Qwen-Image-3.0
Freemium · Open source
Safety guardrails in AI systems
DeepSeek V3
FreemiumOpen SourceAPISelf-Hosted
DeepSeek V3 delivers coding and math performance that rivals GPT-4o and Sonnet on public benchmarks at a fraction of their cost—but the gap between lab numbers and production reality is wider than the benchmark tables suggest. The real issues surface once you start shipping: capacity constraints that throttle during demand spikes, built-in censorship that persists even on self-hosted versions, and tool-use features that don't work as advertised on some inference platforms.
$0.14 per million input tokens and $0.28 per million output tokens · Free tier: Open-source MIT-licensed weights available for free deployment on users' own infrastructure with zero API charges
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Get This Tool
License: MIT Any use incl. commercial
Local-run terms: DeepSeek released V3 under an MIT license and published complete model weights on Hugging Face, allowing organizations to download, modify, and deploy the model for commercial purposes without restriction.
Screenshots 1
Summary
DeepSeek-V3 stands as the best-performing open-source model and exhibits competitive performance against frontier closed-source models. The 671B-parameter MoE architecture with 37B activated per token uses Multi-head Latent Attention and DeepSeekMoE for efficient inference and cost-effective training. In private benchmarks, DeepSeek v3 0324 ranks as the best non-reasoning model, even beating Claude 3.5 Sonnet. The architecture benefits from the latest training innovations and is the more future-proof option. For code generation, DeepSeek V3 is 29x cheaper than GPT-4o on per-token costs. Production constraints emerge at scale: the model refuses to answer roughly 85% of questions about politically sensitive topics due to Chinese regulatory requirements, self-hosting does not remove this censorship, only privacy. During demand spikes, DeepSeek suspended new account signups and halted API credit top-ups due to server capacity strain. Occasional insertion of Chinese text in code outputs disrupts workflows even at low temperature settings.
Bottom line: *Pick V3 if you need cheap, competitive coding and math for a team building internal tools where censorship won't surface. Switch away when you hit infrastructure limits, need agentic tool-calling at scale, or your workflow touches geopolitically sensitive content.*
Hosted & API Pricing
The model is free to self-host. These are the creator's hosted/API options.DeepSeek Official API
via DeepSeek
Variable
Official API pricing with input at $0.14/M tokens and output at $0.28/M tokens
- Per-token billing
- 128K context window
- Supports streaming
- Available on web, app, and API
OpenRouter
via OpenRouter
Variable
$0.252 per million input tokens, $0.378 per million output tokens with 131,072 token context window
- Multi-provider aggregation
- Higher uptime with multiple providers
- 131K context support
Pricing may have changed since last verified. Check the official site for current plans.
Pricing Plans
Per-token- Price
- $0.14 per million input tokens and $0.28 per million output tokens
- Free Tier
- Open-source MIT-licensed weights available for free deployment on users' own infrastructure with zero API charges
Pay-as-you-go API
Variableper month
Token-based pricing for API access
- Per-token billing
- No minimum spend
- Supports function calling (V3-0324 variant)
- 128K context window
Self-hosted (Free)
Free
Local deployment using open-source weights under MIT License
- No API costs
- Full model control
- Privacy-preserving
- Can be fine-tuned for custom use cases
View full pricing on deepseek.com →
Pricing may have changed since last verified. Check the official site for current plans.
LLM Spec Sheet
Benchmarks
131.1k tokensContext Window
Pricing & Limits
- Input price
- $0.28 / 1M tokens
- Output price
- $0.42 / 1M tokens
- Max output tokens
- 8,192
Metrics from vendor, updated .
Find your fit
Before you choose DeepSeek V3
More options in Open-Source LLMs. Compare pricing, platforms, and features side by side.
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Changelog
Pros
- Cost-effective at $0.27 per million input tokens and $1.10 per million output tokens
- Fast throughput at approximately 60 tokens per second, 3x faster than DeepSeek-V2
- Fully open-source weights available under MIT License for local deployment
- Performance comparable to GPT-4 and Claude 3.5 Sonnet
- Outperforms other open-source models across multiple benchmarks
Cons
- Context window significantly smaller than some competitors
- Does not support tool calling (functions)
- Does not support vision capabilities
About
- Platforms
- Hugging Face, GitHub, DeepSeek API, multiple cloud providers (Cerebras, DeepInfra, Together, OpenRouter, Fireworks, Hyperbolic, SambaNova)
- Languages
- Supports multiple languagesallowing input and output in several languages
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- May 15, 2026
Best For
Who it's for
- Cost-sensitive organizations requiring frontier-class performance
- Teams needing local deployment for data privacy
- Developers building with coding-heavy applications
- Mathematical and scientific problem-solving tasks
- High-volume inference workloads
What it does well
- Code generation and debugging
- Mathematical reasoning and problem-solving
- Multi-language text processing and translation
- Long-document analysis and retrieval-augmented generation
- General-purpose chat and instruction following
Integrations
Compare DeepSeek V3
Frequently Asked Questions
- Is DeepSeek V3 free?
- DeepSeek V3 offers a free option alongside paid upgrades. Pricing: $0.14 per million input tokens and $0.28 per million output tokens. Check the current allowances and terms before choosing a plan.
- Is DeepSeek V3 open source?
- Yes. DeepSeek V3 is open source — the source repository is at https://github.com/deepseek-ai/DeepSeek-V3.
- Does DeepSeek V3 have an API?
- Yes. DeepSeek V3 exposes a developer API. See the official documentation at https://deepseek.com for details.
- Can I self-host DeepSeek V3?
- Yes. DeepSeek V3 supports self-hosting on your own infrastructure.
- When was DeepSeek V3 released?
- DeepSeek V3 was first released in 2024.
- What platforms does DeepSeek V3 support?
- DeepSeek V3 is available on: Hugging Face, GitHub, DeepSeek API, multiple cloud providers (Cerebras, DeepInfra, Together, OpenRouter, Fireworks, Hyperbolic, SambaNova).
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DeepSeek-V3 is a Mixture-of-Experts language model with 671B total parameters with 37B activated for each token, adopting Multi-head Latent Attention (MLA) and DeepSeekMoE architectures. The model pioneers an auxiliary-loss-free strategy for load balancing with a multi-token prediction training objective, pre-trained on 14.8 trillion diverse and high-quality tokens followed by Supervised Fine-Tuning and Reinforcement Learning stages. DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models, despite requiring only 2.788M H800 GPU hours for its full training. The MIT-licensed open-source model can be deployed on users’ own infrastructure with zero API charges, with costs limited to compute and storage.
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