GPT-6 Sol vs Kimi K3
Compare GPT-6 Sol and Kimi K3 side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.
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GPT-6 Sol vs Kimi K3 on Vision Evals
GPT-6 Sol scores higher on 4 of the six Vision Evals tasks.
The widest gap is Counting, where GPT-6 Sol leads 76.6% to 46.0%.
Overall, GPT-6 Sol averages 82.3% (#10 of 60) against 66.5% (#36 of 60) for Kimi K3.
GPT-6 Sol is both cheaper ($0.0065 vs $0.011 per sample) and faster (9.9s vs 12.7s per sample).
GPT-6 Sol vs Kimi K3 Comparison Table
Evals updated September 28, 2026Pricing updated September 28, 2026
| Property | GPT-6 Sol | Kimi K3 |
|---|---|---|
| Organization | OpenAI | Moonshot AI |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Jul 2026 |
| Context Window | 1.1M | 1.0M |
| Parameters | undisclosed | 2.8T |
| License | Proprietary | Modified MIT |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $3.00 |
| Output $/1M | $10.00 | $15.00 |
| 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 |
| Promptable Concept Segmentation | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 82.3% | 66.5% |
| Avg cost / sample | $0.0065 | $0.011 |
| Avg speed / sample | 9.94s | 12.71s |
| By task | ||
| Object Detection (low) | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 | 51.9% |
| Object Detection (high) | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 | – |
| Counting (low) | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 | 46.0% |
| Counting (high) | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 | – |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 81.3% |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | – |
| OCR (low) | 91.7% ±0.4, Mean of 3 runs, range 91.2 to 92.1 | 93.0% |
| OCR (high) | 92.0% ±0.6, Mean of 3 runs, range 91.4 to 92.6 | – |
| Data Extraction (low) | 84.2% ±0.5, Mean of 3 runs, range 83.5 to 84.5 | 84.5% |
| Data Extraction (high) | 83.5% ±2.1, Mean of 3 runs, range 81.4 to 85.6 | – |
| Reasoning (low) | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 | 42.4% |
| Reasoning (high) | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 | 74.2% |
GPT-6 Sol vs Kimi K3: Overview
GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.
The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
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.