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Model comparison

Microsoft MAI-Thinking-1 vs Muse Spark 1.2

Microsoft against Meta, 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

vs

Meta

Muse Spark 1.2

Frontier

AI model capability comparison
SpecificationMicrosoft MAI-Thinking-1Muse Spark 1.2
ProviderMicrosoftMeta
TierFrontierFrontier
Context window256KWinner: 1.05M
Max output64KNot verifiedUnverified
Input / 1M tokens$2Winner: $1.25
Output / 1M tokens$8Winner: $4.25
WeightsClosedClosed
Parameters~1T total / 35B active (sparse MoE)Meta frontier model
Reasoning levelsNot verifiedUnverifiedminimal, low, medium, high, xhigh, max
Modalitiestexttext, image, video, audio, pdf
API model idmai-thinking-1muse-spark-1.2
ReleasedJune 2, 2026August 5, 2026
AIME 2025 (2026-08-12)97Not verifiedUnverified
SWE-bench Verified (2026-06)73.5Not verifiedUnverified
GPQA Diamond (2026-06)84.2Not verifiedUnverified
Terminal-Bench 2.0 (2026-06)46Not verifiedUnverified
Artificial Analysis Intelligence Index [xhigh] (2026-09-26)Not verifiedUnverified39.6

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.

  • 2026-09-26
    Artificial 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.

  • 2026-08-12
    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.

    Comparable with caveat30-question total denominator: one problem = ~3.3 points. Tool-use policy (calculator/code interpreter) must match between compared models.

  • 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.

    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.

Muse Spark 1.2: $1.25/$4.25 per MTok on Meta's Model API (Standard tier shared with Muse Spark 1.3; cached input $0.15). Closed weights.

FrontierRecord checked September 26, 2026

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.

FrontierRecord checked September 26, 2026

Muse Spark 1.2

Muse Spark 1.2 is Meta's closed frontier model until the 1.3 refresh (2026-09-02) — still served and ranked in the LMArena text top 10 as of mid-2026.

Best for

  • Frontier reasoning
  • Meta ecosystem
  • Agentic work

Watch out

Closed-weights (unlike Llama 4); Meta's post-Llama-4 frontier branding.

When the cheaper one wins

Muse Spark 1.2 is cheaper on output at $4.25 per million tokens against $8 for Microsoft MAI-Thinking-1 — about 1.9×. 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.

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Evidence 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.

Related comparisons

Diving deeper on one model? Microsoft MAI-Thinking-1 · Muse Spark 1.2

Common questions

Microsoft MAI-Thinking-1 vs Muse Spark 1.2

Answered from the verified figures on this page rather than general guidance.

Is Microsoft MAI-Thinking-1 or Muse Spark 1.2 cheaper for input?
Muse Spark 1.2 is cheaper at $1.25 per million input tokens, against $2 for Microsoft MAI-Thinking-1 — roughly 1.6× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; 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 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. Muse Spark 1.2 has tiered pricing: $1.25/$4.25 per MTok on Meta's Model API (Standard tier shared with Muse Spark 1.3; cached input $0.15). Closed weights.
Is Microsoft MAI-Thinking-1 or Muse Spark 1.2 cheaper for output?
Muse Spark 1.2 is cheaper at $4.25 per million output tokens, against $8 for Microsoft MAI-Thinking-1 — roughly 1.9× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; 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 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. Muse Spark 1.2 has tiered pricing: $1.25/$4.25 per MTok on Meta's Model API (Standard tier shared with Muse Spark 1.3; cached input $0.15). Closed weights.
Which has the larger context window, Microsoft MAI-Thinking-1 or Muse Spark 1.2?
Muse Spark 1.2 accepts 1.05M tokens against 256K for Microsoft MAI-Thinking-1. This only matters if you routinely send very long documents or large codebases.
Should I use Microsoft MAI-Thinking-1 or Muse Spark 1.2?
Both sit in the frontier tier, so the choice usually comes down to price and context rather than capability. Microsoft MAI-Thinking-1 suits enterprise (azure); Muse Spark 1.2 suits frontier reasoning.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

  • Input price: Muse Spark 1.2 costs $1.25 per 1M tokens versus $2 for Microsoft MAI-Thinking-1 — a 1.6x difference at the headline tier.
  • Context: Muse Spark 1.2 takes 1.05M against 256K for Microsoft MAI-Thinking-1 — only decisive if your prompts approach the smaller window.
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