Deepseek has turned its 75 percent discount on the flagship Deepseek V4 Pro model into a permanent price cut, the company announced on X. The promotion was originally scheduled to expire on May 31, 2026.
Under the permanent pricing, one million input tokens without cache cost $0.435, while one million output tokens cost $0.87. Cache hits push the input price even lower. In comparison, GPT 5.5 charges $5 per million input tokens and $30 per million output tokens, while Opus 4.7 sits at $5 for input and $25 for output.
Pricing comparison at a glance
The table below shows how Deepseek's models stack up against competitors on a per-token basis.
| Model | Input per 1M tokens | Input cache hit | Output per 1M tokens | |, , , -|, , , , , , , , , , -|, , , , , , , , |, , , , , , , , , , , | | Deepseek-V4-Pro | $0.435 | $0.003625 | $0.87 | | Deepseek-V4-Flash | $0.14 | $0.0028 | $0.28 | | GPT-5.5 | $5.00 | $0.50 | $30.00 | | GPT-5.5 (Long Context, >272K) | $10.00 | $1.00 | $45.00 | | Opus 4.7 | $5.00 | $0.50 | $25.00 |
That makes Deepseek's flagship about 11.5 times cheaper than GPT 5.5 on standard input pricing. The gap is much wider on output, where Deepseek V4 Pro is about 34.5 times cheaper. Against GPT 5.5 long context pricing above 272K tokens, Deepseek V4 Pro is about 23 times cheaper on input and about 51.7 times cheaper on output. Deepseek V4 Flash is cheaper still.
Both Deepseek models offer a one million token context window and up to 384,000 output tokens. Deepseek also supports both OpenAI and Anthropic API formats, making it easier for developers to switch.
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Token prices only tell half the story
Raw per-token pricing is only part of the picture, though. Token consumption per task matters just as much. Think of it like gas prices: a low price per gallon does not help if your engine guzzles fuel.
A good example is Google's Gemini Flash 3.5. On paper, it is cheaper and performs similarly to the previous Pro model 3.1, but it burns through far more tokens, making it potentially pricier in practice. Anthropic's Opus 4.7 looks cheaper on paper than GPT-5.5 too, but uses more tokens than its predecessor. GPT-5.5, on the other hand, consumes fewer tokens than GPT-5.4. Still, both models ended up 30 to 90 percent more expensive than the models they replaced.
Deepseek V4 clearly trails the top frontier models GPT-5.5 and Opus 4.7 in raw performance. How much exactly depends on the task, and benchmarks only tell half the story. Only real-world use will tell. But the price gap is massive, especially for agentic AI systems that chew through many times more tokens than a standard chatbot.
As AI usage grows, companies are getting more price-sensitive. As long as ROI on AI spending remains hard to measure, many firms may shift strategy: away from the best model and toward the cheapest one that is still good enough.
Deepseek is entering its first funding round, but it faces nowhere near the revenue pressure that OpenAI and Anthropic do. Both of those labs are also heading toward IPOs.
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