MIDM K 3.0 Pro vs DeepSeek V4.1 Pro: One Name Has Nothing Behind It

The search query that produces this page assumes two models exist. One does. Mi:dm K 3.0 Pro — MIDM K 3.0 Pro on the international price list — has been billable on KT's Token Factory platform since 29 October 2026 at $0.20 per million input tokens and $0.80 per million output tokens. DeepSeek V4.1 Pro has no model card, no endpoint, no price row and no weights; DeepSeek named it exactly once, in the release note that shipped DeepSeek V4.1 Flash on 10 September 2026, and has published nothing about it since. So the honest version of this matchup has to substitute the DeepSeek model a reader can actually call today — DeepSeek V4 Pro 0813, the 1.6-trillion-parameter flagship from the previous generation — and be explicit that the substitution is not what the title says. What follows is that comparison, plus a short account of what would have to appear before the V4.1 Pro column stops being empty.

The name problem, before anything else

The V4.1 Pro name entered circulation attached to a plan DeepSeek announced and then withdrew. On 10 September 2026 the company released DeepSeek V4.1 Flash and said in the same note that requests to its existing Pro tier would be routed to the Flash model until V4.1 Pro launched. The routing plan lasted about a day. Developers objected — a Pro customer being silently served a smaller model at Pro prices is a reasonable thing to object to — and DeepSeek put DeepSeek V4 Pro back online at unchanged prices.

What remains is a name in a footnote. There is no Pro card in the V4.1 family, no pricing row, and no checkpoint. Comparisons that treat DeepSeek V4.1 Pro as an existing model are working from extrapolation: the V4.1 architecture is real and shipped in the Flash tier, so a Pro version of it is a reasonable guess, and a guess is what every published specification for it amounts to. It should not be repeated as fact, and this page does not.

What the two callable models actually are

• Mi:dm K 3.0 Pro — 120B total parameters, 12B active per token, in a hybrid stack of Mamba, mixture-of-experts and attention layers. Text in, text out. 261,795-token context, up to 235,615 tokens of output. Reasoning trace exposed separately; JSON and structured output supported. Served by KT from a checkpoint with NVFP4 experts and FP8 projections.

• DeepSeek V4 Pro 0813 — a 1.6-trillion-parameter mixture of experts with 49 billion active. Text only. 1M-token context, 384K maximum output. Thinking and non-thinking modes with low, high and max reasoning effort, JSON output, tool calling, and a native Responses API with an adaptation aimed at Codex-style coding agents.

The shape difference is worth holding on to. The Korean model is small and cheap and reads 262K tokens. The DeepSeek model is thirteen times the parameter count, reads four times the context, and by KT's own disclosure is not the only architecture in play — DeepSeek's V4 generation shipped a 552-billion-parameter V4.1 Flash on a new Causal Encoder-Decoder design, which is where that lab's current engineering is pointed.

Price, including the part that is not on the rate card

• Mi:dm K 3.0 Pro — $0.20 input and $0.80 output per million tokens, with cached input at $0.04. KT's own platform currently sells all of it at a 30% discount: $0.14 in, $0.56 out, $0.03 cached.

• DeepSeek V4 Pro 0813 — $0.66 input and $1.98 output per million at off-peak rates, with cache reads at $0.022. During the weekday peak windows of 01:00–04:00 and 06:00–10:00 UTC every line doubles: $1.32 in, $3.96 out.

The headline is that the Korean model is about a third of the DeepSeek flagship on input and about 40% on output. The peak/off-peak structure complicates it in a direction that favours KT. A batch job that runs at 03:00 UTC on a Tuesday pays double on DeepSeek — $1.32 and $3.96 — which makes Mi:dm K 3.0 Pro eight times cheaper per output token at that hour than the DeepSeek rate card suggests. A job pinned to DeepSeek's off-peak window sees a 2.5× gap instead. Neither number is wrong; they are different schedules.

There is a second asymmetry: Mi:dm K 3.0 Pro's discount is a promotion with no published end date, while DeepSeek's peak pricing is a standing structure. Modelling on the promotional rate is the same mistake as modelling on an off-peak rate — both are real, neither is the number you should plan a budget on.

One of these models has a score, and it is not the cheap one

Artificial Analysis scores DeepSeek V4 Pro at 36 on its Intelligence Index. That is a modest number by frontier standards — it sits well below the mid-fifties where Claude Opus 5.5 and Gemini 4 Argon live — and it is a real measurement of a real endpoint. DeepSeek V4.1 Flash, the model that actually shipped in the V4.1 generation, scores 39.5 on the same index, which tells you something useful about the tier gap the Pro version is supposed to restore.

Mi:dm K 3.0 Pro has no entry. The evaluator's model path returns 404 and the model does not appear in the index at all. KT published no benchmark table with the launch either — the AID-X Day materials and the Token Factory model card describe architecture, quantization and context, and stop there. Aggregator sites that show a composite score for it are blending price and recency with a capability signal, not reporting a test result; one such page gives it 40 out of 100 overall with price alone carrying a quarter of the weight.

So the comparison a buyer would most want — does the $0.20 model match the $0.66 model — cannot be answered from published material. What can be said is directional: a 120B hybrid with 12B active is a smaller network than a 1.6T mixture with 49B active, and the smaller network is priced accordingly. If it matched on quality, the pricing would be irrational.

DeepSeek's real edge is not the parameter count

Three things in DeepSeek V4 Pro 0813 are worth more than its size, and all three are things KT has not published.

