GPT-6 Astra vs MiMo V2.6 Pro
Compare GPT-6 Astra and MiMo V2.6 Pro 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 MiMo V2.6 Pro on Vision Evals
GPT-6 Astra scores higher on all six Vision Evals tasks.
The widest gap is Reasoning, where GPT-6 Astra leads 87.2% to 35.1%.
Overall, GPT-6 Astra averages 86.6% (#1 of 59) against 62.5% (#49 of 59) for MiMo V2.6 Pro.
MiMo V2.6 Pro is cheaper ($0.0008 vs $0.030 per sample), while GPT-6 Astra is faster (6.7s vs 8.5s per sample).
GPT-6 Astra vs MiMo V2.6 Pro Comparison Table
Evals updated September 22, 2026Pricing updated September 23, 2026
| Property | GPT-6 Astra | MiMo V2.6 Pro |
|---|---|---|
| Organization | OpenAI | Xiaomi |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 1.1M | 1.0M |
| Parameters | Undisclosed | 1.02T total, 42B active |
| License | Proprietary | MIT |
| Pricing per 1M tokens | ||
| Input $/1M | $10.00 | $0.435 |
| Output $/1M | $50.00 | $0.870 |
| 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 | 86.6% | 62.5% |
| Avg cost / sample | $0.030 | $0.0008 |
| Avg speed / sample | 6.67s | 8.47s |
| By task | ||
| Object Detection (low) | 82.1% ±0.8, Mean of 3 runs, range 81.0 to 82.7 | 42.0% ±1.1, Mean of 3 runs, range 40.9 to 43.1 |
| Object Detection (high) | 83.6% ±0.8, Mean of 3 runs, range 82.8 to 84.5 | 46.7% ±0.8, Mean of 3 runs, range 45.7 to 47.3 |
| Counting (low) | 80.2% ±1.4, Mean of 3 runs, range 78.4 to 81.1 | 50.0% ±2.0, Mean of 3 runs, range 48.6 to 52.7 |
| Counting (high) | 81.1% ±1.4, Mean of 3 runs, range 79.7 to 82.4 | 59.0% ±5.4, Mean of 3 runs, range 52.7 to 63.5 |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 76.0% ±1.6, Mean of 3 runs, range 75.0 to 78.1 |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | 78.1% ±4.7, Mean of 3 runs, range 71.9 to 81.3 |
| OCR (low) | 91.9% ±0.2, Mean of 3 runs, range 91.6 to 92.1 | 90.7% ±1.7, Mean of 3 runs, range 88.5 to 91.9 |
| OCR (high) | 91.5% ±0.2, Mean of 3 runs, range 91.3 to 91.7 | 87.5% ±2.7, Mean of 3 runs, range 85.3 to 90.6 |
| Data Extraction (low) | 88.7% ±1.0, Mean of 3 runs, range 87.6 to 89.7 | 81.1% ±0.5, Mean of 3 runs, range 80.4 to 81.4 |
| Data Extraction (high) | 91.1% ±1.0, Mean of 3 runs, range 89.7 to 91.8 | 80.4% ±1.5, Mean of 3 runs, range 79.4 to 82.5 |
| Reasoning (low) | 87.2% ±1.0, Mean of 3 runs, range 86.1 to 88.1 | 35.1% ±2.6, Mean of 3 runs, range 32.5 to 37.8 |
| Reasoning (high) | 91.2% ±0.3, Mean of 3 runs, range 90.7 to 91.4 | 55.9% ±2.3, Mean of 3 runs, range 54.3 to 58.9 |
GPT-6 Astra vs MiMo V2.6 Pro: 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.
MiMo V2.6 Pro is the flagship omni-modal foundation model in Xiaomi's MiMo V2.6 series, released as open weights alongside a Flash variant and a 9B distillation of Qwen3.5. It uses a sparse mixture-of-experts transformer with 1.02 trillion total parameters and roughly 42 billion activated per token, paired with a hybrid attention design that interleaves sliding-window and global attention layers to support a context window of about one million tokens. Dedicated encoders handle non-text inputs, including a vision encoder of roughly 681 million parameters and an audio tokenizer stack, so the model accepts text, images, video, and audio and returns text.
Post-training centers on large-scale reinforcement learning across thousands of interactive environments, combined with agentic grading, self-correction cold start, and a multi-prefix multi-teacher on-policy distillation stage that extends behavior to tasks that are hard to verify automatically. The resulting model targets long-horizon agentic work such as software engineering, terminal and computer-use operation, tool calling, cybersecurity analysis, and visual coding, and it reports gains over the prior MiMo generation on SWE-bench Verified, Terminal Bench, and internal visual coding and cyber benchmarks.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Astra performed better. It scores higher on all six vision tasks and averages 86.6% (#1 of 59) against 62.5% (#49 of 59) for MiMo V2.6 Pro. 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 35.1%. This is the widest gap between the two models across the benchmark's tasks.
MiMo V2.6 Pro is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.030. GPT-6 Astra is priced at $10.00 per 1M input tokens and $50.00 per 1M output; MiMo V2.6 Pro is priced at $0.43 per 1M input tokens and $0.87 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 8.5s. 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 OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.