Roboflow

Claude Haiku 4.5 vs MiMo V2.6 Flash

Compare Claude Haiku 4.5 and MiMo V2.6 Flash side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, OCR, Classification, and Object Detection.

Compare Claude Haiku 4.5 vs MiMo V2.6 Flash 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 Haiku 4.5
Run to compare this model.
MiMo V2.6 Flash
Run to compare this model.

Models in this comparison

Claude Haiku 4.5 vs MiMo V2.6 Flash Comparison Table

Evals updated September 22, 2026Pricing updated September 23, 2026

PropertyClaude Haiku 4.5MiMo V2.6 Flash
OrganizationAnthropicXiaomi
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateOct 2025Sep 2026
Context Window200K1.0M
Parameters309B total, 15B active
LicenseProprietaryMIT
Pricing per 1M tokens
Input $/1M$1.00$0.140
Output $/1M$5.00$0.280
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
OverallNot evaluated
60.6%
Avg cost / sample$0.0003
Avg speed / sample9.20s
By task
Object Detection (low)
37.8%
±1.1, Mean of 3 runs, range 36.4 to 38.7
$0.0004
Object Detection (high)
45.0%
±2.5, Mean of 3 runs, range 42.2 to 47.1
$0.0009
Counting (low)
49.5%
±8.1, Mean of 3 runs, range 41.9 to 58.1
$0.0002
Counting (high)
64.9%
±1.4, Mean of 3 runs, range 63.5 to 66.2
$0.0003
Identification (low)
76.0%
±3.1, Mean of 3 runs, range 71.9 to 78.1
$0.0001
Identification (high)
82.3%
±4.7, Mean of 3 runs, range 78.1 to 87.5
$0.0003
OCR (low)
87.0%
±0.5, Mean of 3 runs, range 86.6 to 87.7
$0.0003
OCR (high)
87.0%
±2.1, Mean of 3 runs, range 84.3 to 88.5
$0.0016
Data Extraction (low)
80.1%
±1.0, Mean of 3 runs, range 79.4 to 81.4
$0.0002
Data Extraction (high)
82.5%
±1.0, Mean of 3 runs, range 81.4 to 83.5
$0.0004
Reasoning (low)
33.1%
±2.0, Mean of 3 runs, range 31.1 to 35.1
$0.0002
Reasoning (high)
58.5%
±1.3, Mean of 3 runs, range 57.0 to 59.6
$0.0008

Claude Haiku 4.5 vs MiMo V2.6 Flash: Overview

Claude Haiku 4.5

Claude Haiku 4.5 is Anthropic’s lightweight model in the Claude 4.5 series, released in October 2025 under a proprietary license. Designed for speed and cost efficiency, it delivers near-frontier performance while maintaining Anthropic’s AI Safety Level 2 standard. Haiku 4.5 supports both text and multimodal (text and image) inputs, integrates tool use and extended reasoning, and features a 200,000 token context window, making it adept at handling long or complex workflows. Though the parameter count remains undisclosed, it achieves about 73.3% on SWE-bench Verified, reflecting strong coding and reasoning ability. Haiku 4.5 is ideal for developers and researchers seeking rapid, cost-effective model calls for analysis, coding, or multimodal understanding.

MiMo V2.6 Flash

MiMo-V2.6-Flash is the efficiency-oriented checkpoint of Xiaomi's MiMo-V2.6 series, a natively omnimodal foundation model that accepts text, image, video, and audio in a single model and supports a one million token context window. The language backbone is a sparse mixture-of-experts transformer with roughly 309 billion total parameters and 15 billion activated per token, organized as 48 layers with 256 routed experts and top-8 routing. It uses a hybrid attention scheme that interleaves sliding window attention with global attention layers to cut key-value cache cost on long sequences, and pairs the backbone with a vision encoder, an audio encoder, and an audio tokenizer, plus a multi-token prediction module and a draft model for faster decoding.

Training emphasizes large scale reinforcement learning on verifiable, long-horizon tasks, with RL compute, environment diversity, and grader compute scaled together in a single mixed run. Xiaomi reports gains during RL on SWE-bench Verified, Terminal Bench, a cybersecurity benchmark, and an internal visual coding benchmark, reflecting a focus on agentic coding, computer use, and multimodal document and screen understanding rather than single turn chat.

Frequently Asked Questions

Claude Haiku 4.5 has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.

Claude Haiku 4.5 is released under Proprietary, while MiMo V2.6 Flash uses MIT. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.

Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.