DeepSeek V4 Flash
Azure AI Foundry chatDeepSeek V4 Flash is an Azure AI Foundry chat model.It supports a 1,000,000-token context windowwith up to 384,000 output tokens.Input is priced at $0.190/M tokens and output at $0.510/M tokens. Capabilities include function calling, reasoning. Route DeepSeek V4 Flash via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate DeepSeek V4 Flash spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.1900/M input · $0.5100/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 Aug 6, 2026.
| Input | $0.190/M | |
| Output | $0.510/M | |
| Blended (3:1 in:out) | $0.270/M Cross-model comparable price |
Context & limits
- Context window
- 1,000,000 tokens
- Max input
- 1,000,000 tokens
- Max output
- 384,000 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 — not advertised
- Reasoning ✓ supported
Strengths & caveats — when to pick DeepSeek V4 Flash
Data-driven from DeepSeek V4 Flash's percentile within chat peers.
Where it's strong
- +long-context tasks — context window in the top 10% of peers
- +long-form generation — 384,000-token max output, top-2% of peers
Watch out for
- !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 V4 Flash 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 V4 Flash 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-v4-flash",
messages=[{"role": "user", "content": "Hello, DeepSeek V4 Flash!"}],
)
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-v4-flash",
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-v4-flash
provider: azure-ai-foundry
weight: 80
fallbacks:
- model: qwen3-8-max
provider: dashscope
- model: gemini-3-1-pro-preview
provider: google
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true } Same model on other providers
deepseek-v4-flash is also available via 2 other routes. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| Fireworks AI | $0.140/M | $0.280/M | Aug 6, 2026 |
| DeepSeek | $0.140/M | $0.280/M | Aug 6, 2026 |
Compare with similar models
Grouped by Chatbot Arena tier (DeepSeek V4 Flash sits at 1436 ELO).
- Claude Opus 4.7Azure AI Foundry · $5.00/M in · $25.00/M out · 1,000,000 ctx
- Claude Opus 4.6Azure AI Foundry · $5.00/M in · $25.00/M out · 1,000,000 ctx
- GPT 5.4Azure AI Foundry · $2.50/M in · $15.00/M out · 1,050,000 ctx
- Claude Sonnet 4.6Azure AI Foundry · $3.00/M in · $15.00/M out · 1,000,000 ctx
FAQ
How much does DeepSeek V4 Flash cost?
Input is priced at $0.190 per 1M tokens and output at $0.510 per 1M tokens (Azure AI Foundry, last verified Aug 6, 2026).
What is the context window of DeepSeek V4 Flash?
DeepSeek V4 Flash supports a 1,000,000-token context window with up to 384,000 output tokens.
Does DeepSeek V4 Flash support function calling?
Yes — DeepSeek V4 Flash supports function (tool) calling.
Is DeepSeek V4 Flash good for production?
DeepSeek V4 Flash is well-suited for long-context tasks — context window in the top 10% of peers and long-form generation — 384,000-token max output, top-2% of peers. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.
How can I route to DeepSeek V4 Flash 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 Flash
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.