Kimi K3 vs Muse Spark 1.2
Muse Spark 1.2 and Kimi K3 score 73.8% vs 56.0% averaged across five Vision Evals tasks (#18 and #45 of 65 models), so Muse Spark 1.2 leads overall.
- Muse Spark 1.2 wins all 5 tasks, with its widest lead on reasoning (75.1% vs 42.4%).
- Kimi K3 is about half the cost: $0.0052 vs $0.0083 overall average cost per sample.
- Kimi K3 is faster: 9.9s vs 11.5s per sample.
Roboflow Vision Evals. Full breakdown below.
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Kimi K3 vs Muse Spark 1.2 on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Kimi K3 vs Muse Spark 1.2 Comparison Table
Evals updated October 26, 2026Pricing updated November 5, 2026
| Property | Kimi K3 | Muse Spark 1.2 |
|---|---|---|
| Organization | Moonshot AI | Meta |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Aug 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | 2.8T | Unknown |
| License | Modified MIT | Proprietary |
| Commercial use | See license terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.192 | $1.25 |
| Output $/1M | $16.00 | $4.25 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 56.0% | 73.8% |
| Avg cost / sample | $0.0052 | $0.0083 |
| Avg speed / sample | 9.86s | 11.51s |
| By task | ||
| Object Detection (low) | 51.9% | 59.0% ±1.0, Mean of 3 runs, range 58.1 to 60.2 |
| Object Detection (high) | – | 60.5% ±0.3, Mean of 3 runs, range 60.2 to 60.7 |
| Counting (low) | 46.0% | 76.6% ±2.7, Mean of 3 runs, range 74.3 to 79.7 |
| Counting (high) | – | 75.2% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Identification (low) | 81.3% | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | – | 87.5% ±0.0, Mean of 3 runs, range 87.5 to 87.5 |
| OCR (low) | 58.6% | 68.7% |
| by category |
|
|
| OCR (high) | 61.8% | 68.6% |
| by category |
|
|
| Reasoning (low) | 42.4% | 75.1% ±0.3, Mean of 3 runs, range 74.8 to 75.5 |
| Reasoning (high) | 74.2% | 75.7% ±0.3, Mean of 3 runs, range 75.5 to 76.2 |
Kimi K3 vs Muse Spark 1.2: Overview
Kimi K3 is a sparse Mixture-of-Experts large language model developed by Moonshot AI, with 2.8 trillion total parameters and a 1-million-token context window. The model activates 16 out of 896 experts per token using the Stable LatentMoE framework, and is built on two architectural innovations: Kimi Delta Attention (KDA), a hybrid linear attention mechanism that enables up to 6.3x faster decoding in long-context settings, and Attention Residuals (AttnRes), which selectively retrieves representations across model depth and delivers roughly 25% higher training efficiency. Together with refined training and data recipes, these structural advances yield approximately 2.5x better overall scaling efficiency compared to its predecessor Kimi K2. The model applies quantization-aware training from the supervised fine-tuning stage onward, using MXFP4 weights with MXFP8 activations for hardware compatibility. Thinking mode is always enabled at launch, with reasoning effort configurable via the reasoning_effort field.
Kimi K3 supports native visual understanding alongside text, accepting image inputs for tasks that combine software engineering and visual reasoning. It targets long-horizon coding, knowledge work, and agentic workflows, and ships in two variants: K3 Max for general chat and agent tasks, and K3 Swarm Max for large-scale parallel processing across many coordinated sub-agents. The model is compatible with the OpenAI SDK via an OpenAI-compatible API. Full model weights are scheduled for release by July 27, 2026 under a Modified MIT license, following the open-weight pattern established by the Kimi K2 model family. A technical report with full architecture, training, and evaluation details is expected to accompany the weights release.
Muse Spark 1.2 is a proprietary multimodal reasoning model from Meta Superintelligence Labs, released as a coding-focused update to Muse Spark 1.1. It accepts text, images, video, audio, and PDF documents and returns text, with a context window of roughly one million tokens that allows whole repositories, long documents, and extended agent trajectories to be held in a single request. The model thinks before answering, and the amount of reasoning effort it spends is configurable per request. Alongside its visual and document understanding, it supports structured output and parallel function calling, and it is designed to operate either as a planning agent that delegates work or as a subagent executing tasks in parallel.
Training for version 1.2 scaled up compute on coding tasks and widened the diversity of training environments, concentrating on long-horizon work such as whole-repository generation, large end-to-end projects, and automated research. Part of the training data was self-generated, with Muse Spark 1.1 producing coding environments and instruction-following templates and grading candidate solutions against them. The model was co-trained with the Muse Code terminal agent, incorporating rejection-sampled harness trajectories and that toolset. Multimodal use cases documented for the family include visual-to-code generation and detailed image and video captioning.
Frequently Asked Questions
On Roboflow's Vision Evals, Muse Spark 1.2 performed better. It scores higher on all five vision tasks and averages 73.8% (#18 of 65) against 56.0% (#45 of 65) for Kimi K3. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Reasoning benchmark at low effort, Muse Spark 1.2 leads with 75.1% against 42.4%. This is the widest gap between the two models across the benchmark's tasks.
Kimi K3 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0052 per sample against $0.0083. Kimi K3 is priced at $0.19 per 1M input tokens and $16.00 per 1M output; Muse Spark 1.2 is priced at $1.25 per 1M input tokens and $4.25 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Kimi K3 is faster. Across Roboflow's Vision Evals it averaged 9.9s per inference against 11.5s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.