DEV Community

Cover image for Measuring MiniMax M3.1 Flash's Five Effort Levels on One Bug Fix
AIHubMix
AIHubMix

Posted on

Measuring MiniMax M3.1 Flash's Five Effort Levels on One Bug Fix

#ai #llm #api #minimax

TL;DR: We ran the same two-bug fix through MiniMax M3.1 Flash at all five effort levels plus "omitted", three runs each, and graded every answer against 215 hidden tests. Every run passed. The omitted default, which MiniMax documents as max, averaged 6,364 thinking tokens, 56 seconds, and $0.0045 per fix. low averaged 591 thinking tokens, 9.5 seconds, and $0.0006. So the default cost about 7× low and bought nothing on this task. Set low explicitly and move up per task.

The engineering problem: M3.1 Flash always thinks, and if the request has no effort field, MiniMax's text generation guide says it "defaults to max". Code migrated from MiniMax M2.x never set an effort level because M2.x had none. That code now runs at the most expensive setting by default.

Setup

  • Model: minimax-m3.1-flash on AIHubMix. Upstream reports MiniMax-M3.1-Flash.
  • Endpoint: POST https://aihubmix.com/v1/chat/completions, max_tokens: 32768, key from AIHUBMIX_API_KEY (see the AIHubMix quick start).
  • Date: 19 October 2026. Three runs per setting.
  • Prices (AIHubMix, per 1M tokens): $0.165 input, $0.66 output, $0.0165 cached input. Thinking tokens bill as output.

The task: a billing function with two bugs. A pause that runs past the period end double-counts one day, and overlapping pauses get subtracted twice. The prompt states both symptoms and asks for the corrected function in one code block. A local harness runs it against 215 cases: 15 hand-written edge cases plus 200 random ones checked against a brute-force reference. The original buggy function passes 138.

Field names per protocol

Protocol Field Values Default
OpenAI Chat Completions reasoning_effort low, medium, high, xhigh, max max
Anthropic Messages output_config.effort low, medium, high, xhigh, max max
OpenAI Responses reasoning.effort low, medium, high, xhigh, max max

All three protocols work for this model on AIHubMix. Thinking can't be turned off. MiniMax's Messages API reference says thinking: {"type": "disabled"} and effort none return HTTP 400 with model "MiniMax-M3.1-Flash" requires adaptive thinking. Older MiniMax models ignore the effort field.

The measurement loop

This is the shape of the run, minus the grader:

import os, time
from openai import OpenAI

client = OpenAI(base_url="https://aihubmix.com/v1", api_key=os.environ["AIHUBMIX_API_KEY"])
prompt = open("bugfix_prompt.md").read()

for effort in [None, "low", "medium", "high", "xhigh", "max"]:
    for run in range(3):
        extra = {"reasoning_effort": effort} if effort else {}
        t0 = time.time()
        r = client.chat.completions.create(
            model="minimax-m3.1-flash", max_tokens=32768,
            messages=[{"role": "user", "content": prompt}], **extra)
        u = r.usage
        print(effort or "omitted", run, round(time.time() - t0, 1),
              u.completion_tokens, u.completion_tokens_details.reasoning_tokens)
Enter fullscreen mode Exit fullscreen mode

Results

Effort Avg thinking tokens Range (3 runs) Avg time Avg cost per fix Tests
low 591 344–812 9.5 s $0.0006 215/215 ×3
medium 1,522 970–2,488 21.7 s $0.0013 215/215 ×3
high 2,863 2,194–3,673 27.1 s $0.0022 215/215 ×3
xhigh 1,949 1,019–2,562 19.2 s $0.0015 215/215 ×3
max 4,699 2,612–6,246 40.4 s $0.0034 215/215 ×3
omitted 6,364 4,284–9,376 56.3 s $0.0045 215/215 ×3

Cost arithmetic for the "omitted" row: 464 prompt tokens, about 192 of them cached, plus 6,673 output tokens. That's (272 × $0.165 + 192 × $0.0165 + 6,673 × $0.66) ÷ 1M ≈ $0.0045.

