DeepSeek released V4 on June 10, 2026. The model is not a research preview. The weights are live on Hugging Face and the code is on GitHub. DeepSeek V4 is a Mixture-of-Experts model with 1.6 trillion total parameters and 38 billion active parameters. It supports a 256,000 token context window. The release includes bf16 and fp8 weights, tokenizer, configs, and an eval harness. The license is the DeepSeek Open License 2.0, which allows commercial use and fine-tuning. This launch matters because it puts a GPT-5.5 class model into local, private, and free deployment. It arrives after a rough June for free tier users across AI Free Tier limits.
The benchmark gap is small. DeepSeek V4 scores 92.4 on MMLU-Pro, 81.2 on GPQA Diamond, 96.5 on HumanEval, and 95.1 on MATH. GPT-5.5 scores 93.1 on MMLU-Pro, 82.7 on GPQA Diamond, 97.0 on HumanEval, and 96.3 on MATH. The difference is often less than two points. For many real coding, math, and reasoning tasks, the open model is within striking range. DeepSeek also released a 110B dense teacher model and training code. Day one support includes vLLM, SGLang, and llama.cpp. That matters after June pricing changes in AI coding tools pricing.
License implications are the main story. DeepSeek Open License 2.0 does not charge for weights. You can deploy, fine-tune, distill, and serve the model. There is one major condition. If a product exceeds 50 million monthly active users, the operator must request a separate commercial grant. The clause is unusual but does not block most startups, researchers, or internal enterprise teams. There are no telemetry calls in the inference code. No API logs leave your network. This is not true for GPT-5.5, which remains closed behind OpenAI API and ChatGPT. For a wider view of no cost models, see best free AI models 2026.
Cost and hardware require honesty. You can run the full fp8 model on 8x80GB GPUs. That is a serious capital expense. Quantized Q4_K_M versions can fit on 160GB to 320GB total VRAM. Consumer cards under 48GB are not practical for full context. The official DeepSeek API costs about $0.14 per million input tokens and $0.28 per million output tokens. GPT-5.5 API costs $2.50 input and $10.00 output. The open model wins on cost after hardware is paid off. It loses on setup effort and hardware access. This choice follows the June 2026 shift in AI API free tier policy.
How Do the Top Options Compare?
| Model | Type | Parameters | Context Window | License | Input / Output per 1M Tokens | Best For |
|---|---|---|---|---|---|---|
| DeepSeek V4 Self-Hosted | Open weights, MoE | 1.6T total, 38B active | 256K tokens | DeepSeek Open License 2.0 | $0 per token after hardware | Private and fine-tuned inference |
| DeepSeek V4 Official API | Hosted open model | 1.6T total, 38B active | 256K, 128K free tier | DeepSeek Open License 2.0 | $0.14 input / $0.28 output | Low-cost hosted inference |
| GPT-5.5 API | Closed API | Undisclosed | 256K tokens | Proprietary | $2.50 input / $10.00 output | Highest accuracy managed API |
| GPT-5.5 ChatGPT | Closed chat product | Undisclosed | 128K free tier | Proprietary | Subscription from $20/mo | Individual convenience |
Prices are list rates as of June 2026 and do not include hardware, power, engineering, or negotiated discounts. DeepSeek V4 self-hosted inferencing has GPU memory costs. GPT-5.5 parameter count and architecture have not been published.
1. DeepSeek V4 Open Weights (Self-Hosted) , Best for teams that need full data control and no token fees
DeepSeek V4 is a 1.6T parameter Mixture-of-Experts model with 38B active parameters. It was released June 10, 2026 under the DeepSeek Open License 2.0. The weights are available on Hugging Face and the training and inference code is on GitHub. You can download bf16, fp8, AWQ, and GPTQ formats. The full bf16 model uses about 640GB of GPU memory. That is too large for any single widely available GPU. The fp8 version needs around 320GB. A 4-bit quantized version can run on 160GB, which is four 40GB A100s or two 80GB H100s. This is not a hobbyist download for a laptop, but it is workable for a well equipped team.
