Model Comparison

GLM-5 vs Sarvam-30BWhich is better in 2026?

GLM-5 significantly outperforms across most benchmarks.

Verdict: GLM-5 vs Sarvam-30B — which is better?

GLM-5 (by Zhipu AI) and Sarvam-30B (by Sarvam AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

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.

Choose GLM-5 if…

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks

Choose Sarvam-30B if…

  • you want the most recent training data — it shipped Mar 2026
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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.

Wed Jun 17 2026 • llm-stats.com

Arena Performance

Human preference votes

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
Wed Jun 17 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

4 months ago

Sarvam-30B

Mar 6, 2026

3 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

Notice missing or incorrect data?Start an Issue discussion→

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

No standout differentiators in the data we have for this pair.

Detailed Comparison

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

FAQ

Common questions about GLM-5 vs Sarvam-30B.

Which is better, GLM-5 or 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.

How does GLM-5 compare to Sarvam-30B in benchmarks?

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%.

What are the context window sizes for GLM-5 and Sarvam-30B?

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.

What are the main differences between GLM-5 and Sarvam-30B?

Key differences include licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5 and Sarvam-30B?

GLM-5 is developed by Zhipu AI and Sarvam-30B is developed by Sarvam AI.

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