What the table supports:

  • Quality was flat. 18 Chat runs, 18 perfect fixes. On this bounded bug, thinking beyond low bought nothing measurable.
  • low → medium is a real step: about 2.6× the thinking tokens and more than twice the time.
  • Above medium, n=3 can't order the levels. xhigh averaged below high, and max overlaps both.
  • Omitted ≈ max, as documented. The higher "omitted" average is within run-to-run noise.
  • Time tracks tokens. Throughput held at about 112 output tokens per second across all 24 Flash runs (Chat and Messages), so a 6,000-token think costs close to a minute of wall time.

Short prompts behave differently. With five support tickets sorted into four labels, low used 0 thinking tokens and the default averaged 6, with every label correct. "What is 17 × 23?" at low produced no thinking and 2 output tokens. Adaptive thinking scales with the prompt, so the missing off switch barely costs anything on classification-style calls.

Failure modes and fixes

Truncation from thinking. Thinking counts toward max_tokens. One default run used 9,376 thinking tokens on a single function. A 4K or 8K limit carried over from M2.x can end with finish_reason: "length" and a cut-off answer. Fix: raise max_tokens (MiniMax recommends 131,072, max 524,288) and check finish_reason.

Latency spikes at a fixed level. On the Anthropic Messages endpoint, three low runs used 511, 1,861, and 6,060 thinking tokens; the last took 56 seconds. Effort sets the average depth, not a cap. Fix: set client and proxy timeouts to 120 s or more at high and above, and don't size timeouts from low averages alone.

Effort values that silently do nothing. Through AIHubMix, reasoning_effort values none, minimal, and auto all returned HTTP 200, and the model still thought. MiniMax's own endpoints reject unknown values, according to a gateway project's tests (sub2api issue #8025). Fix: send one of the five documented values. AIHubMix's unified reasoning parameters page describes how the gateway maps reasoning fields across vendors.

Tools that don't send a level. OpenCode resolves no effort variants for this model, so its effort toggle does nothing. Some frameworks also send "thinking disabled", which returns the 400. Fix: check the outgoing request body.

Mixed-model routing. M3 and M2.x ignore the effort field, so a level that saves money on Flash does nothing on the others.

Verify it reached the model. Read usage: completion_tokens_details.reasoning_tokens (Chat), output_tokens_details.thinking_tokens (Messages), output_tokens_details.reasoning_tokens (Responses). If these don't change when you change the level, the setting isn't arriving.

Setting it on Messages and Responses through AIHubMix:

import os
import anthropic
from openai import OpenAI

ant = anthropic.Anthropic(base_url="https://aihubmix.com", api_key=os.environ["AIHUBMIX_API_KEY"])
msg = ant.messages.create(
    model="minimax-m3.1-flash", max_tokens=32768,
    messages=[{"role": "user", "content": "Write a function that merges overlapping date ranges."}],
    extra_body={"output_config": {"effort": "medium"}},
)
print(msg.usage)

oai = OpenAI(base_url="https://aihubmix.com/v1", api_key=os.environ["AIHUBMIX_API_KEY"])
resp = oai.responses.create(
    model="minimax-m3.1-flash", reasoning={"effort": "low"}, max_output_tokens=32768,
    input="Write a function that merges overlapping date ranges.",
)
print(resp.usage)
Enter fullscreen mode Exit fullscreen mode

Starting points

Only the first two rows are backed by the measurements above. The rest are recommendations to check against your own retry rates.

Workload Start at Move up when
Classification, extraction, routing low Labels are wrong on hard cases
Single-function fixes, small reviews low A fix fails tests twice
Multi-file changes, refactors medium The agent misses a dependent file
Agent loops with many tool calls low or medium Steps repeat or stall
Hard debugging, design questions high Answers stay shallow
Interactive chat, person waiting low Never default to max here

What we can't verify

  • Where higher levels start to pay off. Our task was solved at every level. MiniMax hasn't published per-level token, latency, or accuracy data, and one bounded task can't show where high, xhigh, or max earn their cost.
  • Ordering above medium. Three runs per level isn't enough to separate high, xhigh, and max.
  • Other tasks and prompt lengths. All numbers come from one short coding prompt and one classification prompt, measured by AIHubMix on 19 October 2026. Nobody else has reproduced them.

Keep reading: the MiniMax M3.1 Flash series

Sources

Top comments (0)