The launch arrived with a masked model and left with an argument. MiniMax's new agent model now sits ahead of several open-weight rivals on independent scoring — at roughly half the speed the company advertised, with no weights in sight and no license named.
For well over a week, an anonymous model ran on OpenRouter under the name "Space Bunny." Nobody knew whose it was. The community tallied more than 200 trillion tokens of traffic and watched it climb toward the top of global leaderboards. On October 19, MiniMax stepped forward and admitted the mask was its own: an early build of M3.1 Flash, the company's new code-and-agent model [1][2][15]. Since then it has been live through the MiniMax API, the M Plan subscription and the MiniMax Code app [1][9]. The weights are nowhere to be found. The company says they "will be released later" and has not uttered a word about licensing [1][7]. Any of that would make a serviceable launch post. The interesting part is the shape of the debut: a closed-weight model walked on stage at the exact moment it was about to overtake the open-weight league on independent scoring — then tethered its first week of access to a cloud promotion.
1. The lab behind the mask
MiniMax is a publicly listed Chinese company (HKEX: 00100), and the playbook here is a familiar one: release the model anonymously, let curiosity compound, then drop the official blog post. OpenRouter's community spent weeks guessing who "Space Bunny" belonged to; when the announcement landed, the surprise itself became the press release [1][8]. The same day, MiniMax's X account celebrated the model shipping with "Day0" support on Ollama [8]. That pairing isn't new either — M2 already joined Ollama's cloud back in October 2025 [6].
On the numbers side, the company is positioning the whole "Flash" segment from scratch. The old M3 sat at 29 points on Artificial Analysis' Intelligence Index, costing about $0.51 per task; M3.1 Flash lands at 43 points and roughly $0.22 [11]. MiniMax's "half the price, three times the speed" pitch rests entirely on the distance between those two rows — and for now it is the company's own claim; nobody outside has reproduced it [1][15].
The company's benchmark sheet falls in the same category: 62.2 on Automation-Bench, 73.7 on DeepSWE, 76.2 on BankerToolBench. Then there is Terminal Bench 4.0, where it drops to 38.1 against Opus 5's 51.8 — the quiet corner of that table [1][12][13]. Worth remembering that all of these arrive from a marketing document. The independent table below keeps a different set of books.
2. One model, two speed numbers, one honest read
Speed is the messiest line of this launch. MiniMax's blog post cites a peak above 200 TPS and a P50 north of 170 [1] — measured on the company's own infrastructure, by the company's own instruments. Artificial Analysis, an independent evaluator, had output speed at roughly 93 tokens per second on the evening of October 21, against a class median of 113 [17]. That is about half the advertised rate, sitting below the middle of the pack. (Housekeeping note: the retrievable snapshot of AA's model page is frozen at an older speed value, so the current number comes from its comparison page [10][17]. These measurements drift; this article says "~93 tok/s, accessed Oct 21" and leaves it there.)
The context window carries a similar marketing-versus-practice gap. MiniMax's documentation and Artificial Analysis both cite 1,000,000 tokens [7][10]; Ollama lists the actually usable ceiling at 976K [3]. "1M context" is the product name. 976K is the number left in your account.
Here is the model's pedigree as far as we can actually establish it. Ollama's page shows 428B parameters [3]. That it is a mixture-of-experts architecture with roughly 23B active parameters, sparse attention and a built-in vision encoder comes from MarkTechPost's reporting — MiniMax has published no technical report, so we cannot verify it [12]. Text and image inputs are safe claims; the docs also mention video, but no data shows video working at the Ollama end [3][7][10]. Effort spans five levels from low to max, and reasoning stays on throughout [7]. Integrations written against the old M3 will trip immediately: calls that set thinking.type=disabled or effort=none now return errors, and reasoning text arrives in a separate reasoning_content field — anyone migrating meets this break on the very first call [12].
