DeepSeek v3.1

Azure AI Foundry chat Deprecated 114d ago
Heads-up: Azure AI Foundry has scheduled DeepSeek v3.1 for deprecation on Jul 13, 2026. Plan a migration. Use Agent Command Center's model fallback routing to swap models without code changes.

DeepSeek v3.1 is an Azure AI Foundry chat model.Input is priced at $1.23/M tokens and output at $4.94/M tokens. Route DeepSeek v3.1 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: Oct 22, 2026 View source ↗
Cost calculator

Estimate DeepSeek v3.1 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
$0.005666
in $0.003690 · out $0.001976
Per day
$28.33
5,000 requests
Per month
$862
152,188 requests

Estimate uses $1.23/M input · $4.94/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 $1.23/M
Output $4.94/M
Blended (3:1 in:out) $2.16/M
Cross-model comparable price

Context & 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

Strengths & caveats — when to pick DeepSeek v3.1

Data-driven from DeepSeek v3.1'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
  • !strict structured output — no JSON-schema enforcement, expect retry loops
  • !already deprecated — provider stopped accepting new traffic 114 days ago

Benchmarks pending

We haven't logged public benchmark scores for DeepSeek v3.1 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call DeepSeek v3.1 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 v3.1 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="azure-ai-foundry/deepseek-v3-1",
    messages=[{"role": "user", "content": "Hello, DeepSeek v3.1!"}],
)

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="azure-ai-foundry/deepseek-v3-1",
    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 ↗

Same model on other providers

deepseek-v3-1 is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
SambaNova$3.00/M$4.50/MOct 22, 2026

Explore similar models

DeepSeek v3.1 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.

FAQ

How much does DeepSeek v3.1 cost?

Input is priced at $1.23 per 1M tokens and output at $4.94 per 1M tokens (Azure AI Foundry, last verified Oct 22, 2026).

What is the context window of DeepSeek v3.1?

Context window for DeepSeek v3.1 is not currently public.

Does DeepSeek v3.1 support function calling?

DeepSeek v3.1 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 v3.1 good for production?

DeepSeek v3.1 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 v3.1 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 v3.1

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