The harness work. The 0813 revision shipped with a native Responses API and a targeted adaptation for Codex-style coding agents, and DeepSeek's own numbers on it — 87.9 on Terminal Bench 2.1, 74.1 on Toolathlon Verified, 62.7 on DeepSWE, 61.5 on NL2Repo — are vendor-reported and should be labelled as such, but they describe work aimed at agentic tool use specifically. A model built and tuned for a coding-agent harness behaves differently from a general model dropped into one.

The effort ladder. DeepSeek exposes low, high and max reasoning effort alongside a non-thinking mode. That is a cost lever: the same model can answer a classification prompt cheaply and a hard reasoning prompt expensively. Mi:dm K 3.0 Pro exposes a reasoning flag but no published effort ladder, so the only lever a caller has is on or off.

Tool calling. DeepSeek V4 Pro lists tools and tool_choice among supported parameters. KT's catalogue entry for Mi:dm K 3.0 Pro does not list them, which is not the same as saying the endpoint refuses them — it means nobody has documented support, and an agent loop that depends on function calling should test rather than assume.

What 262K tokens buys, and what it costs you

Four times the context is the DeepSeek model's clearest structural advantage, and its practical value depends on which of two worlds your workload lives in.

If you send whole repositories, entire case files, or multi-hour transcripts in one request, a 262K ceiling is a real constraint. Prompt truncation is a silent quality failure — the model answers confidently from the half of the document it received — and the workarounds, chunking and retrieval, add a pipeline the cheap model's savings have to pay for. At $0.20 input and a million-token workload, though, the arithmetic is unusual: Mi:dm K 3.0 Pro could process the same document four times over in 262K slices for less than DeepSeek charges once at off-peak, so the question is whether the chunking strategy can preserve the cross-references the task depends on. For extraction it can. For "find the contradiction between page 4 and page 300" it usually cannot.

If your prompts are ordinary — a few thousand tokens of instruction and context — the context gap costs nothing, and the 20-to-1 parameter gap is the only difference that matters.

Hybrid architecture versus scale, as a bet

The two models represent opposite answers to the same 2026 problem: how to serve long context without paying quadratic attention costs.

KT's answer is architectural heterogeneity — mix Mamba for linear-time sequence handling, mixture-of-experts for cheap conditional compute, and attention where exact recall is needed, then quantize the experts to NVFP4 to bring the serving cost down further. It is the more interesting engineering bet and the less proven one. A hybrid stack that is trained well can beat a same-sized dense transformer at long context; one that is trained badly produces a model that is mediocre at both of the things its components were chosen for, and there is no public evidence yet about which happened here.

DeepSeek's answer is the more conventional one: a very large sparse mixture of experts, trained with enough data that a 49B active slice carries frontier-adjacent capability, and served cheaply enough by a lab that has been optimizing MoE inference longer than almost anyone. It is less novel and it is measured.

For a buyer, that translates to a simple asymmetry of risk. Betting on the DeepSeek model means betting on a score you can read. Betting on the Korean model means betting on an architecture description from a vendor with no track record in the international API market, at a price where the downside is small and the upside is a tenth of the cost.

How to settle it in an afternoon

DeepSeek V4 Pro 0813 is live on OrcaRouter as deepseek/deepseek-v4-pro-0813 at DeepSeek's list rate with no markup on top — $0.66 input, $1.98 output, $0.022 cache read at off-peak, with the peak multiplier showing on the same card. Mi:dm K 3.0 Pro is served by KT alone and is not on our routes; there is no second provider to fall back to, which is a real operational difference rather than a disclaimer, because a single-host model cannot be routed around when its one endpoint degrades.

That leaves a cheap and decisive experiment. Take fifty prompts from your own production traffic — not a public benchmark, the prompts you actually send — and run them against both models with a scorer you already trust. At $0.20 and $0.80 against $0.66 and $1.98, fifty prompts of 4K in and 1K out costs a fraction of a cent on the Korean model and a couple of cents on the DeepSeek one. Then run the same fifty through DeepSeek V4.1 Flash, which sits on the same key at $0.15 and $0.60 off-peak and scores 39.5 — because for a lot of workloads the answer to "should I use DeepSeek's Pro tier" is that you should not, and the Flash model is both cheaper than Mi:dm K 3.0 Pro's list price and independently scored.

Keeping the whole comparison set on one key at provider list price, with a routing rule that fails a request over to a second model when the first errors, is the part OrcaRouter does for the three models it hosts. For Mi:dm K 3.0 Pro the fallback has to be written in your own code, because KT is the only door.

Where this leaves the question

DeepSeek V4.1 Pro is a name with a release note behind it and nothing else; until DeepSeek publishes a card, a price and an endpoint, every comparison involving it is a comparison with a rumour. The callable matchup is Mi:dm K 3.0 Pro against DeepSeek V4 Pro 0813, and on the published evidence the DeepSeek model wins on context, on documented tool support, on a published independent score, and on having a second thing to fall back to when its primary is slow.

The Korean model wins on price by a factor of two and a half to eight depending on the hour, on output ceiling, on hardware efficiency by construction, and potentially on Korean-language work, where KT has been training deliberately and DeepSeek has not. Those are not small advantages for the right workload. They are just unmeasured ones — and at thirteen days of availability, unmeasured is the honest word rather than a verdict.

The practical split writes itself. Bulk text processing, Korean-language work, and anything where a cheap second opinion is worth having: test the Korean model this week. Anything agentic, anything that needs to hold 400K tokens, anything where a wrong answer costs more than the tokens: keep DeepSeek V4 Pro, or step up to a frontier model, and revisit Mi:dm K 3.0 Pro when someone publishes a number for it.