DeepSeek V4 Flash: Specs, Parameters, and Modelflare Setup

A practical guide to DeepSeek-V4-Flash-0731: its 284B/13B MoE design, 1M-token context, thinking controls, current Modelflare prices, and Chat Completions and Responses configuration.

On July 31, 2026, DeepSeek updated deepseek-v4-flash to the public-beta API release DeepSeek-V4-Flash-0731. The model ID did not change. DeepSeek says the update keeps the preview model's architecture and size, while new post-training materially improves coding-agent and tool-use performance and adds native Responses API support.

V4 Flash targets text workloads that need reasoning, long context, throughput, and a lower cost profile than V4 Pro. This guide separates three things that are easy to blur together: specifications published by DeepSeek, protocols and prices currently exposed by Modelflare, and behaviors that still need verification on your own workload.

Core specifications

Item DeepSeek V4 Flash
Modelflare model ID deepseek-v4-flash
Current official API version DeepSeek-V4-Flash-0731
Architecture Mixture-of-Experts (MoE)
Total parameters 284B
Activated parameters per token 13B
Context length 1M tokens
Maximum output 384K tokens
Thinking mode Enabled by default; default effort is high
Reasoning effort low, high, or max; xhigh currently maps to high
Official API features JSON Output, Tool Calls, Responses API, Chat Prefix Completion; FIM only in non-thinking mode
Primary input and output Text input and text output; do not infer vision support

The useful combination is not merely 284B total parameters, but 13B activated parameters with a 1M-token context window. MoE routing activates only part of the model for each token, aiming to retain model capacity while controlling inference cost. DeepSeek's technical report also describes attention changes designed for long-context efficiency. Whether that becomes lower latency in practice still depends on prompt length, reasoning effort, upstream load, and output size.

What changed in the 0731 update

DeepSeek describes 0731 as a new post-training release with no architecture or size change. Its published agent results include 82.7 on Terminal Bench 2.1 and 70.3 on Toolathlon Verified, and the company says the agent capability now exceeds the earlier V4 Pro Preview by a wide margin.

Treat those numbers as release-direction evidence, not as your production SLA. DeepSeek's public code-agent evaluations used a particular harness, max effort, top_p=0.95, and temperature=1.0; some reported sets are internal. A useful evaluation should pin the model ID, client, tools, timeouts, and task corpus, then record success rate, time to first output, total latency, token use, and repair turns.

Choosing thinking parameters

V4 Flash enables thinking by default at high effort. A practical starting policy is:

  • Summarization, classification, and formatting: start with low and measure quality;
  • Code changes, complex analysis, and multi-step tools: start with high;
  • A small number of difficult tasks: evaluate max, with explicit latency and reasoning-token budgets;
  • Fast direct answers: set thinking.type=disabled instead of relying on the retired deepseek-chat alias.

In thinking mode, temperature, top_p, presence_penalty, and frequency_penalty have no effect even if they are accepted. Tool-using multi-turn conversations must also preserve and replay reasoning_content within the tool-call turn; omitting it can produce an upstream 400. If a client cannot preserve that field correctly, validate the basic non-tool route first.

Current Modelflare availability

As of August 4, 2026, Modelflare lists deepseek-v4-flash for Chat Completions and Responses. DeepSeek also offers an Anthropic-compatible API upstream, but that does not mean Modelflare currently exposes the same route for this model. Use Models & Pricing as the live protocol source of truth.

The current public price snapshot is below, in USD per 1M tokens:

Available group Input Output Cached input Note
deepseek-award $0.105 $0.21 $0.0021 Available only to API keys eligible for this group
deepseek-stable $0.15 $0.30 $0.003 Stable group

Prices, group eligibility, and supported protocols can change. Do not hard-code this snapshot as a permanent billing contract. Recheck the live catalog before launch and verify the actual group, tokens, and charge in usage logs.

Chat Completions setup

Store the Modelflare API key in an environment variable:

export MODELFLARE_API_KEY='YOUR_MODELFLARE_API_KEY'

Then call the standard public base URL:

curl -N -sS https://modelflare.dev/v1/chat/completions \
  -H "Authorization: Bearer $MODELFLARE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-flash",
    "messages": [
      {"role": "system", "content": "You are a careful coding assistant."},
      {"role": "user", "content": "Review this migration plan and list the rollback risks."}
    ],
    "thinking": {"type": "enabled"},
    "reasoning_effort": "high",
    "stream": true
  }'

If an OpenAI SDK does not expose thinking directly, pass it through the SDK's extra_body. Provider-specific fields should remain raw JSON; do not rename them or drop an explicit false value.

Responses API setup

V4 Flash is also available through Modelflare's Responses endpoint:

curl -N -sS https://modelflare.dev/v1/responses \
  -H "Authorization: Bearer $MODELFLARE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-flash",
    "input": "Inspect the proposed deployment sequence and identify unsafe assumptions.",
    "reasoning": {"effort": "high"},
    "stream": true
  }'

Chat Completions and Responses are different wire contracts. A model appearing on both endpoints does not prove identical event types, tool state, error shapes, or continuation behavior. Validate non-streaming, streaming, and tool calls separately before production use.

  1. Create a separate Modelflare API key for each application and select a primary group that actually contains deepseek-v4-flash;
  2. Add an ordered fallback only when it supports the same model and protocol;
  3. Start ordinary text jobs at low or in non-thinking mode, and start complex agent jobs at high;
  4. Set client timeouts for long reasoning and long output, not for a single short health prompt;
  5. For tools, preserve the complete assistant message, especially reasoning_content and tool_calls;
  6. Before launch, check success rate, first output, total latency, output tokens, selected group, and per-request cost on real tasks.

Where it fits

V4 Flash is a strong candidate for frequent coding assistance, long-document analysis, tool-using agents, batch text processing, and applications balancing capability against cost. Compare V4 Pro or another flagship on the hardest knowledge-heavy and long-horizon tasks. For image input, select a model whose multimodal contract has been explicitly published and verified on Modelflare.

Sources and freshness

This article was verified on August 4, 2026. Model snapshots, prices, and protocol support can change; production configuration should use the current official documentation and Modelflare catalog.