GPT-6 Astra vs Kimi K3
Compare GPT-6 Astra and Kimi K3 side-by-side. See how these vision models stack up in Object Detection, OCR, Open Prompt, Classification, and Image Captioning.
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Models in this comparison
GPT-6 Astra vs Kimi K3 on Vision Evals
GPT-6 Astra scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where GPT-6 Astra leads 87.2% to 42.4%.
Overall, GPT-6 Astra averages 86.6% (#1 of 53) against 66.5% (#31 of 53) for Kimi K3.
Kimi K3 is cheaper ($0.011 vs $0.030 per sample), while GPT-6 Astra is faster (6.7s vs 12.7s per sample).
GPT-6 Astra vs Kimi K3 Comparison Table
Evals updated September 5, 2026Pricing updated September 5, 2026
| Property | GPT-6 Astra | 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 | $10.00 | $3.00 |
| Output $/1M | $50.00 | $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 |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 86.6% | 66.5% |
| Avg cost / sample | $0.030 | $0.011 |
| Avg speed / sample | 6.67s | 12.71s |
| By task | ||
| Object Detection (low) | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 | 51.9% |
| Object Detection (high) | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 | – |
| Counting (low) | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | 46.0% |
| Counting (high) | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 | – |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 81.3% |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | – |
| OCR (low) | 91.9% ±0.2, Mean of 3 runs, range 91.6 to 92.1 | 93.0% |
| OCR (high) | 91.5% ±0.2, Mean of 3 runs, range 91.3 to 91.7 | – |
| Data Extraction (low) | 88.7% ±1.0, Mean of 3 runs, range 87.6 to 89.7 | 84.5% |
| Data Extraction (high) | 91.1% ±1.0, Mean of 3 runs, range 89.7 to 91.8 | – |
| Reasoning (low) | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 | 42.4% |
| Reasoning (high) | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 | 74.2% |
GPT-6 Astra vs Kimi K3: Overview
GPT-6 Astra is a proprietary multimodal reasoning model from OpenAI that accepts text and image input and produces text output. It is positioned as the company's flagship system for long-horizon end-to-end work spanning complex reasoning, software engineering, computer use, browsing, research and document creation. The model exposes a graduated reasoning effort control with low, medium, high, xhigh and max settings, and it accepts a change to that setting partway through a conversation rather than only at request time. It launches as a single tier with no smaller mini or nano variants, carries a context window of roughly 1.05 million tokens with a maximum output of 128,000 tokens, and reports a knowledge cutoff of April 30, 2026.
OpenAI reports evaluation results across agentic, scientific and security benchmarks, including 96.0% on GPQA Diamond, 64.6% on Terminal-Bench Science, 72.6% on OSWorld 2.0, and a perfect score on ExploitBench, along with near saturation on FrontierMath Tier 4 and ARC-AGI-3. The model supports computer use, structured outputs, streaming, programmatic tool calling, multi-agent orchestration, prompt caching and persisted reasoning, and it keeps earlier context windows searchable so it can recover requirements or tool outputs from previous turns. OpenAI describes Astra as the first of its models to cross the Critical cybersecurity capability threshold under its Preparedness Framework.
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, GPT-6 Astra performed better. It scores higher on 5 of the six vision tasks and averages 86.6% (#1 of 53) against 66.5% (#31 of 53) for Kimi K3. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, GPT-6 Astra leads with 87.2% 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 $0.011 per sample against $0.030. GPT-6 Astra is priced at $10.00 per 1M input tokens and $50.00 per 1M output; Kimi K3 is priced at $3.00 per 1M input tokens and $15.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Astra is faster. Across Roboflow's Vision Evals it averaged 6.7s 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 OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.