen 32B 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 DeepSeek R1 Distill Qwen 32B spend

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

~3K in / ~400 out · 5K req/day
3,000
0131,072
400
0131,072
5,000
01,000,000
Per request
$0.003060
in $0.002700 · out $0.000360
Per day
$15.30
5,000 requests
Per month
$466
152,188 requests

Estimate uses $0.9000/M input · $0.9000/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 $0.900/M
Output $0.900/M

Limits

Context window
131,072 tokens
Max input
131,072 tokens
Max output
131,072 tokens
Modalities
text

Capabilities

  • Function calling — not advertised
  • 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

Where it's strong

  • +long-form generation — 131,072-token max output, top-10% of peers

Watch out for

  • !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback
  • !strict structured output — no JSON-schema enforcement, expect retry loops

Benchmarks pending

We haven't logged public benchmark scores for Accounts Fireworks Models DeepSeek R1 Distill Qwen 32B yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Accounts Fireworks Models DeepSeek R1 Distill Qwen 32B 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 DeepSeek R1 Distill Qwen 32B 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-deepseek-r1-distill-qwen-32b",
    messages=[{"role": "user", "content": "Hello, Accounts Fireworks Models DeepSeek R1 Distill Qwen 32B!"}],
)

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-deepseek-r1-distill-qwen-32b",
    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 DeepSeek R1 Distill Qwen 32B 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 DeepSeek R1 Distill Qwen 32B cost?

Input is priced at $0.900 per 1M tokens and output at $0.900 per 1M tokens (Fireworks AI, last verified May 12, 2026).

What is the context window of Accounts Fireworks Models DeepSeek R1 Distill Qwen 32B?

Accounts Fireworks Models DeepSeek R1 Distill Qwen 32B supports a 131,072-token context window with up to 131,072 output tokens.

Does Accounts Fireworks Models DeepSeek R1 Distill Qwen 32B support function calling?

Accounts Fireworks Models DeepSeek R1 Distill Qwen 32B does not currently advertise function-calling support. For agentic workloads, prefer a tool-calling-capable model and route via Agent Command Center for fallback.

Is Accounts Fireworks Models DeepSeek R1 Distill Qwen 32B good for production?

Accounts Fireworks Models DeepSeek R1 Distill Qwen 32B is well-suited for long-form generation — 131,072-token max output, top-10% of peers. Consider alternatives if you need agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback.

How can I route to Accounts Fireworks Models DeepSeek R1 Distill Qwen 32B 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 DeepSeek R1 Distill Qwen 32B

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