MiniMax M3.1 Flash Review: Benchmarks, Pricing, and the DeepSeek Showdown

By Muhammad Ramzan Aatish

MiniMax M3.1 Flash arrived four days ago with a simple pitch: frontier-agent performance at a fraction of the cost, and open weights to prove it. The weights landed on Hugging Face on schedule, the price card undercuts nearly everything in its class, and DeepSeek answered two days earlier with its own flash model. So this review does what the launch coverage mostly skipped. It puts MiniMax M3.1 Flash next to DeepSeek V4.1 Flash on price and benchmarks, marks every number MiniMax reported as exactly that, and surfaces what early testers found once the hype settled.

Two flash models, one price war

The timing is the story as much as the model. DeepSeek shipped V4.1-Flash-1017 on October 17. MiniMax answered on October 19 with M3.1 Flash. Both are cheap, fast, agentic models aimed at the same buyer: anyone running AI agents at scale who winces at the invoice. Artificial Analysis framed it well, noting the axis of competition has moved from capability to cost per completed task. That single sentence explains this whole release cycle better than any benchmark table.

The bet behind MiniMax M3.1 Flash is simple. Take a 428-billion-parameter mixture-of-experts model with roughly 23 billion active parameters per token, give it a million-token context window, make reasoning always-on with five effort levels, and price it at $0.15 per million input tokens flat. No tiers, no peak hours, no long-context surcharge. If you run agents, that flatness matters more than it sounds, and the pricing section below shows exactly why.

There is also a fun origin story. For about three weeks before launch, an anonymous model called “Space Bunny” was quietly processing over 200 trillion tokens on OpenRouter. Community testers had already linked it to MiniMax by probing its tokenizer. On launch day MiniMax confirmed Space Bunny was an early MiniMax M3.1 Flash build, which means the model had a public shadow trial before anyone knew its name. Michael Leung, an independent tech writer, ran that preview inside OpenCode for most of an Outlook-clone build and found it fast and good, though his own speed test on the free tier came in slower than MiniMax’s claims. That gap between claimed and measured speed keeps showing up, so keep it in mind.

What MiniMax M3.1 Flash actually is

Under the hood it is the same size class as the original M3: 428B total parameters, about 23B active. The “Flash” part refers to inference speed and serving cost, not size, and confusing those two is the single most common mistake in the discussion threads. More on that below.

MiniMax credits four efficiency tricks for the speed: more aggressive sparsity at inference (MiniMax Sparse Attention), DSpark speculative decoding, quantization-aware training, and power-aware scheduling. Inputs can be text, image, or video; output is text, up to 524,288 tokens per OpenRouter’s listing. Reasoning cannot be turned off. There are five effort levels from Low to Max, Max is the recommended default, and requests that try thinking.type=disabled or effort=”none” get a 400 error. One practical consequence: integrations that parsed inline <think> tags from M3 need updating, because reasoning now arrives in a separate reasoning_content field.

MiniMax also teased a bigger M3.1 Pro coming later. No specs, no date, and nothing here depends on it.

MiniMax M3.1 Flash benchmarks: the vendor’s numbers, with the asterisks

Every MiniMax M3.1 Flash benchmarks figure below comes from MiniMax itself. MarkTechPost and SiliconAngle both note that no independent replication exists yet. Treat the table as the vendor’s case, not a settled ranking.

Scrolling through the r/LocalLLaMA release threads, the mood was easy to read: excitement about the price and the weights, held in check by exactly this caveat. People wanted the numbers and distrusted them at the same time. That is the right instinct, because the fine print has real conflicts.

Three conflicts deserve daylight. First, Humanity’s Last Exam: one outlet reports 48% for MiniMax against DeepSeek’s 43%, while MarkTechPost’s table shows 43.4 “without tools.” Those are almost certainly different HLE configurations, so there is no single HLE number to quote. Second, MiniMax’s own table lists DeepSeek at 74.2 on DeepSWE while DeepSeek’s own card says 77.0; use DeepSeek’s figure when comparing, since it comes from the lab itself. Third, MiniMax’s cost chart plots 62.8 on Automation-Bench at max effort while the table says 62.2, a small internal inconsistency MarkTechPost flagged. And the speed claim: MiniMax advertises 200+ tokens per second in everyday use, but Artificial Analysis measured roughly 105 TPS and NYU Shanghai measured 123 against a 116 average. Vendor marketing, meet independent measurement.

The honest read: genuinely strong on agentic office work and computer use, mid-pack on coding, weak on terminal-bench style tasks. Reading head-to-head model comparisons has trained me to look for exactly these lopsided profiles, and this one is lopsided in an interesting way. One more limitation worth stating plainly: independent analysis from kie.ai found the model has an unusually high tendency to generate a wrong answer rather than admit uncertainty. For supervised coding help that is a nuisance. For unsupervised agents it is a real hazard.

