DeepSeek V4 Pro API

DeepSeek V4 Pro is the flagship tier of DeepSeek's V4 family, released on April 24, 2026 under the MIT license with open weights on Hugging Face. It is a Mixture-of-Experts model with 1.6 trillion total parameters and 49 billion active per token, pre-trained on 33 trillion tokens, and it ships alongside the lighter DeepSeek V4 Flash (284B / 13B active).

The model is text-in, text-out and exposes a single model ID — deepseek-v4-pro — with a reasoning-effort parameter instead of separate chat and reasoner endpoints. Both endpoints are OpenAI ChatCompletions- and Anthropic-compatible, so calling the DeepSeek V4 Pro API from an existing GPT or Claude client is a base-URL and model-string change, not a rewrite. Because the weights are MIT-licensed, the deepseek v4 pro api open source release also permits self-hosting and commercial use with no usage or regional restrictions.

Spec table

DeepSeek V4 Pro vs DeepSeek V4 Flash

Both models share the 1M-token context window, 384K max output, MIT license, and the same reasoning-effort controls. The split is size and cost. V4 Pro (1.6T / 49B active) is the choice for the hardest coding, math and long-horizon agent work; V4 Flash (284B / 13B active) trails Pro by roughly 1–2 points across most benchmarks in exchange for a much lower price, making it the default for high-volume chat, extraction and agent subtasks. A common pattern is to route routine calls to V4 Flash and escalate only the difficult tickets to V4 Pro — both live under the same GPTProto key and balance, so switching between them is a one-line model-string change.

DeepSeek V4 Pro vs Claude Opus 4.8

Claude Opus 4.8 leads on the hardest coding: it posts 88.6% on SWE-bench Verified against the DeepSeek V4 Pro API's 80.6%, and it holds an edge on general-knowledge recall and long-haystack retrieval. Where V4 Pro competes is competition math — a Codeforces rating of 3206 — open MIT weights, and price: on GPTProto it runs $1.39 / $2.78 per 1M versus Opus 4.8's $4 / $20, roughly 3x cheaper on input and 7x on output. Both share a 1M-token context window and both sit under one GPTProto key. The practical read: reach for Opus 4.8 when a wrong answer is expensive and reliability outranks cost; use the DeepSeek V4 Pro API for high-volume coding, math and long agent runs where the price gap compounds.

DeepSeek V4 Pro vs GLM-5.2

GLM-5.2, from Z.ai (formerly Zhipu AI), is the closest open-weight rival to the DeepSeek V4 Pro API: a 753B-parameter MoE model, also MIT-licensed, also with a 1M-token context window, and also tuned for agentic coding. On independent third-party scoring (Artificial Analysis Intelligence Index) GLM-5.2 currently rates a little higher overall, while V4 Pro stands out on competition math with a Codeforces rating of 3206. The two labs report their coding results on different SWE-bench variants, so a single head-to-head coding number would be misleading — treat them as roughly the same tier and let price and workload decide.

Price is where the choice sharpens. On GPTProto the DeepSeek V4 Pro API is $1.39 / $2.78 per 1M and GLM-5.2 is $1.26 / $3.96: GLM-5.2 is about 10% cheaper on input, while V4 Pro is roughly 30% cheaper on output. Output-heavy work — agent loops, long generations — is cheaper on V4 Pro; input-heavy work that pushes large contexts leans GLM-5.2. Both run under the same GPTProto key and balance, so you can route per task.

Switching from the Official DeepSeek API

If you already call DeepSeek directly, moving to  GPTProto keeps your code and swaps only the credentials and host. Point base_url at GPTProto's endpoint, use your GPTProto API key, and keep the model string deepseek-v4-pro — the request and response format are unchanged because the endpoint stays OpenAI- and Anthropic-compatible. What you gain is one balance that also spends against GPT, Claude, Gemini and 200+ other models, with no separate DeepSeek account, top-up, or region check to clear first. Teams outside DeepSeek's direct-billing regions use this to reach the DeepSeek V4 Pro API without setting up a China-based payment method.

Where DeepSeek V4 Pro Falls Short

The honest limits matter as much as the benchmarks. DeepSeek V4 Pro trails top closed models on world-knowledge recall — it scores about 57.9% on SimpleQA-Verified against Gemini 3.1 Pro's ~75.6% — and, like most reasoning-heavy models, it tends to answer rather than abstain on questions it can't be sure of, so confidence calibration is worth watching in factual-lookup workloads. It is a text-only model: no image or audio input. And while it ships a 1M-token window cheaply, the very best long-haystack retrieval accuracy still belongs to models like Claude Opus. For code, math and long agent runs it competes at the frontier; for factual QA where being wrong is costly, pair it with retrieval or route those calls elsewhere.