Is DeepSeek V4 Flash 0731 a chat model?

Yes—DeepSeek V4 Flash 0731 is exposed as a chat/completions-style endpoint on LLM.API.

What modalities does DeepSeek V4 Flash 0731 support?

DeepSeek V4 Flash 0731 accepts text and produces text according to its architecture metadata on LLM.API.

Does DeepSeek V4 Flash 0731 support streaming?

Yes—at least one listed provider advertises streaming.

How is DeepSeek V4 Flash 0731 priced on LLM.API?

Listed pricing metadata shows: In $0.14 / 1M tokens · Out $0.28 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

When should I choose DeepSeek V4 Flash 0731?

Your prompts benefit from the model's family strengths (reasoning, speed, or cost) — especially when you specifically need DeepSeek V4 Flash 0731.

Which providers serve DeepSeek V4 Flash 0731?

LLM.API currently lists: alibaba, canopywave, novita, togetherai. Availability can vary by region and account.

What are limitations of DeepSeek V4 Flash 0731?

Like other API models, DeepSeek V4 Flash 0731 can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

How do I call DeepSeek V4 Flash 0731 via API?

Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "deepseek-v4-flash-0731" and your LLM.API key. See the code snippet on this page.

Which endpoint do I call for DeepSeek V4 Flash 0731?

POST https://api.llmapi.ai/v1/chat/completions with model "deepseek-v4-flash-0731" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to DeepSeek V4 Flash 0731 on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does DeepSeek V4 Flash 0731 support function calling and structured outputs?

Function calling: supported via tools. Structured outputs: supported via response_format. Reasoning effort accepts high, max (default max).

DeepSeek V4 Flash 0731: Self-Hosting & Deployment Guide

Up to 30%

DeepSeek V4 Flash 0731 is available on LLM.API as an OpenAI-compatible chat endpoint—route DeepSeek quality through one key with transparent token pricing.

Start Using API

What is DeepSeek V4 Flash 0731?

On LLM.API, DeepSeek V4 Flash 0731 (deepseek-v4-flash-0731) serves as a DeepSeek conversational model for products that need 1M tokens context windows and predictable token pricing. Official DeepSeek V4 Flash release with enhanced agentic capabilities and integrated DSpark speculative decoding.

How to run DeepSeek V4 Flash 0731 yourself

Open weights (mit), 304.2B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

DeepSeek V4 Flash 0731 VRAM requirements

Precision / quantization	Weights	Figure	Source
BF16 (full precision)	608.4 GB	Estimate	304.2B params × 2 bytes
FP8	307.4 GB	File size	AtlasCloud/DeepSeek-V4-Flash-0731-FP8-DSpark
AWQ 4-bit	108.2 GB	File size	True2456/DeepSeek-V4-Flash-0731-AWQ
MLX 4-bit	154.2 GB	File size	ToPo-ToPo/DeepSeek-V4-Flash-0731-mlx-4bit

File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.

GPU requirements: deploy DeepSeek V4 Flash 0731 on your hardware

Datacenter GPUsApple SiliconCloud instances

Hardware	BF16 (full precision)	FP8	AWQ 4-bit	MLX 4-bit
NVIDIA H200141 GB	5×	3×	1×	2×
NVIDIA H10080 GB	—	5×	2×	3×
NVIDIA A100 80GB80 GB	—	5×	2×	3×
NVIDIA L40S48 GB	—	8×	3×	4×

GPUs needed per variant.

Hardware	MLX 4-bit
Mac, 512 GBM3 Ultra	Fits
Mac, 192 GBM2 Ultra	—
Mac, 128 GBM4 Max / M3 Max	—
Mac, 64 GBM4 Pro / Max	—

Assumes ~75% of unified memory is usable by the GPU.

Hardware	BF16 (full precision)	FP8	AWQ 4-bit	MLX 4-bit
AWS p5.48xlarge8× H100 · 640 GB	—	Fits	Fits	Fits
AWS p4de.24xlarge8× A100 80GB · 640 GB	—	Fits	Fits	Fits
AWS g6e.12xlarge4× L40S · 192 GB	—	—	Fits	Fits
Google Cloud a3-highgpu-8g8× H100 · 640 GB	—	Fits	Fits	Fits
Azure ND H100 v58× H100 · 640 GB	—	Fits	Fits	Fits

Whole-instance GPU memory; public instance specs.

Serve it yourself

vLLMSGLangMLX (Mac)

pip install -U vllm
vllm serve deepseek-ai/DeepSeek-V4-Flash-0731

pip install 'sglang[all]'
python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V4-Flash-0731

pip install -U mlx-lm
mlx_lm.generate --model ToPo-ToPo/DeepSeek-V4-Flash-0731-mlx-4bit --prompt "Hello"

Commands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, up to 30% below list price.

Get API key See API code ↓

Providers

LLM.API routes DeepSeek V4 Flash 0731 to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput

alibaba$0.44 in

$1.32 out

novita$0.44 in

$1.32 out

togetherai$0.44 in

$1.32 out

canopywave$0.44 in

$1.32 out

Provider list prices; the LLM.API discount applies on top.

Provider	Pricing	Context	Capabilities
alibaba30% off	in $0.44; out $1.32 per 1M tokens	1M tokens	tools, streaming, reasoning, web search, JSON, structured
novita	in $0.44; out $1.32 per 1M tokens	1M tokens	tools, streaming, reasoning, web search, JSON, structured
togetherai	in $0.44; out $1.32 per 1M tokens	1M tokens	tools, streaming, reasoning, web search, JSON, structured
canopywave10% off	in $0.44; out $1.32 per 1M tokens	1M tokens	tools, streaming, reasoning, web search, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 11 Oct 2026.

Try this model

Test DeepSeek V4 Flash 0731 right here — free to start.

