Kimi K3 vs Muse Spark Open

Muse Spark Open is the better all-round choice, scoring 68/100 against Kimi K3 (65). It leads on price. Kimi K3 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Muse Spark Open is cheaper at $1.25 input / $4.25 output per million tokens (median across 16 API providers). Kimi K3 costs $3.00 input / $15.00 output per million tokens (official Moonshot AI API price). At a typical mix of three input tokens to one output token, that is $2.00 per million tokens for Muse Spark Open versus $6.00 for Kimi K3 (3× as much).

Kimi K3 scores higher on the Capabilities Index (ECI): Kimi K3 158.3 (#20 of 165) and Muse Spark Open 155.1 (#39 of 165). The confidence ranges of the top two overlap (155.8–161.4 vs 153.1–157.3), so treat the gap as small. On individual benchmarks: GPQA Diamond — Kimi K3 93.1%, Muse Spark Open 88.6%; FrontierMath Tiers 1–3 — Kimi K3 72.2%, Muse Spark Open 69.8%; OTIS Mock AIME 2024–2025 — Muse Spark Open 98.1%, Kimi K3 97.2%; SimpleQA Verified — Muse Spark Open 56.6%, Kimi K3 50.6%.

There are no published SWE-bench Verified results for Kimi K3 and Muse Spark Open yet, so there is no like-for-like coding score. On overall capability, Kimi K3 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Kimi K3 and Muse Spark Open share the same 1,048,576-token context window. Maximum output per response: Kimi K3 up to 131,072, Muse Spark Open up to 131,072 tokens.

Kimi K3 accepts text, images and video; Muse Spark Open accepts text, images, audio and video. Muse Spark Open handles the widest range of inputs.

Yes, both publish their weights (Muse Model License), so you can self-host them.

Muse Spark Open is the newest, released Oct 15, 2026. Kimi K3 came out Jul 16, 2026. Knowledge cutoff: Muse Spark Open Jan 4, 2026.

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.