MiniMax M3.1 Flash vs DeepSeek V4.1 Flash on price

MiniMax M3.1 Flash vs DeepSeek V4.1 Flash is really two fights: the per-token price and the per-task cost. On per-token pricing the m3.1 flash pricing card is refreshingly simple. Flat $0.15 per million input tokens, $0.60 per million output, $0.015 per million cached-input tokens. DeepSeek is trickier: $0.30/$1.20 at peak hours, half that off-peak. So at night and on weekends the two cost exactly the same per token. At peak hours MiniMax is half the price. DeepSeek’s cache pricing is cheaper though, $0.003 per million against MiniMax’s $0.015, a fivefold gap that can flip the math on long-context agent loops.

The per-task picture favors MiniMax slightly. Artificial Analysis estimates about $0.22 per index task for MiniMax against $0.26 for DeepSeek, mostly because MiniMax emits fewer output tokens per task. MiniMax’s own framing is punchier: “70% lower overall usage cost than M3” and “1/3 the cost of M3” in different materials, though the company has not fully shown its math and straight per-token arithmetic gives one-half to one-quarter depending on context length. Treat the 70% as marketing until someone reproduces it.

Two non-price differences matter. DeepSeek’s weights are MIT-licensed, the most permissive option available. MiniMax’s are not, which the next section covers. And DeepSeek’s thinking mode can be disabled while MiniMax’s reasoning is always on, which changes integration work. OpenAI made a similar aggressive pricing move with GPT-6 Sol and Luna at half price, so the whole industry is repricing at once. For the bigger picture on where agent economics are heading, our complete guide to autonomous AI systems is worth a read.

The license is the part nobody read

The MiniMax M3.1 Flash weights landed on Hugging Face on schedule, with FP8 builds for vLLM, SGLang, and Docker Model Runner plus same-day community quants for llama.cpp, Ollama, and LM Studio. Day-one chip support reads like a trade show roster: Huawei Ascend, NVIDIA, AMD, and several Chinese accelerators, with Together AI, Fireworks, Nebius, Baseten, Modal, and DigitalOcean on the cloud side.

But the license is MiniMax Community, not MIT, and the difference is not cosmetic. Under $20M in annual revenue you need a notification email and a visible “Built with MiniMax M3.1-Flash” credit. Above $20M you need written authorization. Military uses are prohibited outright. Compare that with DeepSeek V4.1 Flash under MIT, which asks for nothing. If you are deploying AI agents locally or building a product on the weights, read the actual license text before you commit. “Open weights” and “open source” are not the same sentence here.

“Flash” does not mean small

Worth correcting directly, because it keeps circulating: “Flash” describes inference speed and serving cost, not parameter count. This is a 428B model. If you could run M3, you can run this; if you could not, the name changes nothing. One r/LocalLLaMA commenter put it well, calling the name kinda misleading for exactly this reason. The VRAM debate in the same threads makes the point concrete: these are data-center-class weights, not something for a laptop GPU, whatever the branding suggests.

What people who ran it say

Beyond Michael Leung’s OpenCode test, the early verdicts cluster in two places. On coding, a heavy Space Bunny user on r/LocalLLaMA judged it “fast, but it’s not as fast or as capable as DS4.1 flash,” which lines up with the DeepSWE and Terminal-Bench numbers above. On the NVIDIA DGX Spark forums, local-inference users were already framing the live question as how it performs against DeepSeek’s 1017 build specifically. Nobody I found called it bad. Several people called it the cheaper way to do agent work they were already doing, which is arguably the entire product thesis in one sentence.

Against other recent head-to-head comparisons, the pattern here is familiar: the cheaper model wins the value argument while the coding crown stays contested. Early days, vendor numbers, small sample. But the direction of the community verdict is consistent.

Before you switch: the checklist

Six practical things, each checkable:

1. Route by the clock. If your agents run at peak hours, MiniMax’s flat $0.15 input price beats DeepSeek’s $0.30 peak. Off-peak they tie per-token, so schedule batch work accordingly.
2. Do the cache math. Heavy DeepSeek cache users should not assume MiniMax is cheaper: $0.003 vs $0.015 per million cached tokens is a 5x gap that compounds in long agent loops.
3. Fix M3 integrations first. Anything sending thinking.type=disabled or parsing inline <think> tags will error. Update before migrating, not after.
4. Try it free. OpenRouter’s free endpoint and Vercel’s AI Gateway both carry the model at no cost. There is no reason to pay before testing.
5. Check the license against your revenue. The $20M threshold, the attribution requirement, and the military-use ban are real constraints. Not MIT.
6. Keep humans in the loop. The uncertainty-avoidance finding matters: this model would rather answer wrong than abstain. Fine for drafts, risky for unsupervised actions. Enterprise agent failures usually start with exactly this kind of overconfidence.

