The $0.015 Cache Price: MiniMax M3.1 Flash Makes Context Reuse a Published Rate

Key takeaways

• MiniMax launched M3.1 Flash on October 19, 2026 with a three-row flat price sheet — $0.15 input, $0.60 output, and a published $0.015/1M cached-input rate, with no peak/off-peak windows (MiniMax; Reuters).
• The published cached-input rate is 10% of the uncached list — 90% below it — and the vendor frames overall agentic-task usage cost as 70% lower than its predecessor M3, which OpenRouter's current listing puts at $0.23/$0.96 with a $0.05/M cache-read row (MiniMax; OpenRouter).
• The flash/subagent tier now holds five models at $0.10–$0.15 uncached input — OpenAI GPT-6 Luna, Claude Haiku 5.5, GLM-5.3-Flash, DeepSeek-V4.1-Flash-1017, and M3.1 Flash — with cache mechanics as the tier's differentiator (Anthropic; Z.AI).
• A published cache row still does not state the bill: effective input = hit share × cached rate + miss share × list rate — at the 91.5% hit rate observed on DeepSeek's official endpoint, DeepSeek's sheet computes to ≈$0.017/1M, and the same illustrative hit share applied to MiniMax's sheet computes to ≈$0.0265/1M (DeepSeek pricing; Local AI Zone, aggregator-compiled).
• MiniMax is a listed company (HKEX: 0100.HK), which makes the cached-input row a procurement line item rather than promotional copy — while model weights remain "released later," with community threads titling October 23 and the license still undisclosed (Reuters; MiniMax; r/LocalLLaMA).

On October 19, 2026, MiniMax put three numbers on its M3.1 Flash price sheet: $0.15 per million input tokens, $0.60 per million output tokens, and $0.015 for a cached input token — one-tenth of the list price, published on day one, with no peak or off-peak windowing (MiniMax). Reuters carried the launch from Beijing the same Monday, noting the company's Hong Kong listing, ticker 0100.HK (Reuters). The uncached numbers are competitive but not unusual at the flash tier — five models now sit at $0.10–$0.15 input. What is new is the shape of the announcement: the cached-input rate is in the launch headline, as a flat quoted rate, and it is the number the release post wants you to see.

This article walks that price sheet the way a buyer should read it: what M3.1 Flash actually ships, how its published cache rate compares with the DeepSeek-V4.1-Flash-1017 cache arithmetic we documented yesterday, what still cannot be read off any price sheet, and the procurement moves that follow. The parent article established that cache share and task runtime — not the rate card — set the cost of an always-on agent; MiniMax's launch is the first datapoint of the vendor-published counterpart: a cache rate you can quote from the sheet, with the hit-share caveat attached.

The price sheet, read as a document

MiniMax's launch post states the pricing in one line: "Flat pricing per 1M tokens: $0.15 input · $0.60 output · $0.015 cached input," followed by a three-row table with the same figures (MiniMax). Three properties of that sheet carry operational meaning:

• Flat. DeepSeek's Flash tier prices cache-miss input at $0.15 off-peak and $0.30 peak, with cache-hit input at $0.003 and $0.006 in the same two windows, and every weekend hour and Chinese public holiday counts as off-peak (DeepSeek pricing). MiniMax's sheet has one column and no time windows — there is no scheduling lever to model, and no surprise band when traffic drifts into a peak hour.
• Quoted at launch. The cached-input rate is not buried on a pricing page; it is in the announcement body as one of three headline figures. A listed vendor (HKEX: 0100.HK) publishing a specific rate for a specific token class makes that row a procurement artifact — something a quote can reference.
• Priced at 10% of list. A cached input token costs $0.015 against the $0.15 miss price — a 10:1 ratio. DeepSeek's published cache-hit ratio is tighter still ($0.003–$0.006 against a $0.15–$0.30 miss, 2% of the off-peak miss price), while the predecessor M3's OpenRouter listing shows a cache-read row at $0.05 against a $0.23 input — roughly 22% of list (DeepSeek pricing; OpenRouter). The cache ratios differ; the direction is the same, and the flash tier is converging on the cache row as its competitive surface.

