Kimi K3 vs Muse Spark 1.3
Compare Kimi K3 and Muse Spark 1.3 side-by-side. See how these vision models stack up in Object Detection, Open Prompt, Image Captioning, OCR, and Classification.
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Models in this comparison
Kimi K3 vs Muse Spark 1.3 on Vision Evals
Muse Spark 1.3 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Muse Spark 1.3 leads 73.3% to 42.4%.
Overall, Kimi K3 averages 66.5% (#30 of 52) against 79.8% (#10 of 52) for Muse Spark 1.3.
Muse Spark 1.3 is cheaper ($0.0075 vs $0.011 per sample), while Kimi K3 is faster (12.7s vs 23.1s per sample).
Kimi K3 vs Muse Spark 1.3 Comparison Table
Evals updated September 3, 2026Pricing updated September 3, 2026
| Property | Kimi K3 | Muse Spark 1.3 |
|---|---|---|
| Organization | Moonshot AI | Meta |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | 2.8T | |
| License | Modified MIT | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $1.25 |
| Output $/1M | $15.00 | $4.25 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 66.5% | 79.8% |
| Avg cost / sample | $0.011 | $0.0075 |
| Avg speed / sample | 12.71s | 23.14s |
| By task | ||
| Object Detection (low) | 51.9% | 58.6% ±0.7, Mean of 3 runs, range 58.0 to 59.4 |
| Object Detection (high) | – | 56.6% ±2.4, Mean of 3 runs, range 54.5 to 59.3 |
| Counting (low) | 46.0% | 74.3% ±2.0, Mean of 3 runs, range 73.0 to 77.0 |
| Counting (high) | – | 75.7% ±3.4, Mean of 3 runs, range 73.0 to 79.7 |
| Identification (low) | 81.3% | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| Identification (high) | – | 86.5% ±1.6, Mean of 3 runs, range 84.4 to 87.5 |
| OCR (low) | 93.0% | 91.3% ±0.5, Mean of 3 runs, range 90.7 to 91.6 |
| OCR (high) | – | 86.9% ±4.1, Mean of 3 runs, range 82.2 to 90.4 |
| Data Extraction (low) | 84.5% | 88.7% ±1.5, Mean of 3 runs, range 86.6 to 89.7 |
| Data Extraction (high) | – | 87.6% ±0.0, Mean of 3 runs, range 87.6 to 87.6 |
| Reasoning (low) | 42.4% | 73.3% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
| Reasoning (high) | 74.2% | 73.1% ±1.0, Mean of 3 runs, range 72.2 to 74.2 |
Kimi K3 vs Muse Spark 1.3: 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 1.3 is a proprietary multimodal reasoning model from Meta Superintelligence Labs and the fourth Muse Spark release in five months, arriving on September 2, 2026. It takes text, images, video, and document files as input and returns text, and it operates over a context window of 1,048,576 tokens. Meta trains the model for long-horizon agentic work, so it carries accumulated context and prior tool results forward across many turns, reconciles messy or conflicting inputs, and asks for clarification when a task is underspecified. Visual inputs such as screenshots and video clips feed a reasoning loop that runs against a real execution environment rather than a scripted sequence of steps.
The model exposes graded reasoning effort settings. An xhigh configuration is generally available at launch, while a max reasoning configuration aimed at harder reasoning and agentic problems arrives after further safety testing. Artificial Analysis measures Muse Spark 1.3 (max) at 62 on its Intelligence Index and the xhigh configuration at 61, with agentic tool-use evaluations driving most of the gain over Muse Spark 1.2; max reaches 52% on Tau3-Bench Banking by spending more turns and reasoning tokens than xhigh. Prior Muse Spark versions emit bounding box coordinates, transcriptions, and structured field extractions from images on Roboflow Vision Evals.
Frequently Asked Questions
On Roboflow's Vision Evals, Muse Spark 1.3 performed better. It scores higher on 5 of the six vision tasks and averages 79.8% (#10 of 52) against 66.5% (#30 of 52) 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 1.3 leads with 73.3% against 42.4%. This is the widest gap between the two models across the benchmark's tasks.
Muse Spark 1.3 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0075 per sample against $0.011. Kimi K3 is priced at $3.00 per 1M input tokens and $15.00 per 1M output; Muse Spark 1.3 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 12.7s per inference against 23.1s. 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.