Kimi K3 vs Qwen3.7 Plus
Compare Kimi K3 and Qwen3.7 Plus 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 Qwen3.7 Plus on Vision Evals
Kimi K3 scores higher on 3 of the six Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.7 Plus leads 60.1% to 51.9%.
Overall, Kimi K3 averages 66.5% (#20 of 31) against 67.4% (#18 of 31) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.011 per sample) and faster (7.0s vs 12.7s per sample).
Kimi K3 vs Qwen3.7 Plus Comparison Table
Evals updated August 20, 2026Pricing updated August 24, 2026
| Property | Kimi K3 | Qwen3.7 Plus |
|---|---|---|
| Organization | Moonshot AI | Qwen |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Jul 2026 | — |
| Context Window | 1.0M | — |
| Parameters | 2.8T | |
| License | Modified MIT | |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $0.320 |
| Output $/1M | $15.00 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Vision Language | ||
| 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% | 67.4% |
| Avg cost / sample | $0.011 | $0.0008 |
| Avg speed / sample | 12.71s | 7.01s |
| By task | ||
| Object Detection | 51.9% $0.020 | 60.1% $0.0013 |
| Counting | 46.0% $0.0046 | 50.0% $0.0004 |
| Identification | 81.3% $0.0041 | 84.4% $0.0003 |
| OCR | 93.0% $0.0094 | 86.5% $0.0009 |
| Data Extraction | 84.5% $0.0046 | 83.5% $0.0004 |
| Reasoning (low) | 42.4% $0.0044 | 39.7% $0.0003 |
| Reasoning (high) | 74.2% $0.037 | 68.2% $0.0043 |
Kimi K3 vs Qwen3.7 Plus: 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.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.7 Plus performed slightly better overall. The two split the six vision tasks 3 to 3, but Qwen3.7 Plus averages 67.4% (#18 of 31) against 66.5% (#20 of 31) 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 Object Detection benchmark, Qwen3.7 Plus leads with 60.1% against 51.9%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.7 Plus is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.011. Kimi K3 is priced at $3.00 per 1M input tokens and $15.00 per 1M output; Qwen3.7 Plus is priced at $0.32 per 1M input tokens and $1.28 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.0s per inference against 12.7s. 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.