Kimi K3.1 Pro vs MiniMax M3.1 Flash: A 15× Price Gap That Shrinks Every Time the Job Gets Bigger

Line up Kimi K3.1 Pro and MiniMax M3.1 Flash on a price sheet and the decision looks finished before it starts: one costs fifteen times what the other does per blended million tokens [VENDOR prices; the ratio is our arithmetic]. Line them up on a per-task ledger instead and the gap has collapsed to a factor of nine and a half, then to about three. None of the vendor prices moved between those readings. The denominator did. These two are not competing for the same row of your budget, and the fastest way to choose is to work out which row your workload touches.

Two profiles that were never aimed at the same buyer

Kimi K3.1 Pro is a sparse model with 2,800B total parameters and 104B active per token [VENDOR], a 1M-token context [VENDOR], and thinking that is always on: effort runs low, high or max, defaulting to max, while temperature and top_p are fixed [VENDOR]. It is not open weights today; the vendor promises a release by 4 November [VENDOR], and Artificial Analysis still marks it proprietary [INDEPENDENT]. On that index it records 57.5 output tokens per second [INDEPENDENT] and a score of 55.09 [INDEPENDENT].

MiniMax M3.1 Flash shipped on 2026-10-19 [VENDOR] as the opposite kind of object: a 1,048,576-token context with a maximum output of 524,288 tokens [VENDOR], 116.2 tokens per second [INDEPENDENT], and a score of 42.71 [INDEPENDENT]. One rounding note: Artificial Analysis rounds that to 43, our catalogue card to 42 [INDEPENDENT] — the same measurement.

The framing that survives the first paragraph is therefore not "frontier model versus budget model." It is a deep-thinking generalist with an execution-heavy profile against a fast, long-context, cheap-throughput model that is a week old. The 12.38 index points between them [INDEPENDENT] are real, but unevenly spread, and that unevenness is the decision.

Where the index gap actually comes from

Split the index into its rows and a pattern appears: Kimi K3.1 Pro's advantage is concentrated in rows that reward sustained execution rather than stored knowledge. All figures below are [INDEPENDENT], from Artificial Analysis.

• Terminal-Bench 4.0: 67.17% against 25.76%, a 41.4-point gap and the largest separation in the set.

• Terminal-Bench-Science: 34.29% against 3.33%, a 31.0-point gap. Low single digits are not a rounding artefact; that is a model largely failing the task family.

• GDPval-AA: 1,845.72 against 1,457.57, a difference of 388 Elo. Elo is not linear in capability, so read this as a different class of result, not a percentage.

• SciCode: 60.76% against 53.36% — genuinely competitive.

• AA-LCR 86.33% against 79.67%; MMMU-Pro 83.87% against 79.13%; CritPt 24.57% against 22.29%; HLE 52.46% against 47.96%. These are the rows where the two are nearest each other, and all are long-context or knowledge-dense.

• AutomationBench: 73.30% against 72.52% — effectively tied.

• AnalystAgent sits at 38.75 for Kimi K3.1 Pro and FrontierCode at 47.07%, both with no published MiniMax counterpart. An absent result is not a zero and not evidence of anything, except that the comparison has holes.

The row that complicates every simple story is AA-Omniscience. MiniMax M3.1 Flash answers more knowledge questions correctly — 56.20% accuracy against 51.02% — yet scores 15.55 against 29.52, because its hallucination rate is 92.81% versus 43.89% [INDEPENDENT]. The index penalises confident wrong answers. A model that is right slightly more often and wrong with far more confidence is a hazard for retrieval-grounded work, where an unsupported sentence is worse than an admission of ignorance.

Pricing, read at three denominators

At the token level the two are not in the same market. Kimi K3.1 Pro lists at $2.00 input and $10.00 output per million tokens, cache reads at $0.20, blended 3:1 at $4.00 [VENDOR]. MiniMax M3.1 Flash lists at $0.15 input and $0.60 output, cache reads at $0.015, blended 3:1 at $0.2625 [VENDOR]. The blended ratio is 15.2×.

Per completed index task the ratio falls to 9.6×: $2.1145 for Kimi K3.1 Pro against $0.2201 for MiniMax M3.1 Flash [INDEPENDENT]. The reason is mechanical. One index task consumes 55,195 output tokens from Kimi K3.1 Pro and 78,612 from MiniMax M3.1 Flash — 42% more output from the cheaper model, which eats into its own advantage at the expensive end of the sheet. Cost per task is the number a finance team should look at, and it is already a third smaller than the headline ratio suggests.

Inverted, points of index per blended dollar come to 13.8 for Kimi K3.1 Pro and 162.7 for MiniMax M3.1 Flash [INDEPENDENT]. That is not a recommendation: if the only constraint is maximum benchmark per dollar, the answer is unambiguous, and if it is completing this class of task at all, the arithmetic is beside the point.

