Claude Sonnet 5.5 vs MiMo V2.6 Pro
Claude Sonnet 5.5 and MiMo V2.6 Pro score 77.6% vs 50.3% averaged across five Vision Evals tasks (#13 and #59 of 68 models), so Claude Sonnet 5.5 leads overall.
- Claude Sonnet 5.5 wins all 5 tasks, with its widest lead on reasoning (76.4% vs 35.1%).
- MiMo V2.6 Pro is about 1/7 of the cost: $0.0011 vs $0.0073 overall average cost per sample.
- Claude Sonnet 5.5 is faster: 8.3s vs 9.5s per sample.
Roboflow Vision Evals. Full breakdown below.
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Claude Sonnet 5.5 vs MiMo V2.6 Pro on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Sonnet 5.5 vs MiMo V2.6 Pro Comparison Table
Evals updated November 10, 2026Pricing updated November 12, 2026
| Property | Claude Sonnet 5.5 | MiMo V2.6 Pro |
|---|---|---|
| Organization | Anthropic | Xiaomi |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | Unknown | 1.02T total, 42B active |
| License | Proprietary | MIT |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.435 |
| Output $/1M | $10.00 | $0.870 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 77.6% | 50.3% |
| Avg cost / sample | $0.0073 | $0.0011 |
| Avg speed / sample | 8.33s | 9.45s |
| By task | ||
| Object Detection (low) | 74.3% ±0.9, Mean of 3 runs, range 73.5 to 75.3 | 42.0% ±1.1, Mean of 3 runs, range 40.9 to 43.1 |
| Object Detection (high) | 76.8% ±0.4, Mean of 3 runs, range 76.5 to 77.3 | 46.7% ±0.8, Mean of 3 runs, range 45.7 to 47.3 |
| Counting (low) | 79.3% ±0.7, Mean of 3 runs, range 78.4 to 79.7 | 50.0% ±2.0, Mean of 3 runs, range 48.6 to 52.7 |
| Counting (high) | 82.9% ±1.4, Mean of 3 runs, range 81.1 to 83.8 | 59.0% ±5.4, Mean of 3 runs, range 52.7 to 63.5 |
| Identification (low) | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 | 76.0% ±1.6, Mean of 3 runs, range 75.0 to 78.1 |
| Identification (high) | 90.6% ±0.0, Mean of 3 runs, range 90.6 to 90.6 | 78.1% ±4.7, Mean of 3 runs, range 71.9 to 81.3 |
| OCR (low) | 66.3% | 48.6% |
| by category |
|
|
| OCR (high) | – | 49.9% |
| by category |
| |
| Reasoning (low) | 76.4% ±0.7, Mean of 3 runs, range 75.5 to 76.8 | 35.1% ±2.6, Mean of 3 runs, range 32.5 to 37.8 |
| Reasoning (high) | 83.9% ±1.7, Mean of 3 runs, range 82.1 to 85.4 | 55.9% ±2.3, Mean of 3 runs, range 54.3 to 58.9 |
Claude Sonnet 5.5 vs MiMo V2.6 Pro: Overview
Claude Sonnet 5.5 is a proprietary multimodal language model from Anthropic and the second release in the Claude 5.5 family, following Claude Opus 5.5. It accepts interleaved text and image input and returns text, operating with a 1M token context window and a maximum output of 128K tokens per request. The model uses adaptive thinking by default, allocating variable reasoning effort per request rather than exposing a manual extended thinking toggle, and its training data cutoff is June 2026. Anthropic positions it as a faster, lower cost complement to Opus 5.5 for well scoped everyday tasks, bug fixing, and producing documents, slides, and spreadsheets.
Anthropic describes it as the first Sonnet model able to complete Pokemon Red from screenshots alone, and it generates output more than 30% faster than Sonnet 5 while using fewer tokens for equivalent work.
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.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Sonnet 5.5 performed better. It scores higher on all five vision tasks and averages 77.6% (#13 of 68) against 50.3% (#59 of 68) 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 Sonnet 5.5 leads with 76.4% 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 $0.0011 per sample against $0.0073. Claude Sonnet 5.5 is priced at $2.00 per 1M input tokens and $10.00 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 Sonnet 5.5 is faster. Across Roboflow's Vision Evals it averaged 8.3s per inference against 9.5s. 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 OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.