MiniMax M1 API1M context

Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).

What is MiniMax M1?

MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it...

MiniMax M1 on the release timeline

MiniMax M1 pricing

Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):

On output price, MiniMax M1 is cheaper than 40% of the 309 models in the catalog. It is served by 2 providers; the best combined rate at our last snapshot was ≈ $0.40 / $2.20 per 1M via Minimax, with upstream quantizations bf16.

What MiniMax M1 costs per month

Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.

MiniMax M1 vs alternatives

Specs

How to call MiniMax M1

Copyfrom openai import OpenAI
client = OpenAI(base_url="https://api.datallmlab.com/v1", api_key="YOUR_DATALLM_LAB_KEY")
resp = client.chat.completions.create(
    model="minimax/minimax-m1",
    messages=[{"role": "user", "content": "Hello"}],
    # supports tools=[...] and tool_choice
)
print(resp.choices[0].message.content)

Copycurl https://api.datallmlab.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"minimax/minimax-m1","messages":[{"role":"user","content":"Hello"}]}'

Copyimport OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.datallmlab.com/v1", apiKey: process.env.DATALLM_KEY });
const r = await client.chat.completions.create({
  model: "minimax/minimax-m1",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes MiniMax M1 and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for MiniMax M1

• State the goal, not every step. MiniMax M1 reasons internally — give it the objective, constraints and success criteria and let it plan the approach; over-scripting each step tends to lower quality. Turn effort up for hard problems.
• Huge 1M context — but anchor the ask. You can paste whole documents or codebases; models attend most to the start and end, so put the key instruction at the top and restate it after long inputs.
• Give it real tools. Pass a tools=[...] schema instead of asking it to "pretend" — let MiniMax M1 emit tool calls, execute them, and feed results back for the next turn.

Tips are derived from MiniMax M1's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for MiniMax M1

Popular open-source frameworks and agents to build with MiniMax M1 over the API — star counts pulled from GitHub (July 2026).

Listed by GitHub stars; inclusion is by ecosystem relevance (agent frameworks, SDKs and gateways), not affiliation. Stars change — see each repo for current numbers.

Migrating to MiniMax M1

Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → minimax/minimax-m1. Messages, streaming, tool calls and the rest of your code stay the same. Routing & failover guide.

Rate limits & reliability

Rate limits here are the DataLLM Lab gateway's, not the upstream vendor's. On 429 (rate limited) or 503 (provider busy), retry with exponential backoff; because MiniMax M1 is served by 2 providers, requests can fail over to a healthy one automatically. See the error-code guide and failover setup.

Related reading

Frequently asked questions