stealthmodels.MODEL BENCHMARKS
First model of Moonshot AI's K3.1 series: Kimi K3 with much larger post-training compute, a million-token context, image and video input and a faster Highspeed variant. Moonshot plans to release the weights by 4 November 2026.
DATASHEET
A good fit forLong-horizon coding agents and game builds, scripted 3D and CAD modeling in tools such as Blender, and financial document research. Highspeed suits interactive work at double the token price.
Kimi K3.1 ProOther modelsMedian
kimi-k3.1-proKimi API (Moonshot AI); input 2 USD/1M; cached read 0.20/1M; output 10/1M. Highspeed: input 4, cached read 0.20, output 20
per 1M tokens
moonshotai/kimi-k3.1-proper 1M tokens
Available
kimi-k3.1-proper 1M tokens
Available
kimi-k3.1-pro-highspeedmoonshotai/kimi-k3.1-promoonshotai/kimi-k3.1-promoonshotai/kimi-k3.1-promoonshotai/kimi-k3.1-pro-highspeedkimi-k3.1-proOur speed measurement: 116.93 tokens/s on average, from 110.97 to 120.82 tokens/s.
How StealthMark scores models · General and Visual Intelligence
Benchmark results
Model IDmoonshotai/kimi-k3.1-pro
StealthMark#11of 53General53.9Visual62.0
An imagined creature encounters its reflection. The scene tests original character design, visual self-interpretation and the relationship between a subject and its reflected image.
First run
An SVG interpretation of the Mona Lisa tests facial likeness, anatomy and the composition of a familiar portrait.
First run
A busy shoreline at sunset tests scene composition, animal forms and the relationship between light and water reflections.
First run
A spinning metal sphere and paint in a mirrored room test physical reasoning, perspective and consistency across repeated reflections.
First run
A plank, computer mouse and blueberry in a steel room test physical stability, spatial relationships, materials and lighting.
First run
A jet-ski scene pairs a canine driver with a raccoon passenger. It tests the placement and contact of interacting subjects, along with vehicle construction.
First run
A cycling pelican tests anatomy, riding posture and contact between the bird and the bicycle.
First run
Try another task or widen your model selection.
StealthMark scoreStealthMark measures how well a model’s abilities carry over to original, demanding tasks it could not have trained for: physical and spatial reasoning, planning across many steps, and precise visual work. How StealthMark works explains every benchmark, what it tests and why it is hard.
The ability to reason, plan, solve problems and understand how things relate and behave. A high score means it thinks hard problems through, from careful analysis to plans that span many steps.
The ability to understand visual intent and turn ideas, descriptions and plans into coherent, effective visuals. A high score means design ideas come out the way you meant them, in illustrations, icons, diagrams or interface visuals.
Overall shows how well a model’s abilities carry over to original, demanding tasks, weighting every benchmark into one score from 0 to 100. Estimated means the total includes an estimate for one missing scene.
Scores reflect how well each result meets the benchmark requirements. Game scores come from the recorded play: the points earned on each level and how each level ended. How StealthMark keeps scores honest.
Each game is a sequence of levels, and earlier moves carry into later levels. A level that was not played is shown as not played and never counts as zero. Play every level of Latch or Foresight yourself to get a score calculated like the models’.
Good results with little thinking score high. On each task, a model’s result and reasoning tokens are compared with every other model and combined with the Balanced Integration Score (Liesefeld & Janczyk, 2019); 50 is average. Single step shows how much reasoning a model needs to understand and solve one problem, multi-step how efficiently it keeps working on a task by building on its earlier reasoning, as in agentic workflows. Not part of StealthMark.