deepseek-flash / Chat
Sparse MoE model (552B total) built on DeepSeek's Causal Encoder-Decoder architecture, activating 8B parameters on input and 16B on output. Native multimodal vision understanding trained jointly from pre-training. 1M context window with compressed KV cache (4x reduction). Excels at coding, terminal, and computer-use agents—scores 90.6 on Terminal-Bench 2.1 and 74.2 on DeepSWE v1.1, surpassing V4-Pro
Playground Chat
Inicie uma conversa com o modelo de IA. Você pode perguntar qualquer coisa.
As respostas geradas por IA podem variar em precisão.
DeepSeek FlashFast Thinking with Reasoning Effort Control
DeepSeek Flash is a low-latency reasoning chat model with thinking.type toggle, high / max reasoning_effort, optional logprobs, JSON mode, and function tools — analytical depth that still feels interactive.
At a Glance
Flash Reasoning Stack
Analytical depth at interactive latency, with evaluation signals built in.
High or Max Effort
reasoning_effort selects high (default) or max depth. Low and medium map to high; xhigh maps to max for compatibility with existing agent stacks.
Thinking Toggle
thinking.type switches between enabled (default) and disabled depending on task complexity — skip the chain when the answer is obvious.
Logprob Evaluation
logprobs and top_logprobs (0–20) expose token probability signals for confidence scoring, regression tests, and eval harnesses.
JSON & Tool Surface
response_format.type text or json_object, function tools with tool_choice none / auto / required, and optional stream_options.include_usage for metering.
How It Works
Fast deep analysis in four steps.
Set Thinking & Effort
Enable thinking and choose high or max reasoning_effort for the task.
Stream or Wait
stream defaults to false; enable SSE when you need progressive paint.
Collect Logprobs
Turn on logprobs / top_logprobs for confidence scoring and evals.
Shape Output
Use response_format text or json_object, stop lists (≤16), and sampling controls.
DeepSeek Flash Domains
Where analytical depth must stay interactive.
Live Analysis Chat
Optional streaming answers for dashboards and ops consoles.
JSON Products
response_format json_object for structured payloads.
Eval Pipelines
Logprobs for confidence and regression tests.
Agent Decisions
Function tools with none / auto / required choice.
Prompt Tips
Get more from DeepSeek Flash reasoning.
High and Max effort spend tokens more usefully when you request proof paths.
Reserve reasoning_effort max for hard proofs; high covers most product work.
Pass user_id for cache isolation and safer multi-tenant routing — never put PII in it.
DeepSeek Flash Quickstart
High / max reasoning with optional logprobs.
curl -X POST "https://api.powertokens.ai/v1/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-flash",
"messages": [
{"role": "user", "content": "Walk through a careful proof of the AM-GM inequality for two variables."}
],
"stream": true,
"temperature": 0.7
}'Technical Specifications
Confirmed parameters and runtime execution protocols.
Detalhes de preços
A cobrança real deste modelo é calculada dinamicamente com base nos parâmetros específicos da sua solicitação de API. Abaixo estão as combinações específicas e seus preços correspondentes:
- Para solicitações feitas em dias úteis (seg-sex) durante 09:00 - 12:00, 14:00 - 18:00 (UTC+8), um multiplicador de preço de 2x é aplicado.
| Modalidade | Créditos de entrada | Créditos de saída | Preço de entrada | Preço de saída | Cache implícito | Cache explícito | Criação de cache |
|---|---|---|---|---|---|---|---|
| Standard | 150/ 1M Tokens | 600/ 1M Tokens | $0.150 | $0.600 | $0.003 3/ 1M Tokens | $0.003 3/ 1M Tokens | -- |
Models from the Same Channel
Explore complementary models and alternative versions from the same provider channel.
Recommended Related Models
Explore complementary video and multimodal models with your unified API key.
GLM-5.3
glm-5.3
Zhipu AI's flagship text model optimized for complex software engineering and long-horizon Agent tasks. Features a 1M-token context window with mandatory reasoning (3 levels: low/high/max). Coding capability improved 50% over GLM-5.2 on Z.ai Code Bench; scores SOTA on Terminal Bench 3.0. Excels in cybersecurity tasks (cyber vulnerability discovery)
GLM-5.3 Flash
glm-5.3-flash
Z.ai's native multimodal model (320B-A18B) with text, image, video, and file input. Features hybrid sparse + linear attention, reducing attention compute 3x and KV cache 4.4x vs. GLM-5.3. Scores 57 on Artificial Analysis Intelligence Index—matching Claude Opus 4.8—with 1M context and 128K max output. Excels in agentic coding, vision-based Computer Use, and CAD reproduction
Qwen3.8 Flash
qwen3.8-flash
Alibaba's cost-efficient multimodal reasoning model. Supports text, image, and video inputs with text output. Features a native 1M-token context window for long documents, codebases, and agentic workflows. Excels in coding assistance, desktop interaction, chart analysis, and long-video understanding. Compatible with OpenAI/Anthropic protocols for seamless integration
GLM-5.2
glm-5.2
Flagship text model purpose-built for long-horizon agentic workflows. Features a 1M context window supporting project-level engineering in a single session. Excels at autonomous coding: can complete development, testing, and multi-platform deployment from a single prompt. Top open-weight model per Artificial Analysis; #1 globally on Code Arena. MIT-licensed and Day-0 optimized for domestic AI chips
Frequently Asked Questions
Everything you need to know before integrating this model.
Start Building with DeepSeek Flash Today
Create an account in seconds to receive 100 free credits and start generating immediately. No credit card or upfront contract required.