3. The scorecard: cheap, not fast, and not exactly trustworthy
The class photo, with Artificial Analysis data accessed October 21:
| Model | Intelligence Index | Cost per task | Notes |
|---|---|---|---|
| MiniMax M3.1 Flash (max) | 43 (42 at launch) | ~$0.22 | 4/173; 48% HLE, 22% CritPt; 93 tok/s, below median |
| GLM-5.3-Flash (max) | 42 | ~$0.28 | Neck and neck with M3.1 in launch analysis |
| DeepSeek V4.1 Flash 1017 (max) | 45 | ~$0.26 | Class leader on AA; MIT-licensed open weights |
| Gemini 3.8 Flash (high) | 41 | ~$1.24 | Roughly 5.6x M3.1's task cost |
| MiniMax M3 (previous gen) | 29 | ~$0.51 | The generation before |
| Class median | 13 | — | — |
Table: Artificial Analysis model and comparison pages plus launch analysis [10][11][17] · accessed Oct 21, 2026.
The rows need reading between the lines. The GLM entry is GLM-5.3-Flash (42 points, ~$0.28); the similarly named "GLM-5.3 (Max)" is a different, much bigger model that charges $2.01 per task [11][17]. DeepSeek V4.1 Flash both leads the class and ships MIT-licensed — you can pull the weights and run them on your own metal [11][12]. M3.1 Flash's 43 is genuinely impressive; the price for it is standing in a cloud service's queue at $0.15 per million input tokens and $0.60 per million output [1][3][7].
Then there is the hallucination metric nobody is advertising. On AA-Omniscience, M3.1 Flash attempts 98% of questions and stays 56% correct; among the ones it does not know, it answers wrongly rather than saying "I don't know" in 93% of cases (overall score 16) [11]. As VentureBeat also points out, that does not mean "93% of its answers are wrong" [18]. What it does mean: the model fabricates where it is unsure. If your agent chains cannot afford a silently wrong link, building the verification layer is your job, not the model's.
4. The marketing calendar arrived before the licensing desk
Why cloud-first rather than open weights? No official answer exists; the competitive table tells its own story. DeepSeek publishes under MIT while MiniMax defers its weights to "later" and opens its first week as a three-day free campaign on Ollama's cloud [1][11]. Ollama's model page reads "free until Oct 22, 2026 11:59 PM UTC" — 02:59 Turkey time on October 23 — after which normal metered pricing resumes [3][5]. Not a single extra condition, no quota, no terms, is printed anywhere for the campaign [3][5]. A small note for the record: Ollama's blog carries exactly zero posts about this launch. The entire announcement consists of a model page and two X posts [4][5][6].
"Model weights will be released later." — MiniMax official blog announcement, Oct 19, 2026 [1]
The journalistic weight of that detail fits in one sentence: the free window is a marketing budget, not the model. The actual argument about this model lives in the licensing gap. MiniMax's platform documentation calls the model "open-weight" [7]; Venice's registry lists it as closed/proprietary [13]. Both cannot be true, and no official source will name a license. The previous M3 shipped under a Community License requiring attribution and written permission from anyone earning above $20 million a year; what governs M3.1 has not been stated [13][14]. On Ollama the only tag is :cloud, and the model runs on Ollama's US infrastructure; "zero data retention" stands as Ollama's policy statement [3][4]. "Run it locally" is simply not a sentence you can say today.
What you can responsibly do today is a measured test. At the time of writing, the free window has roughly a day left (closing October 23, 02:59 TRT). A sensible sequence: run one end-to-end coding task through ollama run minimax-m3.1-flash:cloud, then wire it into Claude Code with ollama launch claude --model minimax-m3.1-flash:cloud and hand it a PR-sized job in a real repository [3][4]. Run the same tasks against MIT-licensed DeepSeek V4.1 Flash for comparison. Watch the terminal-heavy jobs and the factual questions in particular — those are the two known weak spots [11][12]. A weight release is expected around October 23; if it lands, the license text should be the first thing you read [13][15].
