DeepSeek R1
SambaNova chatDeepSeek R1 is a SambaNova chat model.It supports a 32,768-token context windowwith up to 32,768 output tokens.Input is priced at $5.00/M tokens and output at $7.00/M tokens. Route DeepSeek R1 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate DeepSeek R1 spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $5.00/M input · $7.00/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 | $5.00/M | |
| Output | $7.00/M |
Limits
- Context window
- 32,768 tokens
- Max input
- 32,768 tokens
- Max output
- 32,768 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
- +pricing — cheaper than 79% of priced chat models on Future AGI
Watch out for
- !limited context — 32,768-token window is in the bottom quartile; not ideal for long documents or large RAG
- !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
Benchmark scores
Reported public benchmark numbers. Each row links to the source. Faded bar shows 6-peer average for context.
Call DeepSeek R1 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 R1 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="sambanova/deepseek-r1",
messages=[{"role": "user", "content": "Hello, DeepSeek R1!"}],
)
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="sambanova/deepseek-r1",
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 ↗Advanced: fallback + cache config (YAML) ▸
strategy: cost-optimized
targets:
- model: deepseek-r1
provider: sambanova
weight: 80
fallbacks:
- model: grok-3
provider: xai
- model: gpt-5-mini
provider: openai
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true }Same model on other providers
deepseek-r1 is also available via 3 other routes. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| Azure AI Foundry | $1.35/M | $5.40/M | May 12, 2026 |
| DeepSeek | $0.550/M | $2.19/M | May 12, 2026 |
| Snowflake Cortex | — | — | May 12, 2026 |
Compare with similar models
Grouped by Chatbot Arena tier (DeepSeek R1 sits at 1361 ELO).
≥30 ELO higher
- Claude Opus 4.6Azure AI Foundry · $5.00/M in · $25.00/M out · 200,000 ctx
- Claude Opus 4.6 (2026-02-05)Anthropic · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Gemini 3.1 Pro previewGoogle Vertex AI · $2.00/M in · $12.00/M out · 1,048,576 ctx
- Claude Opus 4.7Azure AI Foundry · $5.00/M in · $25.00/M out · 200,000 ctx
25–100 ELO lower, ≤50% of price
FAQ
How much does DeepSeek R1 cost?
Input is priced at $5.00 per 1M tokens and output at $7.00 per 1M tokens (SambaNova, last verified May 12, 2026).
What is the context window of DeepSeek R1?
DeepSeek R1 supports a 32,768-token context window with up to 32,768 output tokens.
Does DeepSeek R1 support function calling?
DeepSeek R1 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 R1 good for production?
DeepSeek R1 is well-suited for pricing — cheaper than 79% of priced chat models on Future AGI. Consider alternatives if you need limited context — 32,768-token window is in the bottom quartile; not ideal for long documents or large RAG.
How can I route to DeepSeek R1 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 R1
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.