Model comparison
Gemini 3.1 Pro vs Microsoft MAI-Thinking-1
Google against Microsoft, compared on context, price, and verified benchmark results.
Catalog record checked September 5, 2026Individual provider fields may changeEvidence confidence: High — see receipts below
Gemini 3.1 Pro
Frontier
Microsoft
Microsoft MAI-Thinking-1
Frontier
| Specification | Gemini 3.1 Pro | Microsoft MAI-Thinking-1 |
|---|---|---|
| Provider | Microsoft | |
| Tier | Frontier | Frontier |
| Context window | Winner: 1.05M | 256K |
| Max output | Winner: 66K | 64K |
| Input / 1M tokens | $2 | $2 |
| Output / 1M tokens | $12 | Winner: $8 |
| Weights | Closed | Closed |
| Parameters | Not disclosedUnverified | ~1T total / 35B active (sparse MoE) |
| Reasoning levels | low, medium, high | low, medium, high |
| Modalities | text, image, video, audio, pdf | text |
| API model id | gemini-3.1-pro-preview | mai-thinking-1 |
| Released | February 19, 2026 | June 2, 2026 |
| Artificial Analysis Intelligence Index (2026-08-14) | Winner: 60 | 55 |
| SWE-bench Verified (2026-02) | Winner: 80.6 | 73.5 |
| GPQA Diamond (2026-02) | Winner: 94.3 | 84.2 |
| Humanity's Last Exam (2026-02) | 44.4 | Not verifiedUnverified |
| Terminal-Bench 2.1 (2026-07) | 73.8 | Not verifiedUnverified |
| AIME 2025 (2026-08-12) | Not verifiedUnverified | 97 |
| Terminal-Bench 2.0 (2026-06) | Not verifiedUnverified | 46 |
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
- 2026-08-14: Artificial AnalysisComposite index blending reasoning, knowledge, and coding evals into one 0–100 score. Comparability: directly comparable — AA occasionally rebaselines the index scale between snapshots — a score captured on one date is only comparable to same-snapshot scores (check measuredAt).
- 2026-08-12: Microsoft 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. Comparability: comparable with caveat — 30-question total denominator: one problem = ~3.3 points. Tool-use policy (calculator/code interpreter) must match between compared models.
- 2026-07: Google DeepMind model evaluation report (vendor, Terminus-2; own Feb report separately reported TB 2.0 = 68.5 — versions preserved)Agentic terminal work: multi-step tasks executed in a sandboxed shell environment. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
- 2026-06: Microsoft 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. Comparability: comparable with caveat — Post-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.
- 2026-02: Google DeepMind model evaluation report (vendor, no tools; 51.4 with search+code)Real GitHub issue resolution: does the model's patch pass the hidden tests. Comparability: comparable with caveat — Post-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: Gemini 3.1 Pro: $2/$12 per MTok (≤200K); $4/$18 (>200K). Still 'Preview' as of 2026-08-29; 3.5 Pro not yet released. · 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 ~22 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.
Gemini 3.1 Pro
Gemini 3.1 Pro is Google's current flagship Pro model with record benchmark scores and a 1M-token context.
Best for
- Very long documents
- Multimodal reasoning
- Frontier knowledge work
Watch out
Still in Preview; 3.5 Pro is 'coming soon' but unreleased as of 2026-08-29.
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.
When the cheaper one wins
Microsoft MAI-Thinking-1 is cheaper on output at $8 per million tokens against $12 for Gemini 3.1 Pro — about 1.5×. 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: High
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.
- 4/5 core specs verified on both sides — Not published for at least one side: parameter count.
- 3 shared named benchmarks with differing scores — Measured on: Artificial Analysis Intelligence Index, SWE-bench Verified, GPQA Diamond.
- Verified within the last 90 days — Newest catalog check was 6 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.
- Gemini 3.1 Pro: Google Cloud — Gemini 3.1 Pro (accessed 2026-08-29)
- Gemini 3.1 Pro: TechCrunch — Gemini 3.1 Pro (accessed 2026-08-29)
- 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)
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Diving deeper on one model? Gemini 3.1 Pro · Microsoft MAI-Thinking-1
Common questions
Gemini 3.1 Pro vs Microsoft MAI-Thinking-1
Answered from the verified figures on this page rather than general guidance.
Is Gemini 3.1 Pro or Microsoft MAI-Thinking-1 cheaper for input?
Both cost $2 per million input tokens at standard rates, so input price is not a deciding factor between them.
Is Gemini 3.1 Pro or Microsoft MAI-Thinking-1 cheaper for output?
Microsoft MAI-Thinking-1 is cheaper at $8 per million output tokens, against $12 for Gemini 3.1 Pro — roughly 1.5× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Gemini 3.1 Pro has tiered pricing: $2/$12 per MTok (≤200K); $4/$18 (>200K). Still 'Preview' as of 2026-08-29; 3.5 Pro not yet released. Microsoft MAI-Thinking-1 has tiered pricing: Global Standard $2.00/$8.00 per MTok, cached input $0.20 (Azure Retail Prices API, effective 2026-08-01, uniform across ~22 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.
Which has the larger context window, Gemini 3.1 Pro or Microsoft MAI-Thinking-1?
Gemini 3.1 Pro accepts 1.05M tokens against 256K for Microsoft MAI-Thinking-1. This only matters if you routinely send very long documents or large codebases.
Do Gemini 3.1 Pro and Microsoft MAI-Thinking-1 support the same reasoning levels?
Yes — both accept the same effort settings: "low", "medium", "high". Higher effort costs more and takes longer, so start low and raise it only where output quality actually improves.
Should I use Gemini 3.1 Pro or Microsoft MAI-Thinking-1?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. Gemini 3.1 Pro suits very long documents; Microsoft MAI-Thinking-1 suits enterprise (azure).
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Context: Gemini 3.1 Pro takes 1.05M against 256K for Microsoft MAI-Thinking-1 — only decisive if your prompts approach the smaller window.
- Measured capability: Gemini 3.1 Pro leads Artificial Analysis Intelligence Index 60 to 55 (measured 2026-08-14).
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.