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GPT-6.1 Terra vs MiMo V2.6 Flash

GPT-6.1 Terra and MiMo V2.6 Flash score 85.8% vs 49.2% averaged across five Vision Evals tasks (#2 and #57 of 65 models), so GPT-6.1 Terra leads overall.

  • GPT-6.1 Terra wins all 5 tasks, with its widest lead on reasoning (85.4% vs 33.1%).
  • MiMo V2.6 Flash is under a tenth of the cost: $0.0003 vs $0.0033 overall average cost per sample.
  • GPT-6.1 Terra is faster: 10.1s vs 16.3s per sample.

Compare GPT-6.1 Terra 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.

GPT-6.1 Terra vs MiMo V2.6 Flash on Vision Evals

Score by taskGPT-6.1 TerraMiMo V2.6 Flash
Reasoning
85.4
33.1
Object Detection
84.7
37.8
OCR
81.9
49.7
Counting
80.2
49.5
Identification
96.9
76.0

Roboflow Vision Evals. Full breakdown below.

GPT-6.1 Terra vs MiMo V2.6 Flash Comparison Table

Evals updated October 26, 2026Pricing updated October 27, 2026

PropertyGPT-6.1 TerraMiMo V2.6 Flash
OrganizationOpenAIXiaomi
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateOct 2026Sep 2026
Context Window1.1M1.0M
Parametersundisclosed309B total, 15B active
LicenseProprietaryMIT
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1M$1.00$0.140
Output $/1M$5.00$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
85.8%
49.2%
Avg cost / sample$0.0033$0.0003
Avg speed / sample10.05s16.34s
By task
Object Detection (low)
84.7%
±0.1, Mean of 3 runs, range 84.7 to 84.8
$0.0051
37.8%
±1.1, Mean of 3 runs, range 36.4 to 38.7
$0.0004
Object Detection (high)
84.7%
±0.4, Mean of 3 runs, range 84.2 to 84.9
$0.0081
45.0%
±2.5, Mean of 3 runs, range 42.2 to 47.1
$0.0009
Counting (low)
80.2%
±2.0, Mean of 3 runs, range 78.4 to 82.4
$0.0018
49.5%
±8.1, Mean of 3 runs, range 41.9 to 58.1
$0.0002
Counting (high)
84.2%
±3.4, Mean of 3 runs, range 81.1 to 87.8
$0.0029
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
$0.0013
76.0%
±3.1, Mean of 3 runs, range 71.9 to 78.1
$0.0001
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0014
82.3%
±4.7, Mean of 3 runs, range 78.1 to 87.5
$0.0003
OCR (low)
81.9%
$0.0033
49.7%
$0.0004
by category
Single value
70.9%
Transcription
96.7%
Structured JSON
91.5%
Text localization
77.6%
Single value
41.7%
Transcription
72.5%
Structured JSON
68.6%
Text localization
9.2%
OCR (high)
84.4%
$0.0066
50.4%
$0.0012
by category
Single value
74.8%
Transcription
97.0%
Structured JSON
93.4%
Text localization
79.2%
Single value
42.2%
Transcription
68.9%
Structured JSON
69.2%
Text localization
13.2%
Reasoning (low)
85.4%
±1.7, Mean of 3 runs, range 83.4 to 86.8
$0.0016
33.1%
±2.0, Mean of 3 runs, range 31.1 to 35.1
$0.0002
Reasoning (high)
87.0%
±1.3, Mean of 3 runs, range 85.4 to 88.1
$0.0023
58.5%
±1.3, Mean of 3 runs, range 57.0 to 59.6
$0.0008

GPT-6.1 Terra vs MiMo V2.6 Flash: Overview

GPT-6.1 Terra

GPT-6.1 Terra is an OpenAI model released on October 26, 2026 for visual reasoning, computer use and writing. It accepts text and image inputs and produces text, with a 1,050,000-token context window and up to 128,000 output tokens. OpenAI lists applications including matching product photographs to catalog entries, interpreting technical drawings and operating application screens.

The API exposes six reasoning effort settings: none, low, medium, high, xhigh and max. Medium is the default. The model supports structured outputs and tool calling through the Responses API. Vision Evals reports its Low and High results for text understanding, counting, identification, reasoning and object detection, using native OpenAI API inference. Terra is a proprietary model; its weights are not 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, GPT-6.1 Terra performed better. It scores higher on all five vision tasks and averages 85.8% (#2 of 65) against 49.2% (#57 of 65) 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, GPT-6.1 Terra leads with 85.4% 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 $0.0033. GPT-6.1 Terra is priced at $1.00 per 1M input tokens and $5.00 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.

GPT-6.1 Terra is faster. Across Roboflow's Vision Evals it averaged 10.1s per inference against 16.3s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.