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Published model evaluations

Kimi K3: trading task evaluations

See how moonshotai/kimi-k3 performed in 1 trading task evaluation, with exact version, measured results, dates and test inputs.

As of 2026-08-08

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TL;DR

Kimi K3 matched Luna's observed planning mean in the August 8, 2026 replay, but with a higher recorded bill and response time. Across 29 frozen conversations it scored 0.731, cost $0.0799 per decision and took a median 38.9 seconds. The test compared routing and plans before fresh research began. Small score differences carry sample uncertainty; billing reflects the historical run rather than a current price quote.

Exact tested ID
moonshotai/kimi-k3
Published studies
1
Provider
moonshotai

Planning models on 29 frozen conversations: compare the tested outputs

Published mean score

Scale: 0 to 0.759 · zero baseline

Published mean score

  • kimi-k3+0.731
  • muse-spark-1.1+0.759

16-model campaign, 29 frozen conversations; 14 model rows shown in the public table. Published 2026-08-08. Values retain their original units and grading scale. Raw means are descriptive; the unchanged table retains uncertainty and the other reported dimensions.

Show the table · 2 rows, 5 columns

Scroll sideways to compare all columns. Select a heading to sort.

moonshotai/kimi-k30.7310.068$0.079938.9s
meta/muse-spark-1.10.7590.079$0.02299.6s

16-model campaign, 29 frozen conversations; 14 model rows shown in the public table. Published 2026-08-08. Values retain their original units and grading scale.

moonshotai/kimi-k3 was tested on 2026-08-08. 16-model campaign, 29 frozen conversations; 14 model rows shown in the public table. Its row and the other published arm use the same task and measurement units.

What this version helps you choose

Its observed planning mean matched Luna’s, with a higher recorded bill and median response time.

Route a request and plan the work: follow the published task

A useful planner chooses the right kind of work before doing it. The complete customer conversations and system prompts remain private.

Step 1 of 2

Published task summary

An incoming request and its existing conversation.

What you should see

Choose whether to write a plan, call a catalog tool, ask for clarification or continue an existing agent.

Step 2 of 2

Read what came back

A routing decision and plan. Every model receives the same saved conversation; the test compares that decision before fresh research begins.

What you should see

Replay 29 frozen conversations across the model arms, then compare graded plan scores, billing and response time.

Planning models on 29 frozen conversations

For routing and planning, Kimi K3 recorded a mean score of 0.731 with standard error 0.068. Historical billing was $0.0799 per decision, and median response time was 38.9s. Compare that cost and response time with the other models in the same 29-conversation study; the mean alone does not establish a decisive quality advantage.

Show the table · 4 rows, 2 columns
Published metricValue
Score0.731
+/- SE0.068
$ / decision$0.0799
p5038.9s

Planning models on 29 frozen conversations: read these measurements

Read the complete rubric, sample and billing notes

16-model campaign, 29 frozen conversations; 14 model rows shown in the public table

Route an incoming request: write a plan, call a catalog tool, ask for clarification or continue an existing agent.

  1. Scoring

    Top planning means were close at this sample size. The source's paired comparison with Opus was inconclusive.

  2. Cost units

    Cost is provider-billed dollars per decision. Prefix reuse can make it differ from list price multiplied by every input token.

  3. Time units

    p50 is median latency in seconds, not the duration of complete research.

  4. Frozen conversation

    A saved input kept identical across model arms so their decisions can be compared.

  5. Standard error (SE)

    An estimate of uncertainty around the sample mean. A small mean difference needs more evidence than the ranking alone.

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