DeepSeek V4 Pro vs Kimi K3: text, images and reasoning

Start with the input, not a leaderboard

DeepSeek lists thinking and non-thinking modes for V4 Pro and marks vision as unsupported. Moonshot describes Kimi K3 as a model with native visual understanding. Those facts help select a compatible task, but they do not establish which gives the more accurate answer to your documents.

If your source is a screenshot, use an interface that supports image input for the selected model. If you convert the screenshot into text, inspect the transcription first: a missing minus sign or table column can change the answer before either model begins.

DeepSeek: model details · Moonshot AI: Kimi K3

Test 1: extract claims with evidence

Use a short report containing both confirmed facts and forecasts. Ask for a table of claims, exact evidence and uncertainties. Reject claims that have no support in the supplied text.

Do not award points just for producing many rows. An answer that extracts six correct facts can be more useful than twelve rows containing guesses. Check whether qualifications such as “expected” and “subject to approval” survive.

Read only this report: [text]. Make a table with claim, exact supporting quote, and status: confirmed, forecast or unclear. Do not use outside knowledge. Then list contradictions and missing information without resolving them by guessing.

Test 2: a calculation with an answer key

Choose a small example you can verify yourself. Suppose a team plans four sessions with eight participants each, then cancels one session. The remaining total is 24 participant places, not necessarily 24 unique people. That distinction makes a useful reasoning check.

Ask each model to show its arithmetic and state what the numbers count. Compare both the calculation and the interpretation. For important work, verify arithmetic independently; a detailed explanation can still contain an error.

A team schedules four sessions with eight participant places in each. One session is cancelled. How many places remain? Does that establish the number of unique people? Show the calculation and explain what cannot be inferred.

Test 3: explain a function without inventing behaviour

Supply the same short function and ask for the result of a normal input and an edge case. Work out the expected outputs first, then compare the explanations.

Check whether a model confuses what the code actually does with what it should do. A helpful review keeps the observed behaviour, suspected bug and suggested change separate.

Explain this function: [code]. For inputs [normal case] and [edge case], trace the steps and expected output. Separate actual behaviour from possible bugs. Do not claim to have executed it. State any assumption about the programming language or runtime.

Choose a model for a role

Use input compatibility as a first filter, then compare unsupported claims, missed constraints and correction time on the same text tasks. Keep the original answers so a later model update can be tested against the same examples.

A provider’s model benchmark is not automatically a measurement of GO AI. Tools, prompts and service settings affect the experience. We have not run a controlled DeepSeek-versus-Kimi benchmark for this article; the exercises are reusable evaluation material.

Common questions

Sources and scope

• DeepSeek: model details
• Moonshot AI: Kimi K3