DeepSeek DeepSeek R1 Distill Qwen 32B

Novita AI chat

DeepSeek DeepSeek R1 Distill Qwen 32B is a Novita AI chat model.It supports a 64,000-token context windowwith up to 32,000 output tokens.Input is priced at $0.300/M tokens and output at $0.300/M tokens. Capabilities include reasoning. Route DeepSeek 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 DeepSeek 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
064,000
400
032,000
5,000
01,000,000
Per request
$0.001020
in $0.000900 · out $0.000120
Per day
$5.10
5,000 requests
Per month
$155
152,188 requests

Estimate uses $0.3000/M input · $0.3000/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 $0.300/M
Output $0.300/M

Limits

Context window
64,000 tokens
Max input
64,000 tokens
Max output
32,000 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 ✓ supported
  • Prompt caching — not advertised
  • Reasoning ✓ supported

Where it's strong

  • +multi-step reasoning and analysis tasks

Watch out for

  • !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback

Benchmarks pending

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

Try it

Call DeepSeek 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.
# DeepSeek 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="novita-ai/deepseek-deepseek-r1-distill-qwen-32b",
    messages=[{"role": "user", "content": "Hello, DeepSeek 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="novita-ai/deepseek-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

DeepSeek 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 DeepSeek DeepSeek R1 Distill Qwen 32B cost?

Input is priced at $0.300 per 1M tokens and output at $0.300 per 1M tokens (Novita AI, last verified May 12, 2026).

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

DeepSeek DeepSeek R1 Distill Qwen 32B supports a 64,000-token context window with up to 32,000 output tokens.

Does DeepSeek DeepSeek R1 Distill Qwen 32B support function calling?

DeepSeek 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 DeepSeek DeepSeek R1 Distill Qwen 32B good for production?

DeepSeek DeepSeek R1 Distill Qwen 32B is well-suited for multi-step reasoning and analysis tasks. 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 DeepSeek 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 DeepSeek DeepSeek R1 Distill Qwen 32B

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