Why the gap keeps collapsing as the job gets bigger

The cleanest evidence is Plotly's DataAnalyticsBench — 43 questions over synthetic wind-farm data, one run per model, so a direction rather than a distribution [INDEPENDENT]. Kimi K3.1 Pro answered 42 of 43 at $0.31 per question, $13.47 total, using 7.5 model calls and roughly 440,000 input tokens per question. MiniMax M3.1 Flash answered 41 of 43 at about $0.10 per question, $4.09 total, using roughly 45 calls and 4.2 million input tokens per question.

Three things happen at once. Accuracy is nearly identical — one question apart, well inside what a single run resolves. The price gap has narrowed to 3.1×. And the mechanism is visible: the cheaper model gets there by taking 45 calls where the other takes 7.5, and pushing 9.5× the input per question. It buys accuracy with context and iterations rather than parameter depth, and both are cheap per token but not free per task.

Scale to a hundred thousand questions a month and the comparison is $31,000 against $10,000, roughly $252,000 a year apart [INDEPENDENT, extrapolated from Plotly's per-question figures]. That is a real budget line, and the volume at which to price the 41-versus-42 accuracy difference per wrong answer: a two-point edge on a task your business depends on can be worth more than a quarter of a million dollars.

Where the cheap tier genuinely wins

MiniMax M3.1 Flash is the better buy for workloads sharing a shape: high volume, short or loosely constrained outputs, heavy retrieval context, and tolerance for a re-run. Classification, schema-constrained extraction with a validator downstream, summarisation at scale, retrieval-augmented answering where every sentence must trace to a supplied passage, and bulk generation with review. Throughput helps: 116.2 tokens per second against 57.5 [INDEPENDENT] is roughly twice the concurrency per replica before you queue, and the 524,288-token maximum output [VENDOR] means long-form jobs need no stitching.

The hallucination figure constrains that list rather than decorating it. A 92.81% rate [INDEPENDENT] is survivable when ground truth sits in the prompt and a validator rejects unsupported claims. It is not survivable in an open-ended factual assistant with no retrieval, no citation check and no human in the loop — and its slightly higher accuracy will make those failures harder to notice, not easier.

Where the expensive tier pays for itself

Kimi K3.1 Pro earns its price on rows that look like work rather than recall. Terminal-Bench 4.0 at 67.17% and Terminal-Bench-Science at 34.29% are the clearest signals: long-horizon shell and coding tasks where a partial answer is a failed run and each retry costs engineering attention rather than tokens [INDEPENDENT]. GDPval-AA's 388-Elo separation [INDEPENDENT] points the same way for professional work. If your workload is an agent that must finish a multi-step procedure unsupervised, a 25.76% Terminal-Bench 4.0 score [INDEPENDENT] is not a discount — it is a different product.

Two operational details belong here rather than in a footnote. Thinking is always on with effort defaulting to max [VENDOR], so latency is not something you dial down without accepting a capability change; plan for a p50 of about 6.9 seconds rather than a fast path that does not exist. In production on our route over the seven days to 26 October, Kimi K3.1 Pro ran at 52.4 output tokens per second with a 0.064% error rate and carried 1.305B tokens in three days [OURS]. MiniMax M3.1 Flash carried only about 10,000 tokens on our route in that window [OURS], far too little to measure anything, so every latency and cost figure for it here, throughput included, is the evaluator's.

Choosing between them

Start from the evaluation row that most resembles your traffic, not from the index headline or the price sheet. If the answer rests on Terminal-Bench, GDPval or any long-horizon execution family, the 41-point and 388-Elo separations settle it before cost is discussed. If it rests on AA-LCR, MMMU-Pro, CritPt or AutomationBench, the two are within a few points and you should choose on price, throughput and context — which is to say MiniMax M3.1 Flash, with a retrieval layer and a validator managing the hallucination profile.

The middle case is bounded analytical work with a checkable answer. Plotly's 42-of-43 against 41-of-43 [INDEPENDENT] has that shape, and a 3.1× gap on a task where the cheaper model needs 9.5× the input to stay level is a genuine trade. Sample your own traffic first; a single 43-question run is a direction, not a verdict.

One structural note on pricing, which matters more than any ratio above. OrcaRouter passes vendor token prices through unchanged, so when a list price changes, our price for that model changes with it the same day, with no renegotiated tier in between. Every ratio here is a snapshot of two vendors' current sheets, not a markup schedule — if either vendor cuts output pricing next month, the 15.2× and the 9.6× both move while billing stays a mirror of theirs. It is also why the cheap tier's advantage is worth rechecking rather than memorising: MiniMax M3.1 Flash shipped on 19 October [VENDOR], a week before this was written, and launch pricing is rarely final pricing.

Sources: Artificial Analysis Intelligence Index v4.3.2, data as of 26 October 2026, for index, row, throughput and per-task cost figures; Plotly's DataAnalyticsBench write-ups of 20 and 23 October for the 43-question comparison; vendor model cards for prices, context limits, parameters and release dates. MiniMax M3.1 Flash carried roughly 10,000 tokens on OrcaRouter's route in the seven-day window, too little to measure, so its latency and cost figures here are the evaluator's.