Murat Karakaya Academy verdict
What the data says: an Intelligence Index of 43 at ~$0.22 per task is a very low price for performance more than triple the class median of 13 — and that is independent measurement speaking [10][11]. The same data draws an equally clear boundary: 56% factual accuracy and a habit of fabricating in 93% of the questions it doesn't know rule this model out of any workflow that lacks verification [11][18]. The company's own benchmark sheet is a bundle of claims with no independent replication; telling the two apart is hard but possible, and we have kept them apart throughout this piece.
Our opinion: this launch is a story about distribution, not model competition. A lab marketed its strongest code model through a masked stealth run, keeps its weights locked, and tied first-week access to a cloud partner's free campaign — functionally neutralizing, for the first week, the run-it-locally advantage that gives the open-weight community any leverage at all. Until a license text exists, this model is a product, not infrastructure. Our advice to readers: blind-test it on a free day, calibrate your impressions against DeepSeek, whose numbers are measurable and whose license is clean, and make the production decision once the weights and the license actually surface.
References
- minimax.io — "MiniMax M3.1 Flash: Redefining Agent Performance and Cost" (Oct 19, 2026) · accessed Oct 21, 2026
- ir.minimax.io — "New Releases" announcement list (Oct 19, 2026) · accessed Oct 21, 2026
- ollama.com — "minimax-m3.1-flash" model page (428B, 976K ctx, pricing, free-until date, commands) · accessed Oct 21, 2026
- x.com/@ollama — Official launch post (Oct 19, 2026; 1M ctx, US-hosted, zero data retention) · accessed Oct 21, 2026
- x.com/@ollama — "3 days free on Ollama's Cloud" (Oct 20, 2026) · accessed Oct 21, 2026
- ollama.com/blog — Blog archive (Oct 2025 M2 post; absence of any M3.1 post) · accessed Oct 21, 2026
- platform.minimax.io — Text generation documentation (1M ctx, effort levels, "open-weight" phrasing) · accessed Oct 21, 2026
- x.com/@MiniMax_AI — Day0 Ollama support + Space Bunny confirmation (Oct 19, 2026) · accessed Oct 21, 2026
- agent.minimax.io — MiniMax Agent product page (M3.1 Flash GA announcement) · accessed Oct 21, 2026
- artificialanalysis.ai — "MiniMax-M3.1-Flash" model page (Index 43, 4/173, ~$0.22/task, 1M ctx; stale cached speed value — current speed per [17]) · accessed Oct 21, 2026
- x.com/@ArtificialAnlys — Launch benchmark analysis (48% HLE, 22% CritPt, AA-Omniscience: 98% attempt rate, 93% wrong answers among unknowns, overall score 16) · accessed Oct 21, 2026
- marktechpost.com — "MiniMax M3.1 Flash" (428B MoE, ~23B active claim, benchmark table, migration notes; Oct 19, 2026) · accessed Oct 21, 2026
- pc.watch.impress.co.jp — Space Bunny reveal, license analysis, M3 Community License terms · accessed Oct 21, 2026
- faradaymachines.com — License gap and cost-per-task analysis · accessed Oct 21, 2026
- unite.ai — "MiniMax Launches M3.1 Flash, Revealing Space Bunny Stealth Model" ("topped global leaderboards" phrasing) · accessed Oct 21, 2026
- daily.dev — Roundup of the free week and the mixed benchmark picture · accessed Oct 21, 2026
- artificialanalysis.ai — "M3.1-Flash vs GLM-5.3 (Max)" comparison page (live output speed; GLM-5.3 (Max) distinct from the Flash variant) · accessed Oct 21, 2026
- venturebeat.com — "MiniMax's new M3.1 Flash beats DeepSeek and Opus 5 on a key business benchmark at lower cost, but with a high hallucination rate" (hallucination metric definition) · accessed Oct 21, 2026
Labels: Murat Karakaya Academy, MiniMax M3.1 Flash, Ollama, AI, Open Weights
