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Qwen3.7 Max vs GLM 4.6

Qwen3.7 Max

Alibaba

75#87
vs
GLM 4.6

Zhipu AI

75#85
Signal-by-Signal Comparison
SignalQwen3.7 MaxDeltaGLM 4.6
Capabilities
67
--
67
Pricing
96
-2
98
Context window size
95
+11
84
Recency
100
+21
79
Output Capacity
80
-5
85
Benchmarks
0
-70
70
Overall Result
2 wins
of 6
3 wins
GLM 4.6 wins 3 of 6 signals

Score History

Score History (18 data points)
Qwen3.7 MaxGLM 4.6
Qwen3.7 Max

74.8

current score

Leader

GLM 4.6

right now

GLM 4.6

75.1

current score

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

Qwen3.7 Max

Alibaba

Per request$0.003688
Daily$12.29
Monthly$368.75
Annual$4425.00

GLM 4.6

Zhipu AI

Best Value
Per request$0.001500
Daily$5.00
Monthly$150.00
Annual$1800.00

GLM 4.6 saves you $218.75/month

That's $2625.00/year compared to Qwen3.7 Max at your current usage level of 100K calls/month.

59% cheaper
Choose GLM 4.6 for cost optimization

Qwen3.7 Max pricing:
Input:$1.48/M tokens
Output:$4.42/M tokens
GLM 4.6 pricing:
Input:$0.50/M tokens
Output:$2.00/M tokens
Qwen3.7 Max

Alibaba

75

Composite Score

Winner
GLM 4.6

Zhipu AI

75

Composite Score

Signal-by-Signal Comparison
MetricQwen3.7 MaxGLM 4.6Winner
Overall Score
75
75
GLM 4.6
Rank#87#85
GLM 4.6
Quality Rank#87#85
GLM 4.6
Adoption Rank#87#85
GLM 4.6
Parameters------
Context Window1000K205K
Qwen3.7 Max
Pricing$1.48/$4.42/M$0.50/$2.00/M--
Signal Scores
Capabilities
67
67
Qwen3.7 Max
Pricing
96
98
GLM 4.6
Context window size
95
84
Qwen3.7 Max
Recency
100
79
Qwen3.7 Max
Output Capacity
80
85
GLM 4.6
Benchmarks--
70
GLM 4.6
Benchmark Head-to-Head(1 benchmarks)
Qwen3.7 Max: 0GLM 4.6: 0
Qwen3.7 Max
GLM 4.6
Normalized 0-100%
Arena Elo
-1425
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.

Qwen3.7 MaxStrong Performer

Scores 75/100 (rank #87), placing it in the top 70% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100
GLM 4.6Strong Performer

Scores 75/100 (rank #85), placing it in the top 71% 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 Qwen3.7 Max when you need:

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

Choose GLM 4.6 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
Qwen3.7 Max
Input cost$1.48/M tokens
Output cost$4.42/M tokens
Cost per quality point$0.079
Est. monthly (1M tokens/day)$88.50
GLM 4.6Best Value
Input cost$0.50/M tokens
Output cost$2.00/M tokens
Cost per quality point$0.033
Est. monthly (1M tokens/day)$37.50

GLM 4.6 offers 58% better value per quality point. At 1M tokens/day, you'd spend $37.50/month with GLM 4.6 vs $88.50/month with Qwen3.7 Max - a $51.00 monthly difference.

Latency & Speed
Qwen3.7 MaxFaster
Speed score0/100
GLM 4.6
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

Qwen3.7 Max

Customer support chatbot

Suitable for user-facing chat with competitive response times. GLM 4.6 also offers lower per-token costs for high-volume support

Qwen3.7 Max

Long document analysis

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

Qwen3.7 Max

Batch data extraction

Lower output pricing ($2.00/M) reduces costs when processing thousands of records daily

GLM 4.6

Creative writing & content

Higher overall composite score (75/100) correlates with better nuance, coherence, and style in long-form content

GLM 4.6
Which Should You Choose?
Our recommendation:
GLM 4.6

Qwen3.7 Max and GLM 4.6 are extremely close in overall performance (only 0.29999999999999716 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.

by Alibaba

  • 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
GLM 4.6
Recommended

by Zhipu AI

  • Choose for Cost - 58% lower pricing; better value at scale
Capability Comparison
CapabilityQwen3.7 MaxGLM 4.6
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)

Qwen3.7 Max

Alibaba

$7.96
estimated monthly cost

GLM 4.6

Zhipu AI

Best Value
$3.30
estimated monthly cost

GLM 4.6 saves you $4.67/month

That's 59% cheaper than Qwen3.7 Max 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
ParameterQwen3.7 MaxGLM 4.6
Context Window1M205K
Max Output Tokens65,536131,072
Open SourceNoYes
CreatedMay 21, 2026Sep 30, 2025
Last updated: 34m ago

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Qwen3.7 Max vs GLM 4.6 (2026) | LM Market Cap