Model Comparison

GLM-5 vs Sarvam-30B

GLM-5 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

GLM-5 outperforms in 2 benchmarks (BrowseComp, SWE-Bench Verified), while Sarvam-30B is better at 0 benchmarks.

GLM-5 significantly outperforms across most benchmarks.

Fri May 01 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Fri May 01 2026 • llm-stats.com
Zhipu AI
GLM-5
Input tokens$1.00
Output tokens$3.20
Best providerUnknown Organization
Sarvam AI
Sarvam-30B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

714.0B diff

GLM-5 has 714.0B more parameters than Sarvam-30B, making it 2380.0% larger.

Zhipu AI
GLM-5
744.0Bparameters
Sarvam AI
Sarvam-30B
30.0Bparameters
744.0B
GLM-5
30.0B
Sarvam-30B

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
Sarvam AI
Sarvam-30B
Input- tokens
Output- tokens
Fri May 01 2026 • llm-stats.com

License

Usage and distribution terms

GLM-5 is licensed under MIT, while Sarvam-30B 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

Sarvam-30B

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Sarvam-30B was released on 2026-03-06.

Sarvam-30B is 1 month newer than GLM-5.

GLM-5

Feb 11, 2026

2 months ago

Sarvam-30B

Mar 6, 2026

1 months ago

3w newer

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)
Higher BrowseComp score (75.9% vs 35.5%)
Higher SWE-Bench Verified score (77.8% vs 34.0%)
Sarvam AI

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-5
Sarvam AI
Sarvam-30B

FAQ

Common questions about GLM-5 vs Sarvam-30B

GLM-5 significantly outperforms across most benchmarks. GLM-5 is made by Zhipu AI and Sarvam-30B is made by Sarvam AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
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%. Sarvam-30B scores MATH-500: 97.0%, AIME 2025: 96.7%, MBPP: 92.7%, HumanEval: 92.1%, MMLU: 85.1%.
GLM-5 supports 200K tokens and Sarvam-30B 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 Sarvam-30B is developed by Sarvam AI.