LLMdeepseek

DeepSeek: DeepSeek V4 Flash (free)

DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and...

Anyone in the Space can @-mention DeepSeek: DeepSeek V4 Flash (free) with the team's shared context — pooled credits, one chat, one memory.

All models

Starter is free forever — 1 Space, 100 credits/month, 1 MCP. No card.

Verdict

DeepSeek V4 Flash delivers surprisingly capable reasoning and code generation at zero cost, making it the obvious choice for high-volume experimentation and prototyping. The 256K context window handles substantial documents without truncation. Trade-off: response quality lags behind paid frontier models on nuanced tasks, and you're subject to rate limits that can throttle production workloads. Reach for this when budget constraints matter more than peak performance, or when you need to burn through thousands of test queries without opening your wallet.

Best for

High-volume prototyping and experimentation
Cost-sensitive code completion tasks
Long-context document summarization
Educational and learning projects
Batch processing with flexible timelines

Strengths

The 256K context window puts it ahead of many paid models for document-heavy workflows—you can drop entire codebases or research papers without chunking. Zero pricing removes friction for exploratory work: spin up dozens of variants, A/B test prompts aggressively, or let junior developers learn prompt engineering without budget anxiety. Code generation quality exceeds what you'd expect from a free tier, handling standard library tasks and boilerplate with minimal hallucination.

Trade-offs

Response quality trails Claude Sonnet 4.5 and GPT-4 on tasks requiring subtle reasoning or domain expertise—expect more back-and-forth to nail complex instructions. Rate limits aren't published but will constrain production use cases that need guaranteed throughput. Lack of public benchmark data makes it harder to predict performance on your specific workload compared to models with extensive MMLU or HumanEval scores. The proprietary license means no self-hosting or fine-tuning options.

Specifications

Provider: deepseek
Category: llm
Context length: 256,000 tokens
Max output: 256,000 tokens
Modalities: text
License: proprietary
Released: 2026-04-24

Pricing

Input: $0.00/Mtok
Output: $0.00/Mtok
Model ID: deepseek/deepseek-v4-flash:free

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool — one plan, one balance for everyone.

Team cost calculator

Seats5 peopleMessages / seat / day80Avg turn size2 ktokOutput share30 %

Estimated monthly spend

Freeno token cost

17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool — one plan, one balance for everyone.

Providers

Provider-level routing data is not available yet for this model.

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Spaces have used this model yet to share anonymised team stats. We wait for at least 50 distinct Spaces per week before publishing any aggregate.

Starter prompts

Refactor Legacy Code

Review this code and suggest refactoring opportunities that improve readability without changing behavior. Prioritize extracting repeated logic and clarifying variable names.

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Summarize Research Papers

Summarize this research paper in 200 words. Focus on the core hypothesis, methodology, and main findings. Write for a technical audience unfamiliar with the specific subfield.

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Generate Test Cases

Write pytest test cases for this function. Include happy path, edge cases with empty inputs, and at least one test for invalid argument types. Use descriptive test names.

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Draft API Documentation

Write API documentation for this endpoint. Include a brief description, parameter definitions, example request/response, and a common errors section. Assume readers are familiar with REST conventions.

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Explain Complex Concepts

Explain this concept in three versions: one for a beginner with no background, one for an intermediate practitioner, and one for an expert. Keep each version under 100 words.

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