DeepSeek DeepSeek R1 Distill Qwen 32B
Novita AI chatDeepSeek 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.
Estimate DeepSeek DeepSeek R1 Distill Qwen 32B spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
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.
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.
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"))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.
- Baidu Ernie 4.5 VL 28B A3b ThinkingNovita AI · $0.390/M in · $0.390/M out · 131,072 ctx
- OpenAI GPT Oss 120BNovita AI · $0.0500/M in · $0.250/M out · 131,072 ctx
- Zai Org Glm 4.6vNovita AI · $0.300/M in · $0.900/M out · 131,072 ctx
- Qwen Qwen3 Omni 30B A3b ThinkingNovita AI · $0.250/M in · $0.970/M out · 65,536 ctx
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.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.