MiniMax M3.1 Flash API: Python, cURL and Agent Setup

The short answer

To call MiniMax M3.1 Flash, set the base URL to https://api.moyi-api.com/v1 and the model to minimax-m3.1-flash. Short, repeated agent turns are a useful starting workload for your own latency and quality evaluation. On Moyi API, use the stable model ID minimax-m3.1-flash through the OpenAI-compatible Chat Completions endpoint. The live model page is the source of truth for current pricing, providers, and availability, so this article keeps those values out of the copy.

When to choose M3.1 Flash

Use it for tasks that can be split into small, verifiable steps:

• editing a file after a test failure
• summarizing tool output before the next action
• generating a focused SQL or shell command
• classifying tickets and routing them to a specialist
• running a fast first pass before sending hard cases to a larger model

For a long design document, a difficult proof, or a repository-wide refactor, route the final decision to a model that you have already validated for that workload. Measure successful tasks, retries and end-to-end latency before assigning M3.1 Flash that role.

OpenAI-compatible request

curl https://api.moyi-api.com/v1/chat/completions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax-m3.1-flash",
    "messages": [
      {"role": "system", "content": "You are a careful coding agent. Return a plan before editing."},
      {"role": "user", "content": "Inspect the failing test output and propose the smallest fix.\n\n..."}
    ]
  }'

The provider prefix from a public catalog is not part of the Moyi API request. Keep the model field exactly as minimax-m3.1-flash and use the same key format as your other OpenAI-compatible integrations.

Python SDK setup

Create a key in the API key dashboard, set it as the OPENAI_API_KEY environment variable, and install the SDK with python -m pip install openai. Save this as minimax_example.py and run python minimax_example.py. This makes one billable API request.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["OPENAI_API_KEY"],
    base_url="https://api.moyi-api.com/v1",
    timeout=120.0,
    max_retries=0,
)
response = client.chat.completions.create(
    model="minimax-m3.1-flash",
    messages=[{"role": "user", "content": "Explain why an empty list can cause IndexError in Python. Give one minimal fix."}],
)
print(response.choices[0].message.content)
print(response.usage)

Automatic SDK retries are disabled here so a first test has a clear request count. For production, add a bounded retry policy for transient failures and inspect the returned error before retrying authentication or invalid-model failures. See the API integration reference for endpoint and authentication details.

A reliable agent loop

A compact loop makes Flash-tier models easier to evaluate and retry.

1. Observe. Give the model the current file, error, or tool result and ask it to identify facts only.
2. Plan. Ask for one to three actions with a success condition for each action.
3. Act. Let your application or tool runner execute one action at a time.
4. Verify. Send back the command output, then ask whether the success condition passed.
5. Escalate. If the same step fails twice, preserve the trace and hand the case to a stronger model.

This separation prevents a single response from mixing an uncertain diagnosis with irreversible actions. It also gives you a clean place to add approval gates for production tools.

Prompt pattern for coding tasks

Keep the request narrow and include the output contract:

You are reviewing one failing test.

Facts:
- Test: <name>
- Error: <message>
- Relevant file: <path and excerpt>

Return JSON with:
- diagnosis: one sentence
- patch_plan: at most three steps
- verification: the command to run
Do not invent files or claim that a command ran.

For tool calls, validate the JSON before execution and log the model response beside the command result. A short, structured response is usually more useful than asking for a complete patch in the first turn.

Check live model details before shipping

Model catalogs change as routing and provider coverage change. Before a production rollout, open the MiniMax M3.1 Flash model page and confirm the current model ID, pricing, context limit, and availability. If you are comparing it with M3, record the same test prompts and token counts for both models instead of copying an old price table into application code.

Common errors

model_not_found

Use minimax-m3.1-flash, without a public-catalog namespace, date suffix, or :free suffix. Then check the model page for current availability.

The agent repeats a failed action

Add a retry budget and include the exact previous tool output. After two failures, switch to diagnosis mode or escalate. Do not let the model keep issuing the same command without new evidence.

Output is too terse

Ask for a fixed schema and include the relevant error context. If the task needs a long explanation, collect the concise plan first and request details only for the selected step.

Comparing MiniMax models

For the M3 model and its separate model ID, see the MiniMax M3 API guide. Keep prompts and acceptance criteria identical when comparing M3 with M3.1 Flash; the examples above are integration examples, not comparative benchmark results.