The context window is 256K tokens. That allows long document summarization, codebase analysis, and large agent workloads. The model supports function calling, JSON schema output, and streaming. You can serve it with vLLM or SGLang. If you need local inference on smaller hardware, llama.cpp supports quantized GGUF. The open weights mean no per-token billing. However, you pay for GPUs, electricity, cooling, and engineering time. The self-hosted path gives full data control and no third-party log access. That is valuable after recent Claude free tier changes.
Key strengths:
- ✅ Full weight access for fine-tuning, merging, and distillation
- ✅ 256K context window with long-document support
- ✅ No per-token API fees after infrastructure is acquired
- ✅ Commercial use allowed under DeepSeek Open License 2.0
- ✅ Benchmarks within 1 to 3 points of GPT-5.5 on core evals
- ❌ Full precision requires about 640GB of GPU memory
- ❌ Quantized inference still demands 160GB to 320GB of VRAM
- ❌ You maintain the whole serving stack and uptime
Who it’s for: Teams that own GPU capacity and need private, customizable inference without token fees.
2. DeepSeek V4 Official API , Best for low-cost hosted access to the open model
DeepSeek also launched a hosted API for V4 on June 10, 2026. The API uses the same open weights but runs on DeepSeek infrastructure. You send prompts over HTTPS and receive generated tokens. Input costs $0.14 per million tokens. Output costs $0.28 per million tokens. That is roughly 94 percent cheaper than GPT-5.5 API input and 97 percent cheaper than output. A new free tier includes 1 million tokens per day for the first 30 days. After that, you pay as you go with no monthly minimum. The hosted API avoids GPU procurement, but it sends data to DeepSeek servers.
The API has native OpenAI-compatible endpoints. You can switch from GPT-5.5 by changing your base URL and model name. Function calling, JSON mode, and streaming work. The free tier caps context at 128K tokens. Paid tiers unlock the full 256K. Rate limits are lower than OpenAI on free accounts, but paying customers can request higher throughput. This choice is best for developers who want the open model without managing hardware. For context on free API shifts, see AI API free tiers limits 2026.
Key strengths:
- ✅ Hosted inference with no GPU procurement or maintenance
- ✅ About 94 percent cheaper input and 97 percent cheaper output than GPT-5.5 API
- ✅ OpenAI-compatible endpoints reduce integration work
- ✅ Free tier offers 1 million tokens per day for 30 days
- ✅ Full 256K context available on paid tiers
- ❌ Prompts and outputs route through DeepSeek servers
- ❌ Free tier context is capped at 128K tokens
- ❌ Support is limited compared with enterprise OpenAI plans
Who it’s for: Developers who want low-cost hosted access to V4 without buying or renting GPUs.
3. GPT-5.5 API , Best for top benchmark performance and managed tooling
GPT-5.5 is OpenAI’s closed flagship model. It launched June 3, 2026 with no public weights. The API is the only way to access the model outside ChatGPT. Input costs $2.50 per million tokens and output costs $10.00 per million tokens. It supports a 256K context window, multimodal inputs, function calling, and parallel tool calls. OpenAI has not published parameter count or architecture details. The model is available through OpenAI API and Azure OpenAI Service.
GPT-5.5 scores slightly higher than DeepSeek V4 on public benchmarks. It reaches 93.1 on MMLU-Pro, 82.7 on GPQA Diamond, 97.0 on HumanEval, and 96.3 on MATH. That is a real but modest lead. The bigger advantage is ecosystem maturity. Enterprise SLAs, fine-tuning API, structured outputs, and large rate limits are built in. The downside is cost and lock-in. You cannot download, inspect, or self-host the weights. Free tier access is minimal. Developers worried about vendor pricing shifts should check AI subscription tiers compared.
Key strengths:
- ✅ Highest benchmark scores in this comparison
- ✅ Managed API with enterprise SLAs and high rate limits
- ✅ Multimodal support includes image, audio, and video inputs
- ✅ Fine-tuning and structured outputs are available as managed features
- ✅ Large ecosystem of SDKs and third-party integrations
- ❌ Per-token pricing is over 10 times DeepSeek V4 API
- ❌ No public weights, architecture details, or self-hosted option
- ❌ Free tier access is capped and can change quickly
Who it’s for: Teams that need top benchmark performance and turnkey enterprise features despite high cost.
