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GPT-6.1 Terra vs MiMo V2.6 Pro

GPT-6.1 Terra and MiMo V2.6 Pro score 85.8% vs 50.3% averaged across five Vision Evals tasks (#2 and #56 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 35.1%).
  • MiMo V2.6 Pro is about a third of the cost: $0.0011 vs $0.0033 overall average cost per sample.
  • MiMo V2.6 Pro is faster: 9.5s vs 10.1s per sample.

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

Score by taskGPT-6.1 TerraMiMo V2.6 Pro
Reasoning
85.4
35.1
Object Detection
84.7
42.0
OCR
81.9
48.6
Counting
80.2
50.0
Identification
96.9
76.0

Roboflow Vision Evals. Full breakdown below.

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

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

PropertyGPT-6.1 TerraMiMo V2.6 Pro
OrganizationOpenAIXiaomi
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateOct 2026Sep 2026
Context Window1.1M1.0M
Parametersundisclosed1.02T total, 42B active
LicenseProprietaryMIT
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1M$1.00$0.435
Output $/1M$5.00$0.870
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%
50.3%
Avg cost / sample$0.0033$0.0011
Avg speed / sample10.05s9.45s
By task
Object Detection (low)
84.7%
±0.1, Mean of 3 runs, range 84.7 to 84.8
$0.0051
42.0%
±1.1, Mean of 3 runs, range 40.9 to 43.1
$0.0014
Object Detection (high)
84.7%
±0.4, Mean of 3 runs, range 84.2 to 84.9
$0.0081
46.7%
±0.8, Mean of 3 runs, range 45.7 to 47.3
$0.0030
Counting (low)
80.2%
±2.0, Mean of 3 runs, range 78.4 to 82.4
$0.0018
50.0%
±2.0, Mean of 3 runs, range 48.6 to 52.7
$0.0005
Counting (high)
84.2%
±3.4, Mean of 3 runs, range 81.1 to 87.8
$0.0029
59.0%
±5.4, Mean of 3 runs, range 52.7 to 63.5
$0.0016
Identification (low)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0013
76.0%
±1.6, Mean of 3 runs, range 75.0 to 78.1
$0.0004
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0014
78.1%
±4.7, Mean of 3 runs, range 71.9 to 81.3
$0.0012
OCR (low)
81.9%
$0.0033
48.6%
$0.0012
by category
Single value
70.9%
Transcription
96.7%
Structured JSON
91.5%
Text localization
77.6%
Single value
37.0%
Transcription
68.2%
Structured JSON
67.7%
Text localization
18.7%
OCR (high)
84.4%
$0.0066
49.9%
$0.0034
by category
Single value
74.8%
Transcription
97.0%
Structured JSON
93.4%
Text localization
79.2%
Single value
40.4%
Transcription
59.9%
Structured JSON
70.2%
Text localization
16.8%
Reasoning (low)
85.4%
±1.7, Mean of 3 runs, range 83.4 to 86.8
$0.0016
35.1%
±2.6, Mean of 3 runs, range 32.5 to 37.8
$0.0005
Reasoning (high)
87.0%
±1.3, Mean of 3 runs, range 85.4 to 88.1
$0.0023
55.9%
±2.3, Mean of 3 runs, range 54.3 to 58.9
$0.0026

GPT-6.1 Terra vs MiMo V2.6 Pro: 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 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, GPT-6.1 Terra performed better. It scores higher on all five vision tasks and averages 85.8% (#2 of 65) against 50.3% (#56 of 65) 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, GPT-6.1 Terra leads with 85.4% 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 $0.0033. GPT-6.1 Terra is priced at $1.00 per 1M input tokens and $5.00 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.

MiMo V2.6 Pro is faster. Across Roboflow's Vision Evals it averaged 9.5s per inference against 10.1s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.