Model comparison
Microsoft MAI-Thinking-1 vs Xiaomi MiMo-V2.6-Pro
Microsoft against Xiaomi, compared on context, price, and verified benchmark results.
Catalog record checked September 26, 2026Individual provider fields may changeEvidence confidence: Medium — see receipts below
Microsoft
Microsoft MAI-Thinking-1
Frontier
Xiaomi
Xiaomi MiMo-V2.6-Pro
Frontier · Open weights
| Specification | Microsoft MAI-Thinking-1 | Xiaomi MiMo-V2.6-Pro |
|---|---|---|
| Provider | ||
| Provider | Microsoft | Xiaomi |
| Tier | ||
| Tier | Frontier | Frontier |
| Context window | ||
| Context window | 256K | Winner: 1.05M |
| Max output | ||
| Max output | 64K | Not verifiedUnverified |
| Input / 1M tokens | ||
| Input / 1M tokens | $2 | Winner: $0.435 |
| Output / 1M tokens | ||
| Output / 1M tokens | $8 | Winner: $0.87 |
| Weights | ||
| Weights | Closed | Open |
| Parameters | ||
| Parameters | ~1T total / 35B active (sparse MoE) | 1.02T total / 42B active (MoE) |
| Reasoning levels | ||
| Reasoning levels | Not verifiedUnverified | Not verifiedUnverified |
| Modalities | ||
| Modalities | text | text, image, video, audio |
| License | ||
| License | Not disclosedUnverified | MIT |
| API model id | ||
| API model id | mai-thinking-1 | mimo-v2.6-pro |
| Released | ||
| Released | June 2, 2026 | September 21, 2026 |
| AIME 2025 (2026-08-12) | ||
| AIME 2025 (2026-08-12) | 97 | Not verifiedUnverified |
| SWE-bench Verified (2026-06) | ||
| SWE-bench Verified (2026-06) | 73.5 | Not verifiedUnverified |
| GPQA Diamond (2026-06) | ||
| GPQA Diamond (2026-06) | 84.2 | Not verifiedUnverified |
| Terminal-Bench 2.0 (2026-06) | ||
| Terminal-Bench 2.0 (2026-06) | 46 | Not verifiedUnverified |
| Artificial Analysis Intelligence Index (2026-09-26) | ||
| Artificial Analysis Intelligence Index (2026-09-26) | Not verifiedUnverified | 46.3 |
| DeepSWE 1.1 (2026-09-21) | ||
| DeepSWE 1.1 (2026-09-21) | Not verifiedUnverified | 71.9 |
| Terminal-Bench 2.1 (2026-09-21) | ||
| Terminal-Bench 2.1 (2026-09-21) | Not verifiedUnverified | 89.9 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Benchmark receipts
Where each score comes from, and how far it can be compared across models.
- 2026-09-26Artificial Analysis
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check measuredAt.
- 2026-09-21Xiaomi MiMo-V2.6 announcement (vendor)
Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.
Directly comparable
- 2026-08-12Microsoft AI — Introducing MAI-Thinking-1 (vendor, 256k output)
Competition mathematics: AIME 2025 exam problems, typically pass@1 with tools disallowed or single-attempt code execution per the harness.
Comparable with caveat30-question total denominator: one problem = ~3.3 points. Tool-use policy (calculator/code interpreter) must match between compared models.
- 2026-06Microsoft AI model page (vendor, as labeled: Terminal-Bench 2.0 — NOT comparable to 2.1 observations)
Real GitHub issue resolution: does the model's patch pass the hidden tests.
Comparable with caveatPost-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.
Pricing tiers
Microsoft MAI-Thinking-1: Global Standard $2.00/$8.00 per MTok, cached input $0.20 (Azure Retail Prices API, effective 2026-08-01, uniform across 32 public regions; US Gov regions carry premiums). Still preview; Global Standard only, no PTU yet. 256K context with a 64K output cap that includes reasoning tokens. ~1T/35B sparse MoE; reasoning is always on (no effort levels). Microsoft's first true reasoning model, trained without OpenAI distillation.
Xiaomi MiMo-V2.6-Pro: $0.435/$0.87 per MTok (cache hit $0.0036; cache writes free for a limited time) — unchanged from V2.5-Pro. UltraSpeed mode (mimo-v2.6-pro-ultraspeed) costs $4.35/$8.70. Open weights (MIT) as MiMo-V2.6-Pro-RL.
Microsoft MAI-Thinking-1
MAI-Thinking-1 is Microsoft's first in-house reasoning model, reducing dependence on OpenAI.
