DeepSeek AI DeepSeek R1 Distill Qwen 1.5B

Nscale chat

DeepSeek AI DeepSeek R1 Distill Qwen 1.5B is a Nscale chat model.Input is priced at $0.0900/M tokens and output at $0.0900/M tokens. Route DeepSeek AI DeepSeek R1 Distill Qwen 1.5B 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 AI DeepSeek R1 Distill Qwen 1.5B spend

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

~3K in / ~400 out · 5K req/day
3,000
0200,000
400
016,000
5,000
01,000,000
Per request
$0.000306
in $0.000270 · out $0.000036
Per day
$1.53
5,000 requests
Per month
$46.57
152,188 requests

Estimate uses $0.0900/M input · $0.0900/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 $0.0900/M
Output $0.0900/M

Limits

Context window
Max input
Max output
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

Watch out for

  • !high cost — input + output rates are in the top 96% of priced chat peers; consider a cheaper sibling for high-volume workloads
  • !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 DeepSeek AI DeepSeek R1 Distill Qwen 1.5B yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call DeepSeek AI DeepSeek R1 Distill Qwen 1.5B 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 AI DeepSeek R1 Distill Qwen 1.5B 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="nscale/deepseek-ai-deepseek-r1-distill-qwen-1-5b",
    messages=[{"role": "user", "content": "Hello, DeepSeek AI DeepSeek R1 Distill Qwen 1.5B!"}],
)

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="nscale/deepseek-ai-deepseek-r1-distill-qwen-1-5b",
    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 DeepSeek AI DeepSeek R1 Distill Qwen 1.5B cost?

Input is priced at $0.0900 per 1M tokens and output at $0.0900 per 1M tokens (Nscale, last verified May 12, 2026).

What is the context window of DeepSeek AI DeepSeek R1 Distill Qwen 1.5B?

Context window for DeepSeek AI DeepSeek R1 Distill Qwen 1.5B is not currently public.

Does DeepSeek AI DeepSeek R1 Distill Qwen 1.5B support function calling?

DeepSeek AI DeepSeek R1 Distill Qwen 1.5B 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 AI DeepSeek R1 Distill Qwen 1.5B good for production?

DeepSeek AI DeepSeek R1 Distill Qwen 1.5B is best evaluated against your own production traces. Pipe traffic through Agent Command Center to compare it head-to-head against alternatives in shadow mode.

How can I route to DeepSeek AI DeepSeek R1 Distill Qwen 1.5B 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 AI DeepSeek R1 Distill Qwen 1.5B

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