LOLCaspar reports that when using minimax-m3.1-flash-free in Hermes and T3, two or three simple tasks involved prolonged reasoning; one session screenshot shows the model generating approximately 115,900 reasoning tokens in the latter half, about 16 times the gpt-6.1-sol usage in the earlier part of the same chat. The author also says it kept running on a work task while GPT-6.1-Sol had completed it about 29 minutes earlier. Because the task, endpoint, and client configuration are incomplete, this is a personal field report about a free endpoint and cannot be generalized to the official version or other service paths.
Tasks this can help assess: Identify anomalous behavior by a free endpoint on simple tasks, long tasks, and reasoning budgets; design controlled tests for reasoning tokens, per-call duration, subagent count, loops, and task completion rate.
Tasks this should not be generalized to: Assess the overall coding ability, official-version quality, pricing, or fair ranking of MiniMax M3.1 Flash; attribute the free endpoint's behavior to the model itself.
Applicable model version: The screenshot explicitly says minimax-m3.1-flash-free; the body uses the title MiniMax M3.1 Flash and provides no model snapshot or API ID.
Test environment or client: Hermes and T3; OpenCode and the OpenRouter free path also appear in the comments, but the original post does not say whether the author used those paths.
Reasoning tier and parameters: In a comment reply, the author says they used low or no reasoning; temperature, top_p, context limit, tool configuration, and other request parameters are not specified.
The author first says they had used M3 and were interested in the 3.1 version in the benchmark, describing this as “two or three simple tasks.” The body gives no task description, repository, prompt, or completion criteria.
The first screenshot lists a model usage table. The minimax-m3.1-flash-free row shows 315 requests, 840.1k input, 39.2M cache read, 276.9k output, and a cost of $0.00; the screenshot also lists usage for other models but does not state the measurement window, whether the requests came from the same task, or the accounting basis for the cost.
The second screenshot is labeled Reasoning in this chat — measured from the session DB. The session is 20261020_103102_0b5831, with 383 messages and 172 API calls. It compares the first 8 turns using gpt-6.1-sol with turns 9–17 using minimax-m3.1-flash-free.
The author also describes one chat in which the model kept reasoning for 18 minutes about the question “why is reasoning so much?” and spawned two subagents. The body ends with “I asked it to evaluate:”; the subject of that evaluation is not disclosed.
| Item | gpt-6.1-sol | minimax-m3.1-flash-free |
|---|---|---|
| Session turns | 1–8 | 9–17 |
| Reasoning tokens | 7,154 | approximately 115,900 |
| Per call | 199 | approximately 1,900 |
| Screenshot conclusion | — | approximately 16× in the same chat |
The screenshot also lists 922,264 total reasoning characters and labels approximately 115,900 tokens as an estimate based on dividing characters by 4; the two figures do not correspond directly (922,264 ÷ 4 = 230,566), and the original post does not explain the measurement scope. Wall time after the model switch accounts for approximately 45%. These are readings from the author's session database export and cannot be used to recalculate exact token usage.
LOLCaspar says the free version is “unusable” and had it run a work task without interruption; they add that gpt-6.1-sol had completed the task about 29 minutes earlier. The original post does not explicitly say whether M3.1 ultimately failed, so this can only be recorded as field feedback from a point when the task was unfinished.
sugarw0000kie (2026-10-20 12:15:46 UTC) says it worked very well for them over the past few weeks through a MiniMax subscription, but the free OpenCode version is more verbose and Space Bunny feels like a completely different model; they explicitly say they did not run a head-to-head comparison.
Meandernder (2026-10-20 12:59:27 UTC) says the OpenRouter free version was running well at the time, but provides no task or configuration data.
Amarsir (2026-10-20 13:25:04 UTC) thinks Max may be slow and repetitive, suggests trying medium/high, and gives minimax/minimax-m3.1-flash:free; LOLCaspar replies that they used low or no reasoning. Amarsir also speculates that different free quotas could cause the difference; this is only a commenter's speculation.
diaracing (2026-10-20 17:27:56 UTC) says the model will “overthink” in a loop even when given a direct request; they provide no task, endpoint, or logs.
Post ID: t3_1xaq8wv; author: LOLCaspar; post score at collection: 8; comment count: 7.
Key points from the original wording: Was very excited to try this model, used M3 before ..., what is this? This was 2 or 3 simple tasks, in one chat he reasoned with the question "why is reasoning so much?" for 18 minutes and spawned 2 subagents., I use it in hermes and T3.
Original URL for the first screenshot: Usage table.
Original URL for the second screenshot: Session reasoning statistics.
The minimax-m3.1-flash-free row in the first screenshot: 315 requests, 840.1k input, 39.2M cache read, 276.9k output, $0.00.
Session identifiers in the second screenshot: 20261020_103102_0b5831; 383 messages, 172 API calls; total reasoning characters 922,264; reasoning tokens ~115,900; share of wall time (post-switch) ~45%; gpt-6.1-sol (turns 1–8) 7,154 tokens = 199/call; minimax-m3.1-flash-free (turns 9–17) ~115,900 = ~1,900/call.
Visible comment links: sugarw0000kie, LOLCaspar reply, Meandernder, Amarsir, diaracing.
This record supports a limited conclusion: on the minimax-m3.1-flash-free path used by the author, simple tasks may involve excessively long reasoning and subagent calls; the session screenshot provided by the author also shows that reasoning tokens and tokens per call were substantially higher after switching to this model than in the earlier gpt-6.1-sol portion of the same chat. Different experiences reported for the MiniMax subscription, the OpenRouter free version, and the OpenCode free version suggest that the service path may affect the result, but there is no shared task or standardized configuration.
This does not establish the reasoning efficiency of the official MiniMax M3.1 Flash version, nor does it confirm that the long task ultimately failed. The original post provides no evaluation target, prompt, code repository, success criteria, model snapshot, API endpoint implementation, client version, context length, tool permissions, sampling parameters, retry rules, or reasoning-statistics script. The measurement scopes of the two screenshots are also unspecified; the 315 requests in the first table cannot be directly combined with the 172 API calls in the second screenshot.
At minimum, fix the model API ID and snapshot, service path (official subscription, OpenCode free endpoint, or OpenRouter), Hermes/T3/OpenCode version, reasoning tier, context limit, tool and subagent permissions, temperature, top_p, task repository, and prompt. Record input and output tokens per turn, reasoning tokens, call count, subagent count, duration, loop count, failure reason, and completion status separately; measure “why is reasoning so much?” and a long task with explicit completion criteria as separate cases. The statistics script and session scope must be made public to verify the 16× difference shown in the screenshot.
MiniMax M3.1 Flash