The launch pairs the sheet with capability and throughput claims. MiniMax's post reports DeepSWE v1.1 at 73.7%, OSWorld V2 at 61.7 and Agents' Last Exam at 29.6 — "first among Chinese models on both" — plus BankerToolBench at 76.2 and CyberGym at 82, and describes the release as making online inference "nearly three times faster" with output speed at 200+ tokens per second and median (P50) speed at 170+ TPS (MiniMax). Against the predecessor M3's OpenRouter listing of $0.23/$0.96, the new sheet cuts input 34.8% and output 37.5% while adding the dedicated cache row (OpenRouter).

Two honesty markers belong next to those claims. First, the parameters: MiniMax's own post states no size figure; MarkTechPost's same-day write-up bills the model as a 428B-parameter MoE and puts the weights in late October with the license undisclosed (marktechpost) — we cite it as a secondary claim because the primary does not confirm it. Second, the weights: "Model weights will be released later," the post says plainly; community threads title October 23 (MiniMax; r/LocalLLaMA). Until the weights card exists, the verifiable product is the API and its price sheet — quote the prices, hold the parameter and open-weights claims.

One more datapoint from the launch is worth filing: the post notes that "Space Bunny," an unnamed model that processed more than 200 trillion tokens while operating anonymously, was an early version of M3.1 Flash (MiniMax). Two hundred trillion tokens of real traffic is a meaningful burn-in claim for a model whose serving layer is priced around cache behavior — it is the vendor's argument that the cache-hit economics are already exercised at scale.

Two regimes on the cache row

The interesting comparison is not MiniMax against M3 — it is MiniMax's sheet against DeepSeek's, because the two represent the two ways a cache row can reach a buyer's spreadsheet.

Regime one: the published list rate. MiniMax publishes $0.015/1M for cached input. Your cost model reads it directly. What the sheet cannot tell you is the hit share your workload will earn — the vendor discloses no cache-hit rate, no minimum cacheable prefix, and no invalidation policy on the launch page (MiniMax).

Regime two: the published hit price plus an observed rate. DeepSeek publishes cache-hit input at $0.003 off-peak and $0.006 peak, and the observed hit rate on the official endpoint — 91.5%, compiled from OpenRouter data by a third party and labeled as one endpoint's observation, not a guarantee — lands the effective input price near $0.017/1M (DeepSeek pricing; Local AI Zone, aggregator-compiled).

Both regimes resolve to the same arithmetic:

Effective input price per 1M tokens = hit share × cached rate + miss share × uncached rate.

At an illustrative 91.5% hit share, the two sheets compute as follows — with the standing caveat that the 91.5% figure is a DeepSeek-endpoint observation being used here as arithmetic filler, not as a claim about MiniMax's serving behavior:

Sheet	Cached rate	Uncached rate	Effective at 91.5% hit share	1B input tokens/month
MiniMax M3.1 Flash (flat)	$0.015	$0.15	$0.0265	$26.47 vs $150.00 uncached
DeepSeek-V4.1-Flash-1017, off-peak	$0.003	$0.15	$0.0155	$15.50
MiniMax at 100% hit share (floor)	$0.015	$0.15	$0.0150	$15.00
DeepSeek-V4.1-Flash-1017, peak	$0.006	$0.30	$0.0310	$31.00

The table's honest reading: MiniMax's published rate is quoted at 10% of list and wins on simplicity, while DeepSeek's published hit price is tighter at 2% of the miss but conditional on time windows and on an observed hit share no competitor can promise. At equal hit share, DeepSeek's off-peek effective lands about 41% below MiniMax's; all-cached traffic on MiniMax's floor ($0.015) beats DeepSeek's off-peak effective because $0.015 is a published floor with no scheduling condition. Neither sheet states the number that matters most — the hit share your context structure will earn — and that is the point: the cache row is now quotable, and the hit rate still prices the agent.

The published-rate pattern appears to be spreading rather than staying a one-vendor event. OpenRouter's comparison listing for InclusionAI's Ling 3.1 Flash — vendor materials published October 16, compiled by the aggregator — shows input at $0.075/M and a cached-input row at $0.015/M (OpenRouter, aggregator-compiled — treat as secondary until the vendor sheet is verified). One row is a datapoint; three inside a week, across three vendors, is a tier convention.

