DeepSeek API - Chinese Model That Actually Shows Its Work

DeepSeek keeps it simple - two models, clear names. No "gpt-4-turbo-preview-0613-with-experimental-function-calling-v2" bullshit.

deepseek-chat - The Basic One

Your standard chat model that doesn't overthink everything. Works like GPT-4 but cheaper and faster. I use it for code reviews, explaining functions, basic debugging. Handles JSON mode without randomly wrapping everything in markdown code blocks like Claude does.

Function calling is solid. Better than GPT-4 at following schemas actually.

deepseek-reasoner - The One That Shows Its Work

This is why I switched. When I'm stuck on a problem for hours, 7 extra seconds doesn't matter. What matters is not having to guess why the AI is wrong.

Had this recursive thing that was totally fucked. Spent way too long on it. o1 gave me some useless "try this" bullshit with zero explanation. DeepSeek actually walked through the logic and showed me where I was hitting stack limits.

The reasoning traces are massive - like walls of text explaining every step. But when you're debugging production at midnight and need to understand why something's broken, that context saves your ass.

OpenAI Compatibility (Actually Works)

Drop-in replacement. Literally just:

client = OpenAI(
    base_url="https://api.deepseek.com",
    api_key="your-deepseek-key"
)

My entire codebase worked instantly. No parameter incompatibilities or weird edge cases.

Found out the hard way that reasoner can't do function calls. My entire agent framework just... stopped working. Took me 3 hours to figure out why. If you need tools, use deepseek-chat.

Automatic Caching Actually Works

The caching is automatic and aggressive. Same system prompt across thousands of requests? Those tokens cost $0.07 per million instead of $0.55.

My OpenAI bill was getting stupid expensive - maybe $150+ on bad days for batch document processing. DeepSeek cut that way down, like $30-40 on most days, sometimes less if the caching hits right.

The trick: put your repeated stuff (system prompts, examples) at the start of your messages. Cached segments have to be prefixes.

DeepSeek built a 671B parameter model but only runs 37B parameters per request. It's got all these experts but only fires up the ones it needs, so it's not burning through the full model for simple stuff.

The MoE Approach (Without the Buzzword Bullshit)

Most models waste compute running everything for simple requests. DeepSeek only spins up the parts it needs. Ask for Python help? Code experts activate. Math problem? Math experts wake up.

This is how they undercut OpenAI's pricing by 4x. They're not burning through a full 671B model to write your grocery list.

The reasoning model uses the same architecture but different training. Instead of jumping to conclusions, they taught it to show its work. Takes longer but you actually understand what went wrong.

Anyway, here's how this thing actually performs...

Real Performance (Not Benchmark Theater)

Sure, it crushes MATH-500 vs GPT-4, looks good on paper. But benchmarks are mostly bullshit.

Here's what actually matters: It's solid for code reviews - caught some bugs I missed. Math is hit or miss though, sometimes it gets weird with edge cases. The reasoning model helped me untangle a fucked up algorithm that had me stuck for 6 hours.

Reasoning takes longer than o1 - maybe 80-90 seconds. But when you're debugging something at 2am, you care about getting the right answer, not saving 10 seconds.

Drop-in Replacement That Actually Works

Change two lines:

client = OpenAI(
    base_url=\"https://api.deepseek.com\",
    api_key=\"sk-your-deepseek-key\"
)

That's it. My entire codebase worked without changes. No parameter mapping, no weird edge cases.

The auto-caching is aggressive - sometimes saves 80% on repeated prompts. But reasoner can't do function calls. Took me like 3 attempts to figure out why my agent stopped working - turned out I was hitting the reasoning model instead of chat for tool calls.