Pick Gemini 2.5 Pro for 1m context via api or strong multimodal reasoning. Pick MAI-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%) or microsoft's first in-house flagship reasoner, trained without openai distillation. On a tight budget at scale, MAI-Thinking-1 is the value pick.
Gemini 2.5 Pro (Google) and MAI-Thinking-1 (Microsoft) are two of the models people most often weigh against each other in 2026. Gemini 2.5 Pro is google's previous-gen 2M flagship — still a strong long-context multimodal option. MAI-Thinking-1 is microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: Gemini 2.5 Pro holds 3.9× more — 1M (~1,500 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: MAI-Thinking-1 is the newer model by about 12 months (released June 2, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Gemini 2.5 Pro
MAI-Thinking-1
Provider
Google (US)
Microsoft (US)
Released
June 2025
June 2, 2026
Context window
1M (~1,500 pages)
256K (~384 pages)
Price (in/out)
$1.25/$10 per 1M tokens
Not published
Open weight?
No — API only
No — API only
Modalities
text, image, audio, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
1M context via API: Gemini 2.5 Pro — A core design strength of Gemini 2.5 Pro.
Strong multimodal reasoning: Gemini 2.5 Pro — A core design strength of Gemini 2.5 Pro.
Science and maths benchmarks: Gemini 2.5 Pro — A core design strength of Gemini 2.5 Pro.
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%): MAI-Thinking-1 — A core design strength of MAI-Thinking-1.
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation: MAI-Thinking-1 — A core design strength of MAI-Thinking-1.
Efficient reasoning at low token cost for its class: MAI-Thinking-1 — A core design strength of MAI-Thinking-1.
Lowest cost at scale: MAI-Thinking-1 — At Not published, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Gemini 2.5 Pro — Its 1M window is about 3.9× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: MAI-Thinking-1 — At Not published it undercuts Gemini 2.5 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Gemini 2.5 Pro — Larger 1M window fits more in one prompt.
Anyone whose priority is 1m context via api: Gemini 2.5 Pro — It is specifically built for that.
Anyone whose priority is very strong math reasoning (aime 2025 97%, aime 2026 94.5%): MAI-Thinking-1 — That is its strongest area.
Gemini 2.5 Pro: where it fits
Google's previous-gen 2M flagship — still a strong long-context multimodal option. Released June 2025 by Google, it is built for 1M context via API, strong multimodal reasoning, science and maths benchmarks, and whole-book and video analysis.
Its trade-offs are real: superseded by 3.x for newest features, and recall degrades on very long inputs. At $1.25 in / $10 out per million tokens, it sits in the mid price band.
MAI-Thinking-1: where it fits
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. Released June 2, 2026 by Microsoft, it is built for very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%), microsoft's first in-house flagship reasoner, trained without OpenAI distillation, efficient reasoning at low token cost for its class, and competitive with Claude Opus 4.6 on SWE-Bench Pro (vendor-reported).
Its trade-offs: closed and in private preview — no open weights, no published pricing, thin availability, and benchmarks are largely self-reported.
The bottom line for this matchup
Gemini 2.5 Pro and MAI-Thinking-1 overlap enough that the right pick depends on your specific job. MAI-Thinking-1 costs less per token; Gemini 2.5 Pro holds the larger context; and each leads in its own area — Gemini 2.5 Pro for 1m context via api, MAI-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%). Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is Gemini 2.5 Pro or MAI-Thinking-1 better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 2.5 Pro leans toward 1m context via api while MAI-Thinking-1 leans toward very strong math reasoning (aime 2025 97%, aime 2026 94.5%), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 2.5 Pro or MAI-Thinking-1?
MAI-Thinking-1 is cheaper — $1.25/$10 per 1M tokens vs Not published.
Which has the bigger context window?
Gemini 2.5 Pro — 1M vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemini 2.5 Pro and MAI-Thinking-1 together?
Yes — a multi-model platform like LumiChats gives you Gemini 2.5 Pro, MAI-Thinking-1 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Gemini 2.5 Pro or MAI-Thinking-1?
MAI-Thinking-1 — released June 2, 2026, about 12 months after Gemini 2.5 Pro.
Gemini 2.5 Pro vs MAI-Thinking-1
Google · US | Microsoft · US · Updated June 2026
Quick verdict
Pick Gemini 2.5 Pro for 1m context via api or strong multimodal reasoning. Pick MAI-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%) or microsoft's first in-house flagship reasoner, trained without openai distillation. On a tight budget at scale, MAI-Thinking-1 is the value pick.
