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GLM-5.3-Flash vs Sarvam-30B

GLM-5.3-Flash leads the LLM Stats Score 51.1 to 19.9.

Zhipu AI · Sarvam AI · Updated for 2026

Which is better?

GLM-5.3-Flash leads the overall LLM Stats Score 51.1 to 19.9, ranking #12 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose GLM-5.3-Flash

  • overall performance matters — it scores 51.1 and ranks #12 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Aug 2026

Choose Sarvam-30B

  • you are already invested in the Sarvam AI ecosystem

At a glance

The differences that matter most.

GLM-5.3-Flash
Zhipu AI
Sarvam-30B
Sarvam AI
Core performance indexes
LLM Stats Score
51.1
#12
19.9
#186
Reasoning
49.9
#14
19.6
#184
Coding
36.0
#24
6.2
#180
Agents
37.7
#10
-0.7
#158
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.15 / M
— / M
Output price
$0.50 / M
— / M
Context window
1,048,576
—

Individual benchmarks

15 reported for GLM-5.3-Flash · 14 for Sarvam-30B

No common benchmarks found

GLM-5.3-Flash and Sarvam-30Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

290.0B diff

GLM-5.3-Flash has 290.0B more parameters than Sarvam-30B, making it 966.7% larger.

Zhipu AI
GLM-5.3-Flash
320.0Bparameters
Sarvam AI
Sarvam-30B
30.0Bparameters
320.0B
GLM-5.3-Flash
30.0B
Sarvam-30B

Context Window

Maximum input and output token capacity

Only GLM-5.3-Flash specifies input context (1,048,576 tokens). Only GLM-5.3-Flash specifies output context (131,072 tokens).

Zhipu AI
GLM-5.3-Flash
Input1,048,576 tokens
Output131,072 tokens
Sarvam AI
Sarvam-30B
Input- tokens
Output- tokens
Mon Aug 31 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

GLM-5.3-Flash supports multimodal inputs, whereas Sarvam-30B does not.

GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-5.3-Flash

Text
Images
Audio
Video

Sarvam-30B

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5.3-Flash 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-5.3-Flash

MIT

Open weights

Sarvam-30B

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5.3-Flash was released on 2026-08-26, while Sarvam-30B was released on 2026-03-06.

GLM-5.3-Flash is 6 months newer than Sarvam-30B.

GLM-5.3-Flash

Aug 26, 2026

4 days ago

5mo newer
Sarvam-30B

Mar 6, 2026

5 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

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against GLM-5.3-Flash and Sarvam-30B side-by-side, then vote on the output you prefer.

GLM-5.3-Flash
✓ Preferred
Sarvam-30B
Open in Playground

FAQ

Common questions about GLM-5.3-Flash vs Sarvam-30B.

Which is better, GLM-5.3-Flash or Sarvam-30B?

GLM-5.3-Flash leads the LLM Stats Score 51.1 to 19.9. GLM-5.3-Flash is made by Zhipu AI and Sarvam-30B is made by Sarvam AI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does GLM-5.3-Flash compare to Sarvam-30B in benchmarks?

GLM-5.3-Flash scores CharXiv-R: 89.4%, Terminal-Bench 2.1: 84.3%, MMVU: 80.5%, Toolathlon: 78.4%, Chartography: 78.0%. 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-5.3-Flash and Sarvam-30B?

GLM-5.3-Flash supports 1.0M 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-5.3-Flash and Sarvam-30B?

Key differences include LLM Stats Score (51.1 vs 19.9), multimodal support (yes vs no), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3-Flash and Sarvam-30B?

GLM-5.3-Flash is developed by Zhipu AI and Sarvam-30B is developed by Sarvam AI.

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