Chinese AI lab DeepSeek's last model release was V3.2 (and V3.2 Speciale) last December . They just dropped the first of their hotly anticipated V4 series in the shape of two preview models, DeepSeek-V4-Pro and DeepSeek-V4-Flash . Both models are 1 million token context Mixture of Experts. Pro is 1.6T total parameters, 49B active. Flash is 284B total, 13B active. They're using the standard MIT license. I think this makes DeepSeek-V4-Pro the new largest open weights model. It's larger than Kimi K2.6 (1.1T) and GLM-5.1 (754B) and more than twice the size of DeepSeek V3.2 (685B). Pro is 865GB on Hugging Face, Flash is 160GB. I'm hoping that a lightly quantized Flash will run on my 128GB M5 MacBook Pro. It's possible the Pro model may run on it if I can stream just the necessary active experts from disk. For the moment I tried the models out via OpenRouter , using llm-openrouter : llm install llm-openrouter llm openrouter refresh llm -m openrouter/deepseek/deepseek-v4-pro 'Generate an SVG of a pelican riding a bicycle' Here's the pelican for DeepSeek-V4-Flash : And for DeepSeek-V4-Pro : For comparison, take a look at the pelicans I got from DeepSeek V3.2 in December , V3.1 in August , and V3-0324 in March 2025 . So the pelicans are pretty good, but what's really notable here is the cost . DeepSeek V4 is a very, very inexpensive model. Here's DeepSeek's pricing page . They're charging $0.14/million tokens input and $0.28/million tokens output for Flash, and $1.74/million input and $3.48/million output for Pro. Here's a comparison table with the frontier models from Gemini, OpenAI and Anthropic: Model Input ($/M) Output ($/M) DeepSeek V4 Flash $0.14 $0.28 GPT-5.4 Nano $0.20 $1.25 Gemini 3.1 Flash-Lite $0.25 $1.50 Gemini 3 Flash Preview $0.50 $3 GPT-5.4 Mini $0.75 $4.50 Claude Haiku 4.5 $1 $5 DeepSeek V4 Pro $1.74 $3.48 Gemini 3.1 Pro $2 $12 GPT-5.4 $2.50 $15 Claude Sonnet 4.6 $3 $15 Claude Opus 4.7 $5 $25 GPT-5.5 $5 $30 DeepSeek-V4-Flash is the cheapest of the small models, beating even OpenAI's GPT-5.4 Nano. DeepSeek-V4-Pro is the cheapest of the larger frontier models. This note from the DeepSeek paper helps explain why they can price these models so low - they've focused a great deal on efficiency with this release, especially for longer context prompts: In the scenario of 1M-token context, even DeepSeek-V4-Pro, which has a larger number of activated parameters, attains only 27% of the single-token FLOPs (measured in equivalent FP8 FLOPs) and 10% of the KV cache size relative to DeepSeek-V3.2. Furthermore, DeepSeek-V4-Flash, with its smaller number of activated parameters, pushes efficiency even further: in the 1M-token context setting, it achieves only 10% of the single-token FLOPs and 7% of the KV cache size compared with DeepSeek-V3.2. DeepSeek's self-reported benchmarks in their paper show their Pro model competitive with those other frontier models, albeit with this note: Through the expansion of reasoning tokens, DeepSeek-V4-Pro-Max demonstrates superior performance relative to GPT-5.2 and Gemini-3.0-Pro on standard reasoning benchmarks. Nevertheless, its performance falls marginally short of GPT-5.4 and Gemini-3.1-Pro, suggesting a developmental trajectory that trails state-of-the-art frontier models by approximately 3 to 6 months. I'm keeping an eye on huggingface.co/unsloth/models as I expect the Unsloth team will have a set of quantized versions out pretty soon. It's going to be very interesting to see how well that Flash model runs on my own machine. Tags: ai , generative-ai , llms , llm , llm-pricing , pelican-riding-a-bicycle , deepseek , llm-release , openrouter , ai-in-china