Gemini 2.5 Pro (Google) and MAI-Thinking-1 (Microsoft) are two of the models people most often weigh against each other in 2026. Gemini 2.5 Pro is google's previous-gen 2M flagship — still a strong long-context multimodal option. MAI-Thinking-1 is microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: Gemini 2.5 Pro holds 3.9× more — 1M (~1,500 pages) vs 256K (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: MAI-Thinking-1 is the newer model by about 12 months (released June 2, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Gemini 2.5 Pro
MAI-Thinking-1
Provider
Google (US)
Microsoft (US)
Released
June 2025
June 2, 2026
Context window
1M (~1,500 pages)
256K (~384 pages)
Price (in/out)
$1.25/$10 per 1M tokens
Not published
Open weight?
No — API only
No — API only
Modalities
text, image, audio, video, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
1M context via API
Gemini 2.5 Pro
A core design strength of Gemini 2.5 Pro.
Strong multimodal reasoning
Gemini 2.5 Pro
A core design strength of Gemini 2.5 Pro.
Science and maths benchmarks
Gemini 2.5 Pro
A core design strength of Gemini 2.5 Pro.
Very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%)
MAI-Thinking-1
A core design strength of MAI-Thinking-1.
Microsoft's first in-house flagship reasoner, trained without OpenAI distillation
MAI-Thinking-1
A core design strength of MAI-Thinking-1.
Efficient reasoning at low token cost for its class
MAI-Thinking-1
A core design strength of MAI-Thinking-1.
Lowest cost at scale
MAI-Thinking-1
At Not published, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Gemini 2.5 Pro
Its 1M window is about 3.9× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ MAI-Thinking-1
At Not published it undercuts Gemini 2.5 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Gemini 2.5 Pro
Larger 1M window fits more in one prompt.
Anyone whose priority is 1m context via api
→ Gemini 2.5 Pro
It is specifically built for that.
Anyone whose priority is very strong math reasoning (aime 2025 97%, aime 2026 94.5%)
→ MAI-Thinking-1
That is its strongest area.
Gemini 2.5 Pro: where it fits
Google's previous-gen 2M flagship — still a strong long-context multimodal option. Released June 2025 by Google, it is built for 1M context via API, strong multimodal reasoning, science and maths benchmarks, and whole-book and video analysis.
Its trade-offs are real: superseded by 3.x for newest features, and recall degrades on very long inputs. At $1.25 in / $10 out per million tokens, it sits in the mid price band.
MAI-Thinking-1: where it fits
Microsoft's first fully in-house flagship reasoning model — a Claude-class reasoner built independently to cut its OpenAI dependence. Released June 2, 2026 by Microsoft, it is built for very strong math reasoning (AIME 2025 97%, AIME 2026 94.5%), microsoft's first in-house flagship reasoner, trained without OpenAI distillation, efficient reasoning at low token cost for its class, and competitive with Claude Opus 4.6 on SWE-Bench Pro (vendor-reported).
Its trade-offs: closed and in private preview — no open weights, no published pricing, thin availability, and benchmarks are largely self-reported.
The bottom line for this matchup
Gemini 2.5 Pro and MAI-Thinking-1 overlap enough that the right pick depends on your specific job. MAI-Thinking-1 costs less per token; Gemini 2.5 Pro holds the larger context; and each leads in its own area — Gemini 2.5 Pro for 1m context via api, MAI-Thinking-1 for very strong math reasoning (aime 2025 97%, aime 2026 94.5%). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Gemini 2.5 Pro and MAI-Thinking-1 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Is Gemini 2.5 Pro or MAI-Thinking-1 better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, Gemini 2.5 Pro leans toward 1m context via api while MAI-Thinking-1 leans toward very strong math reasoning (aime 2025 97%, aime 2026 94.5%), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Gemini 2.5 Pro or MAI-Thinking-1?
MAI-Thinking-1 is cheaper — $1.25/$10 per 1M tokens vs Not published.
Which has the bigger context window?
Gemini 2.5 Pro — 1M vs 256K, about 3.9× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Gemini 2.5 Pro and MAI-Thinking-1 together?
Yes — a multi-model platform like LumiChats gives you Gemini 2.5 Pro, MAI-Thinking-1 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, Gemini 2.5 Pro or MAI-Thinking-1?
MAI-Thinking-1 — released June 2, 2026, about 12 months after Gemini 2.5 Pro.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.