The five-model flash tier, one table

The launch's family context: GPT-6 Luna and Claude Haiku 5.5 already sat at $0.10/$0.50, GLM-5.3-Flash at $0.15/$0.50, and DeepSeek-V4.1-Flash-1017 at $0.15/$0.60 off-peak with the cache-hit rows quoted above (Anthropic; Z.AI; DeepSeek pricing; GPT-6 Luna's pricing is documented in the Haiku 5.5 article). With M3.1 Flash, the tier reads:

Provider · model	Input (per 1M)	Output (per 1M)	Cached-input rate in this article's sources	Weights
OpenAI · GPT-6 Luna	$0.10	$0.50	not quoted here	Proprietary
Anthropic · Claude Haiku 5.5	$0.10	$0.50	not quoted here	Proprietary
Z.AI · GLM-5.3-Flash	$0.15	$0.50	not quoted here	Open (released)
DeepSeek · V4.1-Flash-1017	$0.15 off-peak / $0.30 peak	$0.60 / $1.20	$0.003 / $0.006 (cache-hit)	MIT, day one
MiniMax · M3.1 Flash	$0.15	$0.60	$0.015 cached (flat)	Announced, unreleased

Cache rates are quoted only where this article's sources state them; the vendors marked "not quoted here" publish cache pricing on their own rate cards — verify against each price sheet before quoting.

The sticker input range has compressed to a single octave — $0.10 to $0.15 — and every incumbent in the table was competitive within that band days before M3.1 Flash shipped. There is no sticker-price reason to switch. The reasons to switch, if any, now live in the rows around the sticker: the cache ratio, the time-window structure, the hit share the serving stack delivers on your context shape, and the honesty of the weights story.

What this changes for procurement

For a team running always-on agent workloads, four moves follow from this launch:

1. Make the cache row a line item in every model quote. Ask each vendor for the same three numbers: uncached input, cached input, output — plus the mechanics the sheet rarely states (minimum cacheable prefix length, invalidation behavior, any hit-share instrumentation exposed on the API). MiniMax's flat sheet is the cleanest current template; DeepSeek's time-windowed sheet is the cautionary example for modeling cost, not for quality.
2. Price the workload from the hit share, not the published row. The formula is four terms: hit share × cached rate + miss share × uncached rate, plus output rate × tokens per task. Measure the hit share on your traffic — the parent article's context-shaping discipline is what turns a cache-friendly rate card into a cache-earned bill.
3. Structure context for reuse before you shop the rate. Stable system prompts and tool schemas up front, retrieved documents placed before volatile user content, no per-step prefix rewrites — the same levers that produced the observed 91.5% band on the DeepSeek endpoint. A flat $0.015 cached rate is only ten cents cheaper than the sticker if your traffic hits the cache.
4. Sequence the weights question explicitly. M3.1 Flash ships API-first with weights "released later" — community threads say October 23 and the license is undisclosed (MiniMax; r/LocalLLaMA). If your build requires pinned, self-hostable checkpoints — the pattern the model-swap article prescribes — treat the weights card as a gate before routing production traffic, and log the served model on every response in the meantime.

The one-minute diagram below puts the launch's economics on one page:

The bars are the launch in one page: the cached-input rate is an order of magnitude below the sticker, and the sheet that published it makes the hit-share arithmetic explicit for anyone comparing tiers.

A representative build

A mid-market industrial distributor runs a 24/7 intake-and-quote agent that re-reads three supplier catalogs and a pricing-rules pack on every RFQ cycle — the access pattern cache tiers are built for. The build uses an MCP module for the ERP and catalog layer, an A2A delegation path to supplier quoting agents, and a routing layer that keeps a frontier model for negotiation and routes bulk intake to the flash tier. With prefixes pinned, the effective input price is read from the hit share the agent's logs report, not from the sticker: at a high hit share, a published cache row in the M3.1 Flash class prices 1 billion monthly input tokens near $26 instead of $150, and the routing layer re-checks that arithmetic on real telemetry every week.

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Related reading

• The $0.017 Agent: DeepSeek's 1017 Snapshot Cuts Task Cost Without Cutting Prices — the parent article: how an observed 91.5% cache-hit rate puts DeepSeek's effective input near $0.017 while every list price stays put
• Inference Economics: Why Always-On Production Agents Are Now Affordable — the per-token cost collapse and the 10:1 integration-to-model spend ratio both cache regimes sit inside
• The $0.10 Subagent: Claude Haiku 5.5 Collapses the Frontier-vs-Open-Weight Cost Gap — the per-token path to cost collapse and the subagent-tier pricing strategies this cache-row comparison extends