DeepSeek V4 Flash 0731

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call DeepSeek V4 Flash 0731 through the OpenAI-compatible API — POST /v1/chat/completions.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to deepseek-v4-flash-0731 and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-flash-0731",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ],
  "reasoning_effort": "max"
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="deepseek-v4-flash-0731",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
    reasoning_effort="max",
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "deepseek-v4-flash-0731",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="deepseek-v4-flash-0731",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "model": "deepseek-v4-flash-0731",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ],
  "reasoning_effort": "max"
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID deepseek-v4-flash-0731 · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Supported	`response_format: {"type": "json_object"}`
Structured outputs	Supported	`response_format: {"type": "json_schema", …}`
Streaming	Supported	`stream: true`
Vision (image input)	Not supported	`image_url content parts`
Web search	Supported	`web_search`
Reasoning effort	high · max	`reasoning_effort: "max"` default max

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

DeepSeek V4 Flash 0731 in production

Best workloadsLLM.API features

What the model is good at.

Analytical writing
Produces clear analyses, comparisons, and decision memos from messy source material.
Conversational UX
Maintains coherent multi-turn assistant behavior for product chat surfaces. Tuned to how teams typically call DeepSeek V4 Flash 0731.
Instruction following
Follows detailed system and user instructions with strong adherence to format and tone. Relevant for `deepseek-v4-flash-0731` workloads on LLM.API.
Long-context synthesis
Summarizes and cross-references information across large prompts when context allows. Reflects DeepSeek positioning for this endpoint.
Structured outputs
Can produce JSON-friendly or schema-oriented responses when prompted carefully.

What you get when you call DeepSeek V4 Flash 0731 through LLM.API.

Unified AI Routing
Production: Reach DeepSeek V4 Flash 0731 and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.
Cost Control
Production: Compare provider price points and keep spend visible as you scale DeepSeek V4 Flash 0731.
More performance, less spend.
Reliability Layer
Practical: Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Trace prompts, tokens, and errors for DeepSeek V4 Flash 0731 alongside the rest of your stack.
See every token, everywhere.
Drop-in SDKs
Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.
Model Breadth
Swap DeepSeek V4 Flash 0731 for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.

When to Use — When NOT to Use

Use it if...

Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (DeepSeek V4 Flash 0731)
You want OpenAI-compatible chat completions through a single LLM.API key (DeepSeek V4 Flash 0731)
You need provider failover options exposed for this model id (DeepSeek V4 Flash 0731)
You need a general-purpose text model for assistants, agents, or content workflows (DeepSeek V4 Flash 0731)

Avoid if...

Your use case depends on unpublished proprietary benchmarks not listed here
You require on-prem only deployment with no cloud inference
You need guaranteed real-time hard latency SLAs without benchmarking the provider
You need pure embedding, OCR, or media generation instead of chat

LLM API rating for DeepSeek V4 Flash 0731

★ 3.1/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena + Artificial Analysis

2.4Production adoptionRanked #87 on the public LMArena leaderboard

3.6Benchmark qualityArtificial Analysis Intelligence Index 34.3 · LMArena text rank #87, Elo 1432

4.0Developer ecosystemContext window, tool and JSON support, price against the category median

Reasoning & chat 3.1Coding 3.1Tool & function calling 3.5Long context 3.5

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about DeepSeek models

LMArena#87 · Elo 1432Arena coding#82 · Elo 1457Speed—Sources: LMArena, Artificial Analysis

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to DeepSeek V4 Flash 0731.

Hands-on reviews call the V4 generation the best value on real code — near-frontier results at a fraction of proprietary pricing.
Analysts note the different V4 sizes land close together on third-party evaluations, which raises questions about paying for the largest variant.
Some community testers report the jump felt smaller than the launch discussion suggested, especially outside coding tasks.

SOURCES

Frequently Asked Questions

Is DeepSeek V4 Flash 0731 a chat model?
Yes—DeepSeek V4 Flash 0731 is exposed as a chat/completions-style endpoint on LLM.API.
What modalities does DeepSeek V4 Flash 0731 support?
DeepSeek V4 Flash 0731 accepts text and produces text according to its architecture metadata on LLM.API.
Does DeepSeek V4 Flash 0731 support streaming?
Yes—at least one listed provider advertises streaming.
How is DeepSeek V4 Flash 0731 priced on LLM.API?
Listed pricing metadata shows: In $0.14 / 1M tokens · Out $0.28 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.
When should I choose DeepSeek V4 Flash 0731?
Your prompts benefit from the model's family strengths (reasoning, speed, or cost) — especially when you specifically need DeepSeek V4 Flash 0731.
Which providers serve DeepSeek V4 Flash 0731?
LLM.API currently lists: alibaba, canopywave, novita, togetherai. Availability can vary by region and account.
What are limitations of DeepSeek V4 Flash 0731?
Like other API models, DeepSeek V4 Flash 0731 can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
How do I call DeepSeek V4 Flash 0731 via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "deepseek-v4-flash-0731" and your LLM.API key. See the code snippet on this page.

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DeepSeek V4 Flash 0731: Self-Hosting & Deployment Guide

What is DeepSeek V4 Flash 0731?

How to run DeepSeek V4 Flash 0731 yourself

DeepSeek V4 Flash 0731 VRAM requirements

GPU requirements: deploy DeepSeek V4 Flash 0731 on your hardware

Serve it yourself

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

DeepSeek V4 Flash 0731 in production

Analytical writing

Conversational UX

Instruction following

Long-context synthesis

Structured outputs

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth

When to Use — When NOT to Use

Use it if...

Avoid if...

LLM API rating for DeepSeek V4 Flash 0731

What developers say about DeepSeek models

Frequently Asked Questions

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