DeepSeek V3.1 Maas

Google Vertex AI chat Deprecated 14d ago
Heads-up: Google Vertex AI has scheduled DeepSeek V3.1 Maas for deprecation on Oct 21, 2026. Plan a migration. Use Agent Command Center's model fallback routing to swap models without code changes.

DeepSeek V3.1 Maas is a Google Vertex AI chat model.It supports a 163,840-token context windowwith up to 32,768 output tokens.Input is priced at $0.600/M tokens and output at $1.70/M tokens. Capabilities include function calling, reasoning, prompt caching. Route DeepSeek V3.1 Maas 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 Maas spend

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

~3K in / ~400 out · 5K req/day
3,000
0163,840
400
032,768
5,000
01,000,000
cached @ $0.0600/M
Per request
$0.002480
in $0.001800 · out $0.000680
Per day
$12.40
5,000 requests
Per month
$377
152,188 requests

Estimate uses $0.6000/M input · $1.70/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 $0.600/M
Output $1.70/M
Blended (3:1 in:out) $0.875/M
Cross-model comparable price
Cached input $0.0600/M
Batch input $0.300/M
Batch output $0.850/M

Context & limits

Context window
163,840 tokens
Max input
163,840 tokens
Max output
32,768 tokens
Modalities
text

Capabilities

  • Function calling ✓ supported
  • 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 ✓ supported
  • Reasoning ✓ supported

Strengths & caveats — when to pick DeepSeek V3.1 Maas

Data-driven from DeepSeek V3.1 Maas's percentile within chat peers.

Where it's strong

  • +multi-step reasoning and analysis tasks
  • +agentic workflows that depend on reliable tool calls
  • +repeated long-context prompts (caching saves up to 90% on input cost)

Watch out for

  • !strict structured output — no JSON-schema enforcement, expect retry loops
  • !already deprecated — provider stopped accepting new traffic 14 days ago

Benchmarks pending

We haven't logged public benchmark scores for DeepSeek V3.1 Maas yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call DeepSeek V3.1 Maas 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 Maas 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="vertex-ai/deepseek-ai-deepseek-v3-1-maas",
    messages=[{"role": "user", "content": "Hello, DeepSeek V3.1 Maas!"}],
)

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="vertex-ai/deepseek-ai-deepseek-v3-1-maas",
    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 ↗

Explore similar models

DeepSeek V3.1 Maas 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 Maas cost?

Input is priced at $0.600 per 1M tokens and output at $1.70 per 1M tokens (Google Vertex AI, last verified Oct 22, 2026).

What is the context window of DeepSeek V3.1 Maas?

DeepSeek V3.1 Maas supports a 163,840-token context window with up to 32,768 output tokens.

Does DeepSeek V3.1 Maas support function calling?

Yes — DeepSeek V3.1 Maas supports function (tool) calling.

Is DeepSeek V3.1 Maas good for production?

DeepSeek V3.1 Maas is well-suited for multi-step reasoning and analysis tasks and agentic workflows that depend on reliable tool calls. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.

How can I route to DeepSeek V3.1 Maas 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 Maas

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