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Claude Fable 5.2 vs MiMo V2.6 Flash

Claude Fable 5.2 and MiMo V2.6 Flash score 86.5% vs 49.2% averaged across five Vision Evals tasks (#2 and #59 of 67 models), so Claude Fable 5.2 leads overall.

  • Claude Fable 5.2 wins all 5 tasks, with its widest lead on reasoning (88.1% vs 33.1%).
  • MiMo V2.6 Flash is under a tenth of the cost: $0.0003 vs $0.031 overall average cost per sample.
  • Claude Fable 5.2 is faster: 9.3s vs 16.3s per sample.

Compare Claude Fable 5.2 vs MiMo V2.6 Flash live

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

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

Claude Fable 5.2 vs MiMo V2.6 Flash on Vision Evals

Score by taskClaude Fable 5.2MiMo V2.6 Flash
Reasoning
88.1
33.1
Object Detection
81.6
37.8
Counting
86.5
49.5
OCR
79.5
49.7
Identification
96.9
76.0

Roboflow Vision Evals. Full breakdown below.

Claude Fable 5.2 vs MiMo V2.6 Flash Comparison Table

Evals updated November 10, 2026Pricing updated November 10, 2026

PropertyClaude Fable 5.2MiMo V2.6 Flash
OrganizationAnthropicXiaomi
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateNov 2026Sep 2026
Context Window1.0M1.0M
ParametersUnknown309B total, 15B active
LicenseProprietaryMIT
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1M$8.00$0.140
Output $/1M$40.00$0.280
Vision Tasks
CaptioningSupportedDemo
ClassificationSupportedDemo
Object DetectionSupportedDemo
OCRSupportedDemo
Visual Question AnsweringSupportedDemo
Both support 5 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
86.5%
49.2%
Avg cost / sample$0.031$0.0003
Avg speed / sample9.33s16.34s
By task
Object Detection (low)
81.6%
±0.3, Mean of 3 runs, range 81.2 to 81.9
$0.048
37.8%
±1.1, Mean of 3 runs, range 36.4 to 38.7
$0.0004
Object Detection (high)
83.3%
±0.5, Mean of 3 runs, range 82.8 to 83.7
$0.055
45.0%
±2.5, Mean of 3 runs, range 42.2 to 47.1
$0.0009
Counting (low)
86.5%
±0.0, Mean of 3 runs, range 86.5 to 86.5
$0.015
49.5%
±8.1, Mean of 3 runs, range 41.9 to 58.1
$0.0002
Counting (high)
92.8%
±1.4, Mean of 3 runs, range 91.9 to 94.6
$0.021
64.9%
±1.4, Mean of 3 runs, range 63.5 to 66.2
$0.0003
Identification (low)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.011
76.0%
±3.1, Mean of 3 runs, range 71.9 to 78.1
$0.0001
Identification (high)
94.8%
±1.6, Mean of 3 runs, range 93.8 to 96.9
$0.012
82.3%
±4.7, Mean of 3 runs, range 78.1 to 87.5
$0.0003
OCR (low)
79.5%
$0.030
49.7%
$0.0004
by category
Single value
64.8%
Transcription
94.7%
Structured JSON
92.8%
Text localization
74.5%
Single value
41.7%
Transcription
72.5%
Structured JSON
68.6%
Text localization
9.2%
OCR (high)
84.8%
$0.043
50.4%
$0.0012
by category
Single value
77.4%
Transcription
97.9%
Structured JSON
94.1%
Text localization
72.9%
Single value
42.2%
Transcription
68.9%
Structured JSON
69.2%
Text localization
13.2%
Reasoning (low)
88.1%
±1.3, Mean of 3 runs, range 86.8 to 89.4
$0.016
33.1%
±2.0, Mean of 3 runs, range 31.1 to 35.1
$0.0002
Reasoning (high)
92.0%
±1.0, Mean of 3 runs, range 91.4 to 93.4
$0.024
58.5%
±1.3, Mean of 3 runs, range 57.0 to 59.6
$0.0008

Claude Fable 5.2 vs MiMo V2.6 Flash: Overview

Claude Fable 5.2

Claude Fable 5.2 is a proprietary multimodal model from Anthropic, released on November 9, 2026. It accepts text and images and produces text, with a one million token context window and up to 128,000 output tokens. Adaptive thinking is always enabled, with an effort setting that controls reasoning. Anthropic positions Fable for demanding reasoning and long-running technical work, including coding and analysis of visual inputs.

Fable 5.2 succeeds Fable 5.1. Anthropic reports improvements in coding, reasoning, and vision, with output generation up to 30% faster than its predecessor. Fable and Mythos 5.2 share an underlying model, with additional cybersecurity and biology safeguards applied to Fable. Model weights are not publicly released.

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.

Frequently Asked Questions

On Roboflow's Vision Evals, Claude Fable 5.2 performed better. It scores higher on all five vision tasks and averages 86.5% (#2 of 67) against 49.2% (#59 of 67) for MiMo V2.6 Flash. 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 Fable 5.2 leads with 88.1% against 33.1%. This is the widest gap between the two models across the benchmark's tasks.

MiMo V2.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0003 per sample against $0.031. Claude Fable 5.2 is priced at $8.00 per 1M input tokens and $40.00 per 1M output; MiMo V2.6 Flash is priced at $0.14 per 1M input tokens and $0.28 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Claude Fable 5.2 is faster. Across Roboflow's Vision Evals it averaged 9.3s per inference against 16.3s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.