GPT-5.6 Luna vs MiMo V2.6 Pro
Compare GPT-5.6 Luna and MiMo V2.6 Pro side-by-side. See how these vision models stack up in Classification, Image Captioning, OCR, Object Detection, and Open Prompt.
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Models in this comparison
GPT-5.6 Luna vs MiMo V2.6 Pro on Vision Evals
GPT-5.6 Luna scores higher on 4 of the six Vision Evals tasks.
The widest gap is Reasoning, where GPT-5.6 Luna leads 60.5% to 35.1%.
Overall, GPT-5.6 Luna averages 73.8% (#20 of 59) against 62.5% (#49 of 59) for MiMo V2.6 Pro.
MiMo V2.6 Pro is cheaper ($0.0008 vs $0.0010 per sample), while GPT-5.6 Luna is faster (7.4s vs 8.5s per sample).
GPT-5.6 Luna vs MiMo V2.6 Pro Comparison Table
Evals updated September 22, 2026Pricing updated September 23, 2026
| Property | GPT-5.6 Luna | MiMo V2.6 Pro |
|---|---|---|
| Organization | OpenAI | Xiaomi |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Sep 2026 |
| Context Window | 1.5M | 1.0M |
| Parameters | 1.02T total, 42B active | |
| License | Proprietary | MIT |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | $0.435 |
| Output $/1M | $1.20 | $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 |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 73.8% | 62.5% |
| Avg cost / sample | $0.0010 | $0.0008 |
| Avg speed / sample | 7.38s | 8.47s |
| By task | ||
| Object Detection (low) | 61.0% ±1.2, Mean of 3 runs, range 59.9 to 62.2 | 42.0% ±1.1, Mean of 3 runs, range 40.9 to 43.1 |
| Object Detection (high) | 62.3% ±1.2, Mean of 3 runs, range 61.4 to 63.8 | 46.7% ±0.8, Mean of 3 runs, range 45.7 to 47.3 |
| Counting (low) | 67.1% ±1.4, Mean of 3 runs, range 66.2 to 68.9 | 50.0% ±2.0, Mean of 3 runs, range 48.6 to 52.7 |
| Counting (high) | 70.7% ±3.4, Mean of 3 runs, range 66.2 to 73.0 | 59.0% ±5.4, Mean of 3 runs, range 52.7 to 63.5 |
| Identification (low) | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 76.0% ±1.6, Mean of 3 runs, range 75.0 to 78.1 |
| Identification (high) | 84.4% ±6.3, Mean of 3 runs, range 78.1 to 90.6 | 78.1% ±4.7, Mean of 3 runs, range 71.9 to 81.3 |
| OCR (low) | 90.7% ±1.8, Mean of 3 runs, range 88.4 to 92.0 | 90.7% ±1.7, Mean of 3 runs, range 88.5 to 91.9 |
| OCR (high) | 91.5% ±0.3, Mean of 3 runs, range 91.2 to 91.7 | 87.5% ±2.7, Mean of 3 runs, range 85.3 to 90.6 |
| Data Extraction (low) | 80.4% ±2.1, Mean of 3 runs, range 78.3 to 82.5 | 81.1% ±0.5, Mean of 3 runs, range 80.4 to 81.4 |
| Data Extraction (high) | 81.8% ±0.5, Mean of 3 runs, range 81.4 to 82.5 | 80.4% ±1.5, Mean of 3 runs, range 79.4 to 82.5 |
| Reasoning (low) | 60.5% ±5.0, Mean of 3 runs, range 55.0 to 64.9 | 35.1% ±2.6, Mean of 3 runs, range 32.5 to 37.8 |
| Reasoning (high) | 65.6% ±3.6, Mean of 3 runs, range 60.9 to 68.2 | 55.9% ±2.3, Mean of 3 runs, range 54.3 to 58.9 |
GPT-5.6 Luna vs MiMo V2.6 Pro: Overview
GPT-5.6 Luna is the fastest and most cost-efficient model in OpenAI's GPT-5.6 family, which also includes Sol (the flagship tier) and Terra (the balanced mid-tier). Introduced under a new naming convention where the generation number (5.6) and a durable capability tier name (Luna, Terra, Sol) together define each model, Luna occupies the lightweight end of the family and is designed for high-volume, latency-sensitive workloads such as summarization, drafting, autocomplete, classification, and routine automation. The GPT-5.6 family as a whole advances capabilities in software engineering, computer use, professional knowledge work, scientific research, and cybersecurity, with all three tiers rated at the "High" capability level under OpenAI's Preparedness Framework for both cybersecurity and biological/chemical risk domains.
GPT-5.6 Luna supports multimodal input and function calling, and shares the family's 1.5 million token context window. On Terminal-Bench 2.1, Luna scores 82.5%, and on the Artificial Analysis Coding Agent Index it outperforms comparable models at roughly one-quarter the estimated cost of higher-tier alternatives. Luna is priced at $1 per million input tokens and $6 per million output tokens, with cached input reads at $0.10 per million tokens under the GPT-5.6 prompt caching scheme, which introduces explicit cache breakpoints and a 30-minute minimum cache life. The model was previewed on June 26, 2026 to a limited group of trusted partners via the OpenAI API and Codex, with general availability rolling out on July 9, 2026 across ChatGPT, Codex, and the API.
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-5.6 Luna performed better. It scores higher on 4 of the six vision tasks and averages 73.8% (#20 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-5.6 Luna leads with 60.5% 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 $0.0010. GPT-5.6 Luna is priced at $0.20 per 1M input tokens and $1.20 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-5.6 Luna is faster. Across Roboflow's Vision Evals it averaged 7.4s 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 image classification and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.