Head to head
Kimi K2.6 vs Kimi K2 Thinking
Kimi K2.6 from Moonshot AI against Kimi K2 Thinking from Moonshot AI — specification, price and every benchmark both makers have published, in one table.
At a glance
Benchmarks
Kimi K2.6 leads
Kimi K2.6 wins 1 of the 1 benchmarks both models report, Kimi K2 Thinking wins 0, consistently. The average gap across shared tests is 3.9 points.
Price
Not directly comparable
At least one of these does not have a published per-token price, so cost cannot be compared like for like. The open-weight model can be run on your own hardware instead.
What actually differs
- Only Kimi K2.6 reads images. If your input includes screenshots, charts or documents, that decides it.
Scorecard
Which is better at what
Maths, coding, reasoning and the rest — one line each, averaged over the benchmarks both models actually report.
| Category | Kimi K2.6 | Kimi K2 Thinking | Better at this |
|---|---|---|---|
| ReasoningGPQA Diamond | 88.4% | 84.5% | Kimi K2.6+3.9 |
| MathsOnly one model reports this | — | — | Not comparable |
| CodingOnly one model reports this | — | — | Not comparable |
| KnowledgeNeither model reports this | — | — | Not comparable |
| MultimodalNeither model reports this | — | — | Not comparable |
| Instruction followingNeither model reports this | — | — | Not comparable |
| Human preferenceNeither model reports this | — | — | Not comparable |
| Categories wonOut of 1 comparable | 1 | 0 | Kimi K2.6 |
Each category averages only the benchmarks every model here reports, so no one is credited for a test the other did not run. A category with no shared test is marked Not comparable rather than guessed at.
Side by side
Kimi K2.6 and Kimi K2 Thinking, row by row
| Attribute | Kimi K2.6Moonshot AI | Kimi K2 ThinkingMoonshot AI |
|---|---|---|
| Specification | ||
| MakerWho built it | Moonshot AI | Moonshot AI |
| Released | 2026-04 | 2025-11 |
| ParametersTotal, and active per token for a mixture of experts | Not reported | 1T total / 32B active |
| Architecture | MoE | MoE |
| Context windowHow much can go in at once | 262,144 tokens | 262,144 tokens |
| Max output | Not reported | 131,072 tokens |
| Input | Text, Image, Video | Text |
| ReasoningSpends extra tokens thinking before it answers | Yes | Yes |
| Tool calling | Yes | Yes |
| Licence | Not stated | Modified MIT |
| Open weightsCan you download and run it yourself | Yes | Yes |
| Price | ||
| Input priceUSD per million tokens in | Not reported | $0.60 |
| Output priceUSD per million tokens out | Not reported | $2.50 |
| Blended 3:1A 3-in-to-1-out million-token mix — a fairer single number than input price alone | Not reported | $1.07 |
| Price note | First-party API rate. | Open weights — this is a representative hosting rate, not a first-party price. Running it yourself costs only hardware. |
| Published benchmarks | ||
| GPQA Diamond198 graduate-level physics, chemistry and biology questions written to be Google-proof. PhD holders in the matching field score about 65%. | ||
| AIME 2025The American Invitational Mathematics Examination — 15 problems, integer answers, no partial credit. A standard test of multi-step maths reasoning. | Not reported | |
| SWE-bench Verified500 human-validated GitHub issues from real Python repositories. The model must produce a patch that makes the project's own tests pass. | Not reported | |
| SWE-bench ProA harder, contamination-resistant successor to SWE-bench Verified, drawn from commercial and copyleft repositories that were never public training data. | Not reported | |
| LiveCodeBenchCompetitive-programming problems collected after each model's training cutoff, so contamination cannot inflate the score. | Not reported | |
| Links | Hugging Face · Full page | Hugging Face · Full page |
| Row verified | 2026-10 | 2026-08 |
Where these numbers come from
Every score on this page is a published figure, taken from the model's own card, system card, technical report or release post, or from a public leaderboard. CorX Labs did not run these evaluations. Most are self-reported by the lab that built the model, which means they were produced under that lab's own choice of prompt, scaffold and number of attempts — so treat them as a starting point for a shortlist, not as a settled ranking.
A score someone other than the model's maker measured is marked Independent and names its measurer. Those are the stronger numbers on this page — an outside harness has no reason to flatter anyone — and there are not many of them.
Where a figure has not been published, the cell reads Not reported rather than an estimate. Nothing here is inferred, interpolated or guessed. Each model records the month its row was last checked. Full method and caveats.
Questions
Kimi K2.6 or Kimi K2 Thinking?
Is Kimi K2.6 better than Kimi K2 Thinking?
Kimi K2.6 wins 1 of the 1 benchmarks both models report, Kimi K2 Thinking wins 0, consistently. The average gap across shared tests is 3.9 points.
Which is cheaper, Kimi K2.6 or Kimi K2 Thinking?
At least one of these does not have a published per-token price, so cost cannot be compared like for like. The open-weight model can be run on your own hardware instead.
What is the difference between Kimi K2.6 and Kimi K2 Thinking?
Only Kimi K2.6 reads images. If your input includes screenshots, charts or documents, that decides it.
