Roboflow

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.

Compare Claude Sonnet 5.5 vs MiMo V2.6 Pro live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Detect and compare bounding boxes across models on the same image.

Open Object Detection in the full playground
AnthropicClaude Sonnet 5.5
Run to compare this model.
MiMo V2.6 Pro
Run to compare this model.

Claude Sonnet 5.5 vs MiMo V2.6 Pro on Vision Evals

Score by taskClaude Sonnet 5.5MiMo V2.6 Pro
Reasoning
76.4
35.1
Object Detection
74.3
42.0
Counting
79.3
50.0
OCR
66.3
48.6
Identification
91.7
76.0

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

PropertyClaude Sonnet 5.5MiMo V2.6 Pro
OrganizationAnthropicXiaomi
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateSep 2026Sep 2026
Context Window1.0M1.0M
ParametersUnknown1.02T total, 42B active
LicenseProprietaryMIT
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1M$2.00$0.435
Output $/1M$10.00$0.870
Vision Tasks
Both support 10 more 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 / sample8.33s9.45s
By task
Object Detection (low)
74.3%
±0.9, Mean of 3 runs, range 73.5 to 75.3
$0.0098
42.0%
±1.1, Mean of 3 runs, range 40.9 to 43.1
$0.0014
Object Detection (high)
76.8%
±0.4, Mean of 3 runs, range 76.5 to 77.3
$0.014
46.7%
±0.8, Mean of 3 runs, range 45.7 to 47.3
$0.0030
Counting (low)
79.3%
±0.7, Mean of 3 runs, range 78.4 to 79.7
$0.0042
50.0%
±2.0, Mean of 3 runs, range 48.6 to 52.7
$0.0005
Counting (high)
82.9%
±1.4, Mean of 3 runs, range 81.1 to 83.8
$0.0053
59.0%
±5.4, Mean of 3 runs, range 52.7 to 63.5
$0.0016
Identification (low)
91.7%
±3.1, Mean of 3 runs, range 87.5 to 93.8
$0.0029
76.0%
±1.6, Mean of 3 runs, range 75.0 to 78.1
$0.0004
Identification (high)
90.6%
±0.0, Mean of 3 runs, range 90.6 to 90.6
$0.0033
78.1%
±4.7, Mean of 3 runs, range 71.9 to 81.3
$0.0012
OCR (low)
66.3%
$0.0075
48.6%
$0.0012
by category
Single value
48.7%
Transcription
76.3%
Structured JSON
83.8%
Text localization
60.6%
Single value
37.0%
Transcription
68.2%
Structured JSON
67.7%
Text localization
18.7%
OCR (high)–
49.9%
$0.0034
by category
Single value
40.4%
Transcription
59.9%
Structured JSON
70.2%
Text localization
16.8%
Reasoning (low)
76.4%
±0.7, Mean of 3 runs, range 75.5 to 76.8
$0.0049
35.1%
±2.6, Mean of 3 runs, range 32.5 to 37.8
$0.0005
Reasoning (high)
83.9%
±1.7, Mean of 3 runs, range 82.1 to 85.4
$0.0061
55.9%
±2.3, Mean of 3 runs, range 54.3 to 58.9
$0.0026

Claude Sonnet 5.5 vs MiMo V2.6 Pro: Overview

Claude Sonnet 5.5

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

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.