Kimi K3.1 Pro vs DeepSeek V4.1 Flash: Sixteen Index Points for Thirteen Times the Price

DeepSeek V4.1 Flash costs $0.15 per million input tokens and $0.60 per million output on our catalogue. Kimi K3.1 Pro costs $2.00 and $10.00 — 13 times as much on input, 17 times on output. On the independent index, that price buys 16 points: Artificial Analysis scores Kimi K3.1 Pro at 55 and DeepSeek V4.1 Flash at 39. It also buys a different shape of model — DeepSeek is 2.5 times faster and more than twice as verbose, so the per-token gap and the per-task gap are not the same number. This is the cleanest capability-for-money trade in the current catalogue, and the interesting part is that it does not point the same way on every task.

Both models carry a million-token context, both accept images, and both generate output in the hundreds of thousands of tokens. One is a Chinese lab's frontier flagship released in October; the other is the small model in DeepSeek's architecture family, released on 10 September 2026 and positioned as the production successor to its earlier Flash models. The comparison is really about what 16 index points are worth to a specific workload — and about the one benchmark where the cheap model wins.

The rate cards, and what a real workload costs

• Input — $2.00 per million tokens on Kimi K3.1 Pro against $0.15 on DeepSeek V4.1 Flash.

• Output — $10.00 against $0.60.

• Context window — 1,048,576 tokens on both.

• Maximum output — 943,718 tokens on Kimi K3.1 Pro, 384,000 on V4.1 Flash's base endpoint and 393,216 on the 1017 snapshot. Both are large enough that output length stops being the constraint.

• Weights and size — DeepSeek V4.1 Flash is a 552-billion-parameter mixture-of-experts model with 16 billion active parameters, published on Hugging Face under an MIT licence. Kimi K3.1 Pro is 3T-class and its weights are promised by 4 November, with the licence not yet published.

• Speed — 181 output tokens per second on the evaluator's provider for DeepSeek, against 73.5 for Kimi K3.1 Pro. For bulk work this is the second-largest difference after price.

The arithmetic is brutal at volume. A call that reads a million input tokens and writes 200,000 output costs $4.00 on Kimi K3.1 Pro and 27 cents on DeepSeek V4.1 Flash. A nightly pipeline that processes 2,000 long documents a month at 50,000 input and 5,000 output tokens each spends roughly $300 on Kimi and $21 on DeepSeek. For classification, extraction, rewriting and summarisation at scale, the 16-point gap rarely shows up in a way a customer notices; the invoice does.

Two caveats keep that from being the whole story. Artificial Analysis, which measured both models through a provider rather than at list price, records DeepSeek V4.1 Flash at $0.295 input and $1.20 output per million tokens — twice our catalogue rate, and a reminder that the cheapest number in a comparison is the one nobody actually pays. More importantly, its per-task cost is $0.48 against Kimi K3.1 Pro's $2.11: a 4.4× saving, not the 15× the token prices imply, because DeepSeek generated 250 million output tokens during the index run against Kimi's 150 million. Verbosity spends the price advantage, and both models are well above the median.

Sixteen points is not a rounding error

The same margin that does not matter for a classification job matters enormously for anything with a plan in it. The independent index is an average across ten evaluations spanning knowledge, reasoning and agentic work; a 16-point gap on that average means the hard end of the distribution is not close. On Moonshot's launch table, Kimi K3.1 Pro scores 61.2% on long-horizon coding and 65.7% on Terminal-Bench 4.0; the Flash tier is not represented on those rows at all.

Practical translation: if the task can be specified in a prompt and checked by a validator, buy the cheap model and spend the savings on retries. If the task requires deciding what to do next — reading a failing test, choosing a file to edit, recovering from a bad tool call — the 16 points are the difference between an agent that finishes and one that loops.

The one row where the cheap model wins

Moonshot's own benchmark sheet contains a row that cuts against it. On xdev-bench-hard at pass@3, a vendor-run adversarial coding evaluation, DeepSeek V4.1 Flash scores 85.7% against Kimi K3.1 Pro's 81.0%. Kimi K3, the predecessor, scores 33.3%.

The reason is visible elsewhere in the launch material: Moonshot removed cyber-related data from both mid-training and reinforcement learning before shipping K3.1 Pro, and published the cost. A checkpoint trained without those measures scores 90.5% on the same benchmark; the delivered model scores 81.0%. DeepSeek, by contrast, has not announced an equivalent capability reduction, and its 85.7% lands above the model that costs thirteen times more.

For defensive security work — scanning, triage, patch drafting under review — that row is worth weighing. For offensive-adjacent work, both vendors' terms and the law apply regardless of the score.

The context and output surprise

One practical note before the comparison gets too tidy: Artificial Analysis marks the base V4.1 Flash as deprecated in favour of the V4.1 Flash 1017 snapshot, the fixed October 17 checkpoint our catalogue also serves at the same price. The index figure of 39 is for the base model; the snapshot is what new work should target, and it carries the same context window with a slightly larger output ceiling.

What makes this comparison less lopsided than the price suggests is that the cheap model did not trade away the warehouse. A million-token context on a $0.15 model means retrieval-augmented pipelines that previously needed chunking can send the whole document. A 384,000-token output ceiling means a batch job can ask for an entire rewritten file in one call.

What it does trade away is reliability at the edges: instruction-following on long multi-step prompts, tool-call recovery, and the ability to hold a plan across turns. Those are exactly the things Kimi K3.1 Pro's post-training budget was spent on, and exactly what a general index compresses into 16 points.

How to split the work

Both models are callable through OrcaRouter at their providers' list prices with no markup added — $2.00 and $10.00 for Kimi K3.1 Pro, $0.15 and $0.60 for DeepSeek V4.1 Flash — on one key, with automatic failover if one provider stalls.

A split that follows the evidence: send extraction, classification, translation, summarisation and first-pass drafting to V4.1 Flash, and send long-horizon coding, agent orchestration and anything that has to recover from its own mistakes to Kimi K3.1 Pro. If the split lands at the usual 80/20 by token volume, the blended cost per million tokens falls to roughly 80 cents — a fifth of an all-Kimi baseline — while the tasks that need the frontier model still get it.

The mistake to avoid is treating either model as the answer to both jobs. At a 13× price gap, the cheap model is not a compromise for bulk work; it is the correct choice. At 16 index points, it is not a substitute for the hard 20%.