import requests r = requests.post( "https://api.aimlapi.com/v1/chat/completions", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "deepseek/deepseek-chat-v3-0324", "messages": [ { "role": "user", "content": "Hello!" } ] }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "deepseek/deepseek-chat-v3-0324", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"deepseek/deepseek-chat-v3-0324","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
| Cached input |
| Benchmark | Score | What it measures | Source | Retrieved |
|---|---|---|---|---|
| LiveCodeBench | 49.2% | Contamination-free competitive programming problems | Source | July 12, 2026 |
| MMLU-Pro | 81.2% | Multi-discipline knowledge + reasoning (harder MMLU) | Source | July 12, 2026 |
| Intelligence | 8.5 | Composite score across standardised reasoning, knowledge and problem-solving evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| Coding | 23 | Composite score across standardised coding evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| Math | 26 | Composite score across standardised mathematics evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
DeepSeek-V3 This page | Coding + agents | |||
| Reasoning + agents | ||||
| Balanced coding + agents | ||||
| Long-context, multimodal & agentic workflows | ||||
| Reasoning + agents |
DeepSeek-V3 has a 128,000 tokens context window and can return up to 124,000 tokens.
DeepSeek-V3 takes text as input and returns text.
Use deepseek/deepseek-chat-v3-0324 as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
DeepSeek-V3 became available on August 28, 2025.
DeepSeek-V3 is priced at input $0.398866 / 1M tokens, output $1.567956 / 1M tokens, cached input $0.192556 / 1M tokens.
Yes, DeepSeek-V3 can stream responses as they are generated.
DeepSeek-V3 was built by DeepSeek.
DeepSeek-V3 is classified as a chat model, not a dedicated reasoning model.
Yes, it supports function calling, parallel tool calls, and structured outputs.