DeepSeek V4 Pro

W&B Inference chat Deprecated 30d ago
Heads-up: W&B Inference has scheduled DeepSeek V4 Pro for deprecation on Oct 5, 2026. Plan a migration. Use Agent Command Center's model fallback routing to swap models without code changes.

DeepSeek V4 Pro is a W&B Inference chat model.It supports a 1,049,000-token context window.Input is priced at $1.15/M tokens and output at $2.55/M tokens. Capabilities include reasoning, prompt caching. Route DeepSeek V4 Pro 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 V4 Pro spend

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

~3K in / ~400 out · 5K req/day
3,000
01,049,000
400
016,000
5,000
01,000,000
cached @ $0.2000/M
Per request
$0.004470
in $0.003450 · out $0.001020
Per day
$22.35
5,000 requests
Per month
$680
152,188 requests

Estimate uses $1.15/M input · $2.55/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 $1.15/M
Output $2.55/M
Blended (3:1 in:out) $1.50/M
Cross-model comparable price
Cached input $0.200/M

Context & limits

Context window
1,049,000 tokens
Max input
1,049,000 tokens
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 ✓ supported
  • Reasoning ✓ supported

Strengths & caveats — when to pick DeepSeek V4 Pro

Data-driven from DeepSeek V4 Pro's percentile within chat peers.

Where it's strong

  • +long-context tasks — context window in the top 2% of peers

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 30 days ago

Benchmarks pending

We haven't logged public benchmark scores for DeepSeek V4 Pro yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call DeepSeek V4 Pro 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 V4 Pro 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="wandb-inference/deepseek-ai-deepseek-v4-pro",
    messages=[{"role": "user", "content": "Hello, DeepSeek V4 Pro!"}],
)

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="wandb-inference/deepseek-ai-deepseek-v4-pro",
    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-ai-deepseek-v4-pro is also available via 2 other routes. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
Nebius$1.75/M$3.50/MOct 22, 2026
DeepInfra$1.30/M$2.60/MOct 22, 2026

Explore similar models

DeepSeek V4 Pro 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 V4 Pro cost?

Input is priced at $1.15 per 1M tokens and output at $2.55 per 1M tokens (W&B Inference, last verified Oct 22, 2026).

What is the context window of DeepSeek V4 Pro?

DeepSeek V4 Pro supports a 1,049,000-token context window.

Does DeepSeek V4 Pro support function calling?

DeepSeek V4 Pro 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 V4 Pro good for production?

DeepSeek V4 Pro is well-suited for long-context tasks — context window in the top 2% of peers. Consider alternatives if you need agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback.

How can I route to DeepSeek V4 Pro 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 V4 Pro

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