Model comparison

Gemini 2.5 Pro vs MiniMax M2.7

Google against MiniMax, compared on context, price, and verified benchmark results.

Catalog record checked September 26, 2026Individual provider fields may changeEvidence confidence: High — see receipts below

Google

Gemini 2.5 Pro

Balanced

MiniMax

MiniMax M2.7

Balanced · Open weights

AI model capability comparison
Specification	Gemini 2.5 Pro	MiniMax M2.7
Provider
Provider	Google	MiniMax
Tier
Tier	Balanced	Balanced
Context window
Context window	Winner: 1.05M	205K
Max output
Max output	66K	Not verifiedUnverified
Input / 1M tokens
Input / 1M tokens	$1.25	Winner: $0.30
Output / 1M tokens
Output / 1M tokens	$10	Winner: $1.20
Weights
Weights	Closed	Open
Parameters
Parameters	Not disclosedUnverified	Not disclosedUnverified
Reasoning levels
Reasoning levels	low, medium, high	Not verifiedUnverified
Modalities
Modalities	text, image, video, audio, pdf	text
API model id
API model id	gemini-2.5-pro	MiniMax-M2.7
Released
Released	June 17, 2025	March 18, 2026
Artificial Analysis Intelligence Index (2026-09-26)
Artificial Analysis Intelligence Index (2026-09-26)	16.1	Winner: 22.8

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.

Pricing tiers

Gemini 2.5 Pro: $1.25/$10 per MTok (≤200K); $2.50/$15 (>200K). Legacy but still widely listed.

MiniMax M2.7: $0.30/$1.20 per MTok on MiniMax's pay-as-you-go API; cache read $0.06, cache write $0.375. Open weights on Hugging Face (licence terms not re-verified).

BalancedRecord checked September 26, 2026

Gemini 2.5 Pro

Gemini 2.5 Pro is Google's previous-generation Pro with a 1.05M-token context and strong multimodal support.

Best for

Long documents
Multimodal input
Google Workspace integration

Watch out

Since 2026-09-18 Google limits Gemini API access to existing 2.5 users (new projects are pointed to 3.8 Flash or 3.5 Flash-Lite); the Gemini API deprecations page shows no shutdown date — check Vertex AI's lifecycle page separately.

BalancedOpen weightsRecord checked September 26, 2026

MiniMax M2.7

MiniMax M2.7 is MiniMax's current text-only open-weight model with a 204,800-token context, priced like M3.

Best for

Text-only agents
Open-weight deployments
Cost-sensitive coding

Watch out

Text-only and a 204,800-token context — M3 has 1M context and multimodal input at the same $0.30/$1.20 list price.

When the cheaper one wins

MiniMax M2.7 is cheaper on output at $1.20 per million tokens against $10 for Gemini 2.5 Pro — about 8.3×. 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: 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.
2/5 core specs verified on both sides — Not published for at least one side: max output, parameter count, reasoning levels.
1 shared named benchmark with differing scores — Measured on: Artificial Analysis Intelligence Index.
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.

Gemini 2.5 Pro: Google — Gemini 2.5 Pro GA (accessed 2026-08-29)
Gemini 2.5 Pro: Google AI pricing (accessed 2026-08-29)
Gemini 2.5 Pro: Gemini API changelog (2.5-series access restriction, 2026-09-18) (accessed 2026-09-26)
MiniMax M2.7: MiniMax — model release notes (M2.7, 2026-03-18) (accessed 2026-09-26)
MiniMax M2.7: MiniMax pay-as-you-go pricing (M2.7 $0.30/$1.20) (accessed 2026-09-26)
MiniMax M2.7: Hugging Face — MiniMaxAI/MiniMax-M2.7 (accessed 2026-09-26)

Related comparisons

Diving deeper on one model? Gemini 2.5 Pro · MiniMax M2.7

Common questions

Gemini 2.5 Pro vs MiniMax M2.7

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

Is Gemini 2.5 Pro or MiniMax M2.7 cheaper for input?

MiniMax M2.7 is cheaper at $0.30 per million input tokens, against $1.25 for Gemini 2.5 Pro — roughly 4.2× 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 2.5 Pro has tiered pricing: $1.25/$10 per MTok (≤200K); $2.50/$15 (>200K). Legacy but still widely listed. MiniMax M2.7 has tiered pricing: $0.30/$1.20 per MTok on MiniMax's pay-as-you-go API; cache read $0.06, cache write $0.375. Open weights on Hugging Face (licence terms not re-verified).

Is Gemini 2.5 Pro or MiniMax M2.7 cheaper for output?

MiniMax M2.7 is cheaper at $1.20 per million output tokens, against $10 for Gemini 2.5 Pro — roughly 8.3× 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 2.5 Pro has tiered pricing: $1.25/$10 per MTok (≤200K); $2.50/$15 (>200K). Legacy but still widely listed. MiniMax M2.7 has tiered pricing: $0.30/$1.20 per MTok on MiniMax's pay-as-you-go API; cache read $0.06, cache write $0.375. Open weights on Hugging Face (licence terms not re-verified).

Which has the larger context window, Gemini 2.5 Pro or MiniMax M2.7?

Gemini 2.5 Pro accepts 1.05M tokens against 205K for MiniMax M2.7. This only matters if you routinely send very long documents or large codebases.

Should I use Gemini 2.5 Pro or MiniMax M2.7?

Both sit in the balanced tier, so the choice usually comes down to price and context rather than capability. Gemini 2.5 Pro suits long documents; MiniMax M2.7 suits text-only agents.

Can I self-host Gemini 2.5 Pro or MiniMax M2.7?

MiniMax M2.7 publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Gemini 2.5 Pro is a closed model whose supported access paths are controlled by its provider.

Next step

Choosing between them

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

Input price: MiniMax M2.7 costs $0.30 per 1M tokens versus $1.25 for Gemini 2.5 Pro — a 4.2x difference at the headline tier.
Context: Gemini 2.5 Pro takes 1.05M against 205K for MiniMax M2.7 — only decisive if your prompts approach the smaller window.
Measured capability: MiniMax M2.7 leads Artificial Analysis Intelligence Index 22.8 to 16.1 (measured 2026-09-26).
Deployment: MiniMax M2.7 publishes weights you can self-host; the other is API-only.

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