Best for
- Enterprise (Azure)
- Reasoning
- Microsoft ecosystem
Watch out
Foundry-gated preview; no MAI-2 exists yet. Of the Build 2026 MAI wave, only MAI-Thinking-1 and MAI-Cyber-1-Flash ($0.60/$3.50, released 2026-07-27) have published token meters.
Xiaomi MiMo-V2.6-Pro
MiMo-V2.6-Pro is Xiaomi's open-weight (MIT) 1.02T MoE with omni-modal input and a 1M context — the top open-weight model on the Artificial Analysis index at under $1 per million output tokens.
Best for
- Open-weight frontier work
- Omni-modal input
- Cheap long-context API
Watch out
Max output is not verified; vendor benchmark claims (DeepSWE 71.9) are not yet independently reproduced.
When the cheaper one wins
Xiaomi MiMo-V2.6-Pro is cheaper on output at $0.87 per million tokens against $8 for Microsoft MAI-Thinking-1 — about 9.2×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. These are standard-tier API rates, excluding batch and cache discounts.
Run the model pickerEvidence confidence: Medium
How strong and complete the evidence behind this comparison is — not a prediction of which model is better.
- Pricing verified on both sides — Input and output rates are verified for both models.
- 3/5 core specs verified on both sides — Not published for at least one side: max output, reasoning levels.
- No shared named benchmark — No benchmark has been measured on both models.
- Verified within the last 90 days — Newest catalog check was 2 days ago.
- Both models carry source citations — Each side has at least two catalog sources on record.
Source receipts
Each catalog figure was checked against the provider or an independent second source on the date shown.
- Microsoft MAI-Thinking-1: Azure Retail Prices API (MAI-Thinking-1 $2/$0.20 cached/$8) (accessed 2026-09-04)
- Microsoft MAI-Thinking-1: AzureSpeed — AI model pricing tracker (same figures) (accessed 2026-09-04)
- Microsoft MAI-Thinking-1: Microsoft — Introducing MAI-Thinking-1 (~1T total / 35B active MoE) (accessed 2026-09-05)
- Microsoft MAI-Thinking-1: Microsoft Foundry — MAI-Thinking-1 usage (256K context, 64K output cap) (accessed 2026-09-05)
- Xiaomi MiMo-V2.6-Pro: Xiaomi — MiMo-V2.6 (pricing, benchmarks) (accessed 2026-09-26)
- Xiaomi MiMo-V2.6-Pro: Hugging Face — XiaomiMiMo/MiMo-V2.6-Pro-RL (MIT, 1.02T/42B, 1M) (accessed 2026-09-26)
Related comparisons
- DeepSeek V4.1 Flash vs Xiaomi MiMo-V2.6-Pro
- Claude Fable 5.1 vs Microsoft MAI-Thinking-1
- Claude Fable 5.1 vs Xiaomi MiMo-V2.6-Pro
- Claude Fable 5 vs Microsoft MAI-Thinking-1
- Claude Fable 5 vs Xiaomi MiMo-V2.6-Pro
- Claude Mythos 5.1 vs Microsoft MAI-Thinking-1
Diving deeper on one model? Microsoft MAI-Thinking-1 · Xiaomi MiMo-V2.6-Pro
Common questions
Microsoft MAI-Thinking-1 vs Xiaomi MiMo-V2.6-Pro
Answered from the verified figures on this page rather than general guidance.
Is Microsoft MAI-Thinking-1 or Xiaomi MiMo-V2.6-Pro cheaper for input?
Is Microsoft MAI-Thinking-1 or Xiaomi MiMo-V2.6-Pro cheaper for output?
Which has the larger context window, Microsoft MAI-Thinking-1 or Xiaomi MiMo-V2.6-Pro?
Should I use Microsoft MAI-Thinking-1 or Xiaomi MiMo-V2.6-Pro?
Can I self-host Microsoft MAI-Thinking-1 or Xiaomi MiMo-V2.6-Pro?
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Input price: Xiaomi MiMo-V2.6-Pro costs $0.435 per 1M tokens versus $2 for Microsoft MAI-Thinking-1 — a 4.6x difference at the headline tier.
- Context: Xiaomi MiMo-V2.6-Pro takes 1.05M against 256K for Microsoft MAI-Thinking-1 — only decisive if your prompts approach the smaller window.
- Deployment: Xiaomi MiMo-V2.6-Pro publishes weights you can self-host; the other is API-only.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.