In July 2026, Moonshot's Kimi K3 and DeepSeek V4-Flash shipped almost back-to-back. They sit close on Coding and Agent leaderboards — yet they take opposite engineering routes.

K3 pulls vision into the main model. V4-Flash keeps parameter scale small and leans on post-training.

K3 has 2.8 trillion total parameters and activates roughly 104 billion per token. It continues the early-fusion recipe from K2.5 — a roughly 15-trillion-token joint pretraining that mixes text and vision tokens at a fixed ratio, plus a 4-billion-parameter vision encoder, MoonViT-V2, trained from scratch. After release, K3 briefly topped the Arena Frontend Code list at 1679, behind only Claude Fable 5, GPT-5.6 Sol and a handful of others. On Arena WebDev, K3 and V4-Flash both entered the top band — 1679 for K3, 1577 for V4-Flash.

DeepSeek V4-Flash chose to stay out of vision. Total parameters are 284 billion, with about 130 billion activated per token — roughly one tenth and one eighth of K3 respectively. The architecture and scale barely move; all the work goes into post-training. According to DeepSeek's own Coding and Agent evaluations, the release version beat the preview version by a wide margin and even surpassed the earlier V4-Pro preview.

Alibaba's Qwen3.8-Max, released right after, chose a direction close to K3's "big and broad" play: 2.4 trillion total parameters, roughly 95 billion activated per token, carrying native vision, Coding and Cowork at the same time. Three frontier base-model makers now line up into two camps — Kimi and Qwen bet on "very large parameters plus native multimodal," DeepSeek bets on "small model plus post-training pressure."

Why Agents need eyes

Vision is becoming increasingly critical for Agents. An OpenAI enterprise-usage report from January 2026 noted that, among the three ChatGPT tools most often used in research roles, image upload ranks third — after search and data analysis. As Agents begin to generate web pages, operate software and inspect runtime results, vision turns into the feedback channel that lets the model check work, spot bugs and adjust action.

Kimi calls this "code-then-screenshot, iterate" loop "vision in the loop" — write code, look at the page, adjust based on what is on screen. The browser-development platform Puter planted five visual discrepancies in a test page; K3 compared the target and rendered screenshots, found all five, and reported no false positives.

Plain text models, by contrast, can only rely on OCR or an external vision-language model to convert images into text, tags, coordinates or structured fields. A multimodal researcher describes that "tooled-up" arrangement as bounded: the LLM is the 'blind person,' and a smaller model downstream is the 'eyes.' The communication channel between vision input and the language backbone inside a native multimodal model is "wider" — it does not compress an image into text before handing it to a separate model. Native multimodal models can also run visual reinforcement learning on their own rendered pages or images and fold the visual result back into the same training trajectory.

But whether vision is essential to a model understanding the world remains contested. Ilya Sutskever, OpenAI co-founder and former chief scientist, has called text "a projection of the world": humans have already compressed much of reality into language, and a model that keeps learning text can still grasp the world. Meta chief AI scientist Yann LeCun argues the opposite: "The vast majority of human knowledge is not expressed in textual form." Language is human-selected and summarized; if a model is to understand objects, space and action, it must ultimately learn from images, video and real-world interaction.

The cost of vision: 1 + 1 is meaningfully greater than 2

Folding vision into the main model costs more than "plugging in another input."

Image and text differ in data structure, information density and learning speed. When they are trained against the same backbone parameters, vision data competes with text, code, math and reasoning for model capacity, and different training objectives can interfere. The Kimi K2.5 technical report recorded this in an ablation on fusion timing: if visual data is added late in pretraining, text capability first drops and then slowly recovers.

"The more tasks, the harder the balance," a multimodal researcher says. "Doing one task, you can keep tuning in one direction; doing two tasks — vision plus text — either make the model larger or add more data."

Apple's MM1 technical report pointed at a similar conclusion: the choice of vision encoder, image resolution and the number of vision tokens all affect outcome; higher resolution and more vision tokens usually bring higher training and inference cost. For tasks that only need a few fields read out, having a general model stare at the whole picture is not necessarily cheaper than OCR or a specialist model.

The timing split: spend compute on multimodality or on Coding?

DeepSeek founder Liang Wenfeng has said: "To train AI well, you don't need a world model — you don't even need multimodality." He also said: "But you eventually have to do multimodality." The two statements do not contradict. Frontier labs do not really disagree on whether multimodal matters long term; the disagreement is on timing and cost.

In late June, Zhipu chief scientist Tang Jie polled users on X about what features a next-gen GLM "must have." The repeated answer in replies was "vision." Public signals suggest the next-gen base models from Zhipu and Tencent Hunyuan could move further toward native multimodality.

But DeepSeek V4-Flash has already shown that, without adding vision and without dramatically scaling parameters, post-training can still lift Coding and other core task capabilities substantially. That makes today's resource-allocation question very concrete: spend vision compute and data budget early, or keep pushing scores on Coding and Agent first?

Several industry voices argue that, in a phase where Agent task chains keep getting longer, Coding is the price of staying at the table — if Coding does not move up, there will be no future seat. In other words, Coding and Agent capability monetizes over months or weeks; moving a model from "seeing" to "understanding" is a longer evolutionary cycle.

Take: two clocks running at once

On multimodality, Chinese frontier base-model makers are running two different time-races at the same time.

Pulling vision into the main model early means "looking ahead from the present" — the bet is that, once Agents take over more long-chain tasks and web pages and GUIs become the dominant working surface, visual feedback shifts from a bonus to an entry ticket. That is K3 and Qwen3.8-Max's wager.

Setting vision aside, pushing parameters down and putting every ounce of pressure into post-training means "holding the line right now" — the bet is that Coding and Agent leaderboards keep refreshing, and that ranking sits closer to cash flow than "being able to see" does. That is DeepSeek V4-Flash's wager.

These routes are not mutually exclusive. Kimi's strategy has always run post-training in parallel; DeepSeek will do native multimodality eventually. But at this 2026 summer moment, each company has drawn a clear line between training cost and product decisions — and that line, more than any technical verdict, reflects each company's read on tempo. When the two lines finally cross is left for the next delivery cycle to answer.

Source notes: factual claims in this piece — model parameters, benchmark scores, release timelines, quoted remarks, technical-report conclusions — trace back to the original report 36Kr, "Kimi K3 与 DeepSeek V4 之间,隔着原生多模态的时间差" and the Kimi K2.5 technical report.