DeepSeek R1
Fireworks AI chatDeepSeek R1 is a Fireworks AI chat model.It supports a 163,840-token context windowwith up to 20,480 output tokens.Input is priced at $3.00/M tokens and output at $8.00/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 $3.00/M input · $8.00/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 Oct 22, 2026.
| Input | $3.00/M | |
| Output | $8.00/M | |
| Blended (3:1 in:out) | $4.25/M Cross-model comparable price |
Context & limits
- Context window
- 163,840 tokens
- Max input
- 163,840 tokens
- Max output
- 20,480 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 — not advertised
Strengths & caveats — when to pick DeepSeek R1
Data-driven from DeepSeek R1's percentile within chat peers.
Where it's strong
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 R1 yet. Have one to contribute? Submit a source — citations help us prioritise.
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="fireworks-ai/accounts-fireworks-models-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="fireworks-ai/accounts-fireworks-models-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 ↗Explore similar models
DeepSeek R1 doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark. Side-by-side links appear where a comparison page exists; otherwise, open the model profile.
- DeepSeek V4.1 FlashFireworks AI · $0.300/M in · $1.20/M out · 1,048,576 ctxView model →
- DeepSeek V4.1 Flash 1017Fireworks AI · $0.300/M in · $1.20/M out · 1,048,576 ctxView model →
- DeepSeek V4.1 Flash 1017 UsFireworks AI · $0.450/M in · $1.80/M out · 1,048,576 ctxView model →
- DeepSeek V4.1 Flash (router · US)Fireworks AI · $0.450/M in · $1.80/M out · 1,048,576 ctxView model →
FAQ
How much does DeepSeek R1 cost?
Input is priced at $3.00 per 1M tokens and output at $8.00 per 1M tokens (Fireworks AI, last verified Oct 22, 2026).
What is the context window of DeepSeek R1?
DeepSeek R1 supports a 163,840-token context window with up to 20,480 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 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 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.