Model Comparison

GLM-5 vs MiniCPM-SALA

Comparing GLM-5 and MiniCPM-SALA across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-5 and MiniCPM-SALA don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Tue Apr 14 2026 • llm-stats.com
Zhipu AI
GLM-5
Input tokens$1.00
Output tokens$3.20
Best providerUnknown Organization
OpenBMB
MiniCPM-SALA
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

734.5B diff

GLM-5 has 734.5B more parameters than MiniCPM-SALA, making it 7750.4% larger.

Zhipu AI
GLM-5
744.0Bparameters
OpenBMB
MiniCPM-SALA
9.5Bparameters
744.0B
GLM-5
9.5B
MiniCPM-SALA

Context Window

Maximum input and output token capacity

Only GLM-5 specifies input context (200,000 tokens). Only GLM-5 specifies output context (128,000 tokens).

Zhipu AI
GLM-5
Input200,000 tokens
Output128,000 tokens
OpenBMB
MiniCPM-SALA
Input- tokens
Output- tokens
Tue Apr 14 2026 • llm-stats.com

License

Usage and distribution terms

GLM-5 is licensed under MIT, while MiniCPM-SALA uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

GLM-5

MIT

Open weights

MiniCPM-SALA

Apache 2.0

Open weights

Release Timeline

When each model was launched

Both models were released on 2026-02-11.

They likely represent similar generations of model development.

GLM-5

Feb 11, 2026

2 months ago

MiniCPM-SALA

Feb 11, 2026

2 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

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Key Takeaways

Zhipu AI
Larger context window (200,000 tokens)
OpenBMB

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-5
OpenBMB
MiniCPM-SALA

FAQ

Common questions about GLM-5 vs MiniCPM-SALA

GLM-5 (Zhipu AI) and MiniCPM-SALA (OpenBMB) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
GLM-5 scores t2-bench: 89.7%, SWE-Bench Verified: 77.8%, BrowseComp: 75.9%, MCP Atlas: 67.8%, Terminal-Bench 2.0: 56.2%. MiniCPM-SALA scores HumanEval: 95.1%, RULER 64k: 92.7%, RULER 128k: 89.4%, MBPP: 89.1%, RULER 512K: 87.1%.
GLM-5 supports 200K tokens and MiniCPM-SALA supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
GLM-5 is developed by Zhipu AI and MiniCPM-SALA is developed by OpenBMB.