The bottom line

MiniMax M3.1 Flash is the clearest sign yet that the agent-model market now competes on cost per task rather than raw capability. The pricing is genuinely aggressive, the open weights are real with a license worth reading, and the agentic benchmarks are strong. All of it vendor-reported, all of it awaiting independent confirmation. Against DeepSeek V4.1 Flash it wins on peak-hour pricing and loses on coding scores and license permissiveness. If your workload is agents at scale, try the free tier and run your own evals. Nothing in this release cycle rewards taking anyone’s numbers on faith, including MiniMax’s.

FAQ

What are the MiniMax M3.1 Flash benchmarks?
The vendor-reported highlights: Automation-Bench 62.2, BankerToolBench 76.2, OSWorld V2 61.7, DeepSWE 73.7, Terminal Bench 38.1, and an Artificial Analysis Intelligence Index of 42. No independent replication exists yet, so treat these as MiniMax’s numbers.

Is it actually open source? Where are the weights?
Weights are on Hugging Face, released on schedule. But the license is MiniMax Community, not MIT: notification plus credit under $20M revenue, written permission above it, no military use. Less permissive than DeepSeek’s MIT release, and worth reading before building on it.

Can I run it at home?
Probably not the full model. 428B parameters in FP8 is data-center hardware even quantized, though community builds for Ollama and LM Studio exist for the determined. The “Flash” name is about speed and cost, not size.

MiniMax M3.1 Flash vs DeepSeek V4.1 Flash — which is cheaper?
Per token: MiniMax is flat $0.15/$0.60; DeepSeek matches that off-peak but doubles it at peak. DeepSeek’s cache hits are cheaper though. Per completed task, Artificial Analysis estimates $0.22 for MiniMax vs $0.26 for DeepSeek. On the m3.1 flash pricing question overall: MiniMax wins at peak hours, ties off-peak, loses on cache-heavy workloads.

Which one codes better?
DeepSeek, on the vendors’ own scorecards: 77.0 vs 73.7 on DeepSWE, 42.3 vs 38.1 on Terminal-Bench. MiniMax leads on agentic office benchmarks instead. Early community testers agree with that split. Nothing independently verified yet.

Why was it called Space Bunny?
That was its anonymous codename on OpenRouter for about three weeks before launch, where it processed 200T+ tokens. MiniMax confirmed the connection on release day. Fun detail, zero technical significance.

Is there a free way to try it?
Yes. OpenRouter’s free endpoint and Vercel AI Gateway both list it. The paid routes are MiniMax’s API, the M Plan subscription, and the MiniMax Code desktop app.

Sources

Every figure above traces to a named source. The benchmark claims are the vendors’; the measurements belong to whoever ran the test.

MiniMax official launch post, “MiniMax M3.1 Flash: Redefining Agent Performance and Cost” — specs, pricing, benchmarks, weights plan. minimax.io
SiliconAngle, October 19 — 70% cost claim, 200 TPS claim, OSWorld/BankerToolBench/CyberGym results, Space Bunny confirmation. siliconangle.com
MarkTechPost, October 19 — full benchmark table, reasoning mechanics, pricing math, comparison table, limitation notes. marktechpost.com
VentureBeat, October 23 — HLE 48% vs DeepSeek 43% (per Artificial Analysis), AA-Omniscience 56% vs 43% with 93% hallucination-rate nuance, per-task cost $0.22 vs $0.26. venturebeat.com
The Decoder, October 19 — AA Intelligence Index 42 vs DeepSeek 45, HLE 48%, CritPt 22%, AA-Omniscience 56%, $0.22/task. the-decoder.com
kie.ai deep-dive — AA-measured speed (~105 TPS), uncertainty-avoidance limitation, community comparisons. kie.ai
eesel.ai review — preview-to-GA changes, effort “none” 400 error, community quotes. eesel.ai
NYU Shanghai — AA measured 123 TPS, weights-date plan. rits.shanghai.nyu.edu
MiniMax M3.1 Flash on Hugging Face — MiniMax Community License text: $20M revenue threshold, military-use ban; weights release. huggingface.co
ChainCatcher, October 23 — open-source announcement, 100T tokens in 72 hours, chip and cloud partners. chaincatcher.com
aifrontier.co.jp — DeepSeek V4.1-Flash-1017 release, in-place swap, DeepSeek card benchmarks, MIT license. aifrontier.co.jp
DeepSeek official API docs — V4.1-Flash-1017 peak/off-peak pricing ($0.30/$1.20 peak, half off-peak), cache pricing. api-docs.deepseek.com
Michael Leung — hands-on Space Bunny test in OpenCode, free-tier speed measurement. michaelleung.info
r/LocalLLaMA — release and weights threads, community tester verdicts. reddit.com
getshint.com — free access routes via OpenRouter. getshint.com
techseen.com — Vercel AI Gateway free listing. techseen.com