4. GPT-5.5 in ChatGPT (Free and Paid) , Best for individual users who want chat access without deployment
ChatGPT remains the consumer front end for GPT-5.5. Free users get limited access, while paid plans start at $20 per month. The free tier includes text chat with GPT-5.5 but with rate resets and occasional ads. Paid tiers remove ads and increase message limits. ChatGPT includes browsing, code interpreter, memory, and image generation. It does not expose weights or raw token pricing. Context may be lower in free tiers. OpenAI changed several free tier rules in June 2026.
Compared with DeepSeek V4, ChatGPT is easier to start. You do not need GPUs or code. You open a browser or mobile app. The tradeoff is less control. You cannot fine-tune GPT-5.5. You cannot serve it in your own datacenter. You cannot inspect the license beyond OpenAI’s terms. The subscription fee may not remove all feature gates. For many users, the convenience is worth it. For developers and tinkerers, open V4 is more attractive.
Key strengths:
- ✅ Zero setup web and mobile access
- ✅ Paid plans include higher limits and remove most ads
- ✅ Native tools include browsing, code execution, and memory
- ✅ Multimodal chat works across text, image, voice, and video
- ❌ Free tier limits are tighter and can change without notice
- ❌ Subscription does not grant weight access or self-hosting rights
- ❌ Token pricing is opaque inside the ChatGPT product
Who it’s for: Individual users who want a ready chat experience and do not need model customization.
Frequently Asked Questions
Is DeepSeek V4 actually free to use?
The weights and code are free to download under the DeepSeek Open License 2.0. Commercial use, fine-tuning, and private deployment are allowed. A separate grant is required only if a product exceeds 50 million monthly active users. You still need GPUs or pay for API usage.
Can I run DeepSeek V4 on a single consumer GPU?
Not at full precision. The full model needs about 640GB of GPU memory. Quantized versions need 160GB to 320GB total VRAM. A single 24GB or 48GB consumer card is not practical for full context. Smaller distilled versions may be released later.
How close is DeepSeek V4 to GPT-5.5 on benchmarks?
DeepSeek V4 scores 92.4 on MMLU-Pro, 81.2 on GPQA Diamond, 96.5 on HumanEval, and 95.1 on MATH. GPT-5.5 scores 93.1, 82.7, 97.0, and 96.3. The gap is under two points on most evals.
What license does DeepSeek V4 use?
The model uses the DeepSeek Open License 2.0. It allows commercial use, model modification, and redistribution. A separate commercial agreement is required for services above 50 million monthly active users.
Does DeepSeek V4 support function calling and agents?
Yes. The model supports function calling, JSON schema output, streaming, and long context. Official API endpoints are OpenAI-compatible. Self-hosted serving works with vLLM, SGLang, and llama.cpp.
Where do I download DeepSeek V4 weights and code?
The weights are on Hugging Face and the code is on GitHub. Look for the DeepSeek V4 model card and repository. The release includes bf16, fp8, AWQ, and GPTQ weights.
Should I switch from GPT-5.5 API to DeepSeek V4?
It depends on your workload. If you need maximum benchmark scores and managed enterprise SLAs, GPT-5.5 API is better. If you want lower cost, open weights, or private deployment, DeepSeek V4 is stronger. Many teams may use both for different tasks.
What Should You Remember?
- DeepSeek V4 release: The 1.6T parameter open model shipped June 10, 2026 with 256K context and free weights.
- Hardware cost: Full precision needs 640GB GPU memory and quantized versions need 160GB to 320GB.
- Benchmark gap: DeepSeek V4 is within 1 to 3 points of GPT-5.5 on MMLU-Pro, GPQA, HumanEval, and MATH.
- License terms: Commercial use and fine-tuning are allowed under DeepSeek Open License 2.0 with a 50M user threshold.
- API pricing: DeepSeek V4 official API costs $0.14 input and $0.28 output per million tokens, much cheaper than GPT-5.5.
- Run options: Self-host via vLLM or llama.cpp, or use the hosted API with OpenAI-compatible endpoints.
- GPT-5.5 tradeoff: Closed model still leads in managed tools and top scores, but costs over 10 times more per token.
Free AI News is an independent editorial publication. Information about AI pricing, free-tier limits, and features changes frequently and may become outdated. Always verify current details through the vendor’s official pages. Affiliate links may earn a commission at no cost to you, and never affect our reporting.
