MiniMax M2-her vs Grok 4.7

MiniMax

68#179

xAI

89#42

Signal-by-Signal Comparison

Signal	MiniMax M2-her	Delta	Grok 4.7
Capabilities	17	-83	100
Benchmarks	70	-16	86
Pricing	99	+4	95
Context window size	76	-14	90
Recency	89	-11	100
Output Capacity	53	-38	90
Overall Result	1 wins	of 6	5 wins

Grok 4.7 wins 5 of 6 signals

Score History

Score History (32 data points)

MiniMax M2-herGrok 4.7

MiniMax M2-her

68.1

current score

Leader

Grok 4.7

right now

Grok 4.7

88.8

current score

LMMarketCap.com

Interactive Price Comparison

Quick presets

Monthly API calls

100Kcalls/month

Avg. input tokens/call

1,000tokens (~1,333 chars)

Avg. output tokens/call

500tokens (~667 chars)

MiniMax M2-her

MiniMax

Best Value

Per request$0.000900

Daily$3.00

Monthly$90.00

Annual$1080.00

Grok 4.7

xAI

Per request$0.004000

Daily$13.33

Monthly$400.00

Annual$4800.00

MiniMax M2-her saves you $310.00/month

That's $3720.00/year compared to Grok 4.7 at your current usage level of 100K calls/month.

78% cheaper

Choose MiniMax M2-her for cost optimization

MiniMax M2-her pricing:

Input:$0.30/M tokens

Output:$1.20/M tokens

Grok 4.7 pricing:

Input:$1.60/M tokens

Output:$4.80/M tokens

MiniMax M2-her

MiniMax

Composite Score

Winner

Grok 4.7

xAI

Composite Score

Signal-by-Signal Comparison

Metric	MiniMax M2-her	Grok 4.7	Winner
Overall Score	68	89	Grok 4.7
Rank	#179	#42	Grok 4.7
Quality Rank	#179	#42	Grok 4.7
Adoption Rank	#179	#42	Grok 4.7
Parameters	--	--	--
Context Window	66K	500K	Grok 4.7
Pricing	$0.30/$1.20/M	$1.60/$4.80/M	--
Signal Scores
Capabilities	17	100	Grok 4.7
Benchmarks	70	86	Grok 4.7
Pricing	99	95	MiniMax M2-her
Context window size	76	90	Grok 4.7
Recency	89	100	Grok 4.7
Output Capacity	53	90	Grok 4.7

Benchmark Head-to-Head(11 benchmarks)

MiniMax M2-her: 0Grok 4.7: 2

MiniMax M2-her

Grok 4.7

Normalized 0-100%

MMLU

-91.5%

MMLU-Pro

82%83.5%

GPQA Diamond

-82%

MATH-500

-95%

HumanEval

-95.5%

SWE-bench Verified

-70%

AIME 2024

-88%

IFEval

-91%

BBH

-90%

Arena Elo

13461462

LiveBench

-73%

Benchmark Interpretation

Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.

MiniMax M2-herCompetitive

Scores 68/100 (rank #179), placing it in the top 39% of all 290 models tracked.

Raw Quality0/100

Cost Efficiency0/100

Speed0/100

Grok 4.7Elite Tier

Scores 89/100 (rank #42), placing it in the top 86% of all 290 models tracked.

Raw Quality0/100

Cost Efficiency0/100

Speed0/100

Grok 4.7 has a 21-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.

When to Use Each Model

Choose MiniMax M2-her when you need:

High-volume production workloads where API costs must be minimized

Choose Grok 4.7 when you need:

Processing long documents or large codebases (500K token context)
Multimodal workflows that require image understanding
Agentic applications using tool/function calling
Step-by-step reasoning and chain-of-thought problem solving

Cost-Performance Analysis

MiniMax M2-herBest Value

Input cost$0.30/M tokens

Output cost$1.20/M tokens

Cost per quality point$0.022

Est. monthly (1M tokens/day)$22.50

Grok 4.7

Input cost$1.60/M tokens

Output cost$4.80/M tokens

Cost per quality point$0.072

Est. monthly (1M tokens/day)$96.00

MiniMax M2-her offers 77% better value per quality point. At 1M tokens/day, you'd spend $22.50/month with MiniMax M2-her vs $96.00/month with Grok 4.7 - a $73.50 monthly difference.

Latency & Speed

MiniMax M2-herFaster

Speed score0/100

Grok 4.7

Speed score0/100

Both models have comparable response speeds. For most applications, the latency difference is negligible.

When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.

Example Use Cases

Code generation & review

Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring

MiniMax M2-her

Customer support chatbot

Suitable for user-facing chat with competitive response times. MiniMax M2-her also offers lower per-token costs for high-volume support

MiniMax M2-her

Long document analysis

Larger context window (500K tokens) can process longer documents, contracts, and research papers in a single pass

Grok 4.7

Batch data extraction

Lower output pricing ($1.20/M) reduces costs when processing thousands of records daily

MiniMax M2-her

Creative writing & content

Higher overall composite score (89/100) correlates with better nuance, coherence, and style in long-form content

Grok 4.7

Image understanding & OCR

Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly

Grok 4.7

Which Should You Choose?

Our recommendation:

Grok 4.7

Grok 4.7 clearly outperforms MiniMax M2-her with a significant 20.700000000000003-point lead. For most general use cases, Grok 4.7 is the stronger choice. However, MiniMax M2-her may still excel in niche scenarios.

By Use Case

Best for Quality

MiniMax M2-her

Marginally better benchmark scores; both are excellent

Best for Cost

MiniMax M2-her

77% lower pricing; better value at scale

Best for Reliability

MiniMax M2-her

Higher uptime and faster response speeds

Best for Prototyping

MiniMax M2-her

Stronger community support and better developer experience

Best for Production

MiniMax M2-her

Wider enterprise adoption and proven at scale

MiniMax M2-her

by MiniMax

Choose for Quality - Marginally better benchmark scores; both are excellent
Choose for Cost - 77% lower pricing; better value at scale
Choose for Reliability - Higher uptime and faster response speeds
Choose for Prototyping - Stronger community support and better developer experience
Choose for Production - Wider enterprise adoption and proven at scale