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MiniMax M1 vs GLM 4.5 Air

MiniMax M1

MiniMax

71#159
vs
GLM 4.5 Air

Zhipu AI

71#160
Signal-by-Signal Comparison
SignalMiniMax M1DeltaGLM 4.5 Air
Capabilities
50
--
50
Benchmarks
71
-1
72
Pricing
98
-1
99
Context window size
95
+14
81
Recency
57
-7
64
Output Capacity
76
-6
83
Overall Result
1 wins
of 6
4 wins
GLM 4.5 Air wins 4 of 6 signals

Score History

Score History (25 data points)
MiniMax M1GLM 4.5 Air
MiniMax M1

70.8

current score

Leader

MiniMax M1

right now

GLM 4.5 Air

70.7

current score

LMMarketCap.com
Interactive Price Comparison
100Kcalls/month
1,000tokens (~1,333 chars)
500tokens (~667 chars)

MiniMax M1

MiniMax

Per request$0.001650
Daily$5.50
Monthly$165.00
Annual$1980.00

GLM 4.5 Air

Zhipu AI

Best Value
Per request$0.000555
Daily$1.85
Monthly$55.50
Annual$666.00

GLM 4.5 Air saves you $109.50/month

That's $1314.00/year compared to MiniMax M1 at your current usage level of 100K calls/month.

66% cheaper
Choose GLM 4.5 Air for cost optimization

MiniMax M1 pricing:
Input:$0.55/M tokens
Output:$2.20/M tokens
GLM 4.5 Air pricing:
Input:$0.13/M tokens
Output:$0.85/M tokens
Winner
MiniMax M1

MiniMax

71

Composite Score

GLM 4.5 Air

Zhipu AI

71

Composite Score

Signal-by-Signal Comparison
MetricMiniMax M1GLM 4.5 AirWinner
Overall Score
71
71
MiniMax M1
Rank#159#160
MiniMax M1
Quality Rank#159#160
MiniMax M1
Adoption Rank#159#160
MiniMax M1
Parameters------
Context Window1000K131K
MiniMax M1
Pricing$0.55/$2.20/M$0.13/$0.85/M--
Signal Scores
Capabilities
50
50
MiniMax M1
Benchmarks
71
72
GLM 4.5 Air
Pricing
98
99
GLM 4.5 Air
Context window size
95
81
MiniMax M1
Recency
57
64
GLM 4.5 Air
Output Capacity
76
83
GLM 4.5 Air
Benchmark Head-to-Head(2 benchmarks)
MiniMax M1: 0GLM 4.5: 2
MiniMax M1
GLM 4.5
Normalized 0-100%
MMLU-Pro
81.1%81.4%
Arena Elo
13641373
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 M1Strong Performer

Scores 71/100 (rank #159), placing it in the top 46% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100
GLM 4.5 AirStrong Performer

Scores 71/100 (rank #160), placing it in the top 45% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100

With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.

When to Use Each Model

Choose MiniMax M1 when you need:

  • Processing long documents or large codebases (1000K token context)
  • Step-by-step reasoning and chain-of-thought problem solving

Choose GLM 4.5 Air when you need:

  • High-volume production workloads where API costs must be minimized
  • Step-by-step reasoning and chain-of-thought problem solving
  • Self-hosted deployments where you need full control over the model
Cost-Performance Analysis
MiniMax M1
Input cost$0.55/M tokens
Output cost$2.20/M tokens
Cost per quality point$0.039
Est. monthly (1M tokens/day)$41.25
GLM 4.5 AirBest Value
Input cost$0.13/M tokens
Output cost$0.85/M tokens
Cost per quality point$0.014
Est. monthly (1M tokens/day)$14.70

GLM 4.5 Air offers 64% better value per quality point. At 1M tokens/day, you'd spend $14.70/month with GLM 4.5 Air vs $41.25/month with MiniMax M1 - a $26.55 monthly difference.

Latency & Speed
MiniMax M1Faster
Speed score0/100
GLM 4.5 Air
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 M1

Customer support chatbot

Suitable for user-facing chat with competitive response times. GLM 4.5 Air also offers lower per-token costs for high-volume support

MiniMax M1

Long document analysis

Larger context window (1000K tokens) can process longer documents, contracts, and research papers in a single pass

MiniMax M1

Batch data extraction

Lower output pricing ($0.85/M) reduces costs when processing thousands of records daily

GLM 4.5 Air

Creative writing & content

Higher overall composite score (71/100) correlates with better nuance, coherence, and style in long-form content

MiniMax M1
Which Should You Choose?
Our recommendation:
MiniMax M1

MiniMax M1 and GLM 4.5 Air are extremely close in overall performance (only 0.09999999999999432 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.

MiniMax M1
Recommended

by MiniMax

  • Choose for Quality - Marginally better benchmark scores; both are excellent
  • 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

by Zhipu AI

  • Choose for Cost - 64% lower pricing; better value at scale
Capability Comparison
CapabilityMiniMax M1GLM 4.5 Air
Vision (Image Input)
Function Calling
Streaming
JSON Mode
Reasoning
Web Search
Image Output
Monthly Cost Calculator
1,000tokens (600 in / 400 out)
100requests/day (3,000/month)

MiniMax M1

MiniMax

$3.63
estimated monthly cost

GLM 4.5 Air

Zhipu AI

Best Value
$1.25
estimated monthly cost

GLM 4.5 Air saves you $2.38/month

That's 65% cheaper than MiniMax M1 at 1,000 tokens/request and 100 requests/day.

Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.

Parameters & Context
ParameterMiniMax M1GLM 4.5 Air
Context Window1M131K
Max Output Tokens40,00098,304
Open SourceNoYes
CreatedJun 17, 2025Jul 25, 2025
Last updated: 41m ago

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