🔍 Read the full analysis: How MiniMax M3.1 Flash Compared With Open Models In Our Newsroom on ThorstenMeyerAI.com
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TL;DR
MiniMax released M3.1 Flash on October 19, 2026, at $0.15 per million input tokens and $0.60 per million output tokens, with its weights promised for October 23. In an independent index it scored 43, behind several open-weight models, while a newsroom test covered 24 English and German articles; the supplied source does not include that test’s results. MiniMax’s speed and benchmark claims remain unverified independently, and the model’s weights and license are not yet available.
MiniMax released M3.1 Flash on October 19, pricing it at $0.15 per million input tokens and $0.60 per million output tokens, as it positions the model for fast, lower-cost agent work. A review by ThorstenMeyerAI.com compared the model’s published claims with Artificial Analysis measurements and a newsroom test of 24 English and German articles; the independent index placed it behind several open-weight rivals, while the source provided does not report the newsroom test scores.
MiniMax says M3.1 Flash costs half as much per token as its predecessor, M3, and about 70% less per agent task. Those savings are company claims, not results established by the newsroom comparison. The listed API prices are $0.15 per million input tokens, $0.60 per million output tokens, and $0.015 per million cached input tokens. The model is available through the MiniMax API, M Plan subscription, MiniMax Code and OpenRouter; OpenRouter offered it free for its first 72 hours.
On Artificial Analysis’ Intelligence Index, M3.1 Flash scored 43. The review’s comparison lists MiMo-V2.6-Pro at 46, DeepSeek V4.1 Flash 1017 and GLM-5.3 at 45, and Step 5 Preview and Kimi K3 at 44. Artificial Analysis measured M3.1 Flash at 120 output tokens per second and 1.08 seconds to first token, below MiniMax’s claim of more than 200 tokens per second in everyday use. These are measurements from the index, not a direct newsroom speed test.
The newsroom test used 12 real news sources, each turned into an English and a German article with the same production prompt. Two judges, GPT-6.1 Sol and Claude Opus 5.5, assessed source fidelity, invented facts, clarity, usefulness, structure and polish on a five-point scale. The source says the model ran on OpenRouter’s free endpoint with a 32,000-token output limit, but its supplied results table stops before any scores appear. No ranking or quality conclusion from that test can be reported from the available material.
Infographic 01 · MiniMax M3.1 Flash
A step behind on the index. Ahead of every open model in our newsroom.
An independent intelligence index puts MiniMax M3.1 Flash a step behind the open-weight leaders. Writing real news from real sources, it beat all of them.
Independent index
Artificial Analysis Intelligence Index, 10 evaluations. Bar length on a 0–50 scale.
Artificial Analysis lists M3.1 Flash as proprietary until its weights are released.
Our newsroom test
Mean score, English articles, 1–5 by GPT-6.1 Sol and Claude Opus 5.5 with the source in context. Bar length on a 0–5 scale.
In German, M3.1 Flash (3.36) also leads every open model, but GPT-6 Luna is clearly ahead (3.62).
Sources: Artificial Analysis, MiniMax-M3.1-Flash and open-weights models (20 Oct 2026); Thorsten Meyer AI newsroom benchmark, 12 sources × English + German, run 20 Oct 2026.
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Price Gains Face Quality and Speed Tests
M3.1 Flash’s low listed price may make it attractive for workloads that generate many tokens, including agent tasks and newsroom drafting. But price per token is not the same as cost per completed task: Artificial Analysis found that the model used 180 million tokens across its index evaluation, compared with a 99 million median. A model that consumes more tokens can narrow the practical savings even when each token costs less.
The index also presents a mixed picture rather than a clear performance lead. M3.1 Flash’s 43-point score trails several listed open-weight models, and its measured speed is below MiniMax’s advertised figure. For publishers, the missing newsroom scores matter: a low-cost model is useful only if it follows source material, avoids invented details and produces copy editors can use. The available source does not establish whether M3.1 Flash meets that standard.
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From Space Bunny to M3.1 Flash
Before its public release, the model appeared on OpenRouter under the anonymous name Space Bunny Alpha. The source says it led OpenRouter’s usage ranking for 10 days after appearing on September 23, processed 56.7 trillion tokens there, and was removed on October 5. MiniMax says it processed more than 200 trillion tokens overall. Those figures are attributed to the source and company; the review does not provide an independent audit.
MiniMax’s own benchmark charts show M3.1 Flash ahead on Automation-Bench, at 62.2, while DeepSeek V4.1 Flash 1017 scores higher on DeepSWE and Terminal-Bench. The review cautions that the charts are self-reported, that MiniMax selected the rivals, and that some comparisons omit newer versions or leave scores unreported. It also says Artificial Analysis classifies M3.1 Flash as proprietary because its weights have not yet been released.
“More than 200 tokens per second in everyday use, with a median above 170.”
— MiniMax
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Newsroom Scores and Data Handling
The supplied source excerpt does not include the results of the 24-article newsroom test; it ends after the English and German score-table headings. The average scores and any comparison with other models are therefore unknown. The index and newsroom measures also address different tasks, so neither can fill in for the missing editorial results.
MiniMax has not disclosed an official parameter count or model license in the source. It says the weights are due October 23, but their release and terms remain unconfirmed here. The review also flags a data question from the anonymous OpenRouter period: it says the provider could retain prompts, though not train on them, and that MiniMax has not explained what happened to that data. The source does not establish whether any newsroom test prompts were retained.
newsroom AI content generation software
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Watch for Weights and Full Results
The next stated milestone is MiniMax’s promised October 23 weights release. Its availability, license and any confirmed technical specifications would clarify whether developers can inspect and run the model outside MiniMax’s services. The review’s missing newsroom-results table is also needed before readers can assess how the model performed on factual editorial writing or compare it with other systems.
Until those details are available, the most supportable assessment is limited: M3.1 Flash has a low listed token price, scored 43 on Artificial Analysis’ index, and measured slower there than MiniMax’s advertised everyday speed. Its editorial quality, real-world cost advantage and prompt-data handling remain unresolved by the provided material.
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Key Questions
When was MiniMax M3.1 Flash released?
MiniMax released M3.1 Flash on October 19, 2026, through its API, M Plan subscription, MiniMax Code and OpenRouter.
How much does M3.1 Flash cost?
The listed price is $0.15 per million input tokens, $0.60 per million output tokens and $0.015 per million cached input tokens. OpenRouter offered the model free for its first 72 hours.
How did it compare with open models on the independent index?
Artificial Analysis gave M3.1 Flash an Intelligence Index score of 43. The review lists several open-weight models at 44 or higher. Its measured speed was 120 tokens per second, below MiniMax’s stated everyday-use figure of more than 200.
What did the newsroom test find?
The test assessed 24 articles: 12 real sources, each written in English and German and judged for source fidelity, invented facts, clarity, usefulness, structure and polish. The provided source does not include the scores, so its outcome cannot be determined from this material.
Are the model weights available?
Not in the supplied report. MiniMax said the weights would arrive on October 23, 2026; the source does not confirm their release or state a license.
Source: ThorstenMeyerAI.com
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