Claude Opus 4.8 vs MiMo V2.6 Pro
Compare Claude Opus 4.8 and MiMo V2.6 Pro side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.
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Models in this comparison
Claude Opus 4.8 vs MiMo V2.6 Pro on Vision Evals
Claude Opus 4.8 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Claude Opus 4.8 leads 53.0% to 35.1%.
Overall, Claude Opus 4.8 averages 68.7% (#31 of 59) against 62.5% (#49 of 59) for MiMo V2.6 Pro.
MiMo V2.6 Pro is cheaper ($0.0008 vs $0.016 per sample), while Claude Opus 4.8 is faster (5.2s vs 8.5s per sample).
Claude Opus 4.8 vs MiMo V2.6 Pro Comparison Table
Evals updated September 22, 2026Pricing updated September 23, 2026
| Property | Claude Opus 4.8 | MiMo V2.6 Pro |
|---|---|---|
| Organization | Anthropic | Xiaomi |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | 1.02T total, 42B active | |
| License | Proprietary | MIT |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.435 |
| Output $/1M | $25.00 | $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 |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 68.7% | 62.5% |
| Avg cost / sample | $0.016 | $0.0008 |
| Avg speed / sample | 5.20s | 8.47s |
| By task | ||
| Object Detection (low) | 38.6% | 42.0% ±1.1, Mean of 3 runs, range 40.9 to 43.1 |
| Object Detection (high) | – | 46.7% ±0.8, Mean of 3 runs, range 45.7 to 47.3 |
| Counting (low) | 54.0% | 50.0% ±2.0, Mean of 3 runs, range 48.6 to 52.7 |
| Counting (high) | – | 59.0% ±5.4, Mean of 3 runs, range 52.7 to 63.5 |
| Identification (low) | 84.4% | 76.0% ±1.6, Mean of 3 runs, range 75.0 to 78.1 |
| Identification (high) | – | 78.1% ±4.7, Mean of 3 runs, range 71.9 to 81.3 |
| OCR (low) | 93.8% | 90.7% ±1.7, Mean of 3 runs, range 88.5 to 91.9 |
| OCR (high) | – | 87.5% ±2.7, Mean of 3 runs, range 85.3 to 90.6 |
| Data Extraction (low) | 88.7% | 81.1% ±0.5, Mean of 3 runs, range 80.4 to 81.4 |
| Data Extraction (high) | – | 80.4% ±1.5, Mean of 3 runs, range 79.4 to 82.5 |
| Reasoning (low) | 53.0% | 35.1% ±2.6, Mean of 3 runs, range 32.5 to 37.8 |
| Reasoning (high) | 52.3% | 55.9% ±2.3, Mean of 3 runs, range 54.3 to 58.9 |
Claude Opus 4.8 vs MiMo V2.6 Pro: Overview
Claude Opus 4.8 is Anthropic's most capable generally available large language model, released on May 28, 2026 as an incremental upgrade to Claude Opus 4.7. The model accepts text and image inputs and produces text outputs, with a 1 million token context window on the Claude API, Amazon Bedrock, and Google Cloud Vertex AI (200k tokens on Microsoft Foundry) and up to 128k max output tokens. It uses adaptive thinking and supports adjustable effort tiers — high by default, with extra and max tiers available for more demanding tasks. A fast mode operates at approximately 2.5x standard speed. The model is described by Anthropic as a hybrid reasoning model designed for advanced coding, agentic workflows, long-context reasoning, and professional knowledge work.
Key behavioral improvements over Opus 4.7 include substantially reduced rates of unreported code flaws, improved honesty in self-assessment, and better tool-calling reliability. On Anthropic's Super-Agent benchmark, Opus 4.8 completes every case end-to-end, and it scores 84% on Online-Mind2Web for computer-use and browser-agent tasks. It achieves 88.6% on SWE-bench Verified and 69.2% on SWE-bench Pro. Alongside the model, Anthropic launched Dynamic Workflows in Claude Code (research preview), which enables Claude to orchestrate hundreds of parallel subagents for codebase-scale tasks such as large migrations. The Messages API was also updated to accept mid-task system messages without breaking prompt caching, improving support for long-running agentic pipelines.
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, Claude Opus 4.8 performed better. It scores higher on 5 of the six vision tasks and averages 68.7% (#31 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, Claude Opus 4.8 leads with 53.0% 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 $0.016. Claude Opus 4.8 is priced at $5.00 per 1M input tokens and $25.00 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.
Claude Opus 4.8 is faster. Across Roboflow's Vision Evals it averaged 5.2s 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 captioning and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.