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DeepSeek-V4-Pro-0813 vs Sarvam-30B

Comparing DeepSeek-V4-Pro-0813 and Sarvam-30B across benchmarks, pricing, and capabilities.

DeepSeek · Sarvam AI · Updated for 2026

Which is better?

DeepSeek-V4-Pro-0813 and Sarvam-30B trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose DeepSeek-V4-Pro-0813

  • 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.

DeepSeek-V4-Pro-0813
DeepSeek
Sarvam-30B
Sarvam AI
Benchmark wins
—
—
Input price
$0.43 / M
— / M
Output price
$0.87 / M
— / M
Context window
1,048,576
—
Released
Aug 2026
Mar 2026
License
MIT
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V4-Pro-0813 and Sarvam-30Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

1570.0B diff

DeepSeek-V4-Pro-0813 has 1570.0B more parameters than Sarvam-30B, making it 5233.3% larger.

DeepSeek
DeepSeek-V4-Pro-0813
1.6Tparameters
Sarvam AI
Sarvam-30B
30.0Bparameters
1600.0B
DeepSeek-V4-Pro-0813
30.0B
Sarvam-30B

Context Window

Maximum input and output token capacity

Only DeepSeek-V4-Pro-0813 specifies input context (1,048,576 tokens). Only DeepSeek-V4-Pro-0813 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4-Pro-0813
Input1,048,576 tokens
Output393,216 tokens
Sarvam AI
Sarvam-30B
Input- tokens
Output- tokens
Mon Aug 24 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V4-Pro-0813 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.

DeepSeek-V4-Pro-0813

MIT

Open weights

Sarvam-30B

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4-Pro-0813 was released on 2026-08-13, while Sarvam-30B was released on 2026-03-06.

DeepSeek-V4-Pro-0813 is 5 months newer than Sarvam-30B.

DeepSeek-V4-Pro-0813

Aug 13, 2026

1 weeks 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 DeepSeek-V4-Pro-0813 and Sarvam-30B side-by-side, then vote on the output you prefer.

DeepSeek-V4-Pro-0813
✓ Preferred
Sarvam-30B
Open in Playground

FAQ

Common questions about DeepSeek-V4-Pro-0813 vs Sarvam-30B.

Which is better, DeepSeek-V4-Pro-0813 or Sarvam-30B?

DeepSeek-V4-Pro-0813 (DeepSeek) and Sarvam-30B (Sarvam AI) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-V4-Pro-0813 compare to Sarvam-30B in benchmarks?

DeepSeek-V4-Pro-0813 scores Terminal-Bench 2.1: 87.9%, CyberGym: 83.3%, Toolathlon: 74.1%, DSBench-FullStack: 71.1%, DSBench-Hard: 67.2%. 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 DeepSeek-V4-Pro-0813 and Sarvam-30B?

DeepSeek-V4-Pro-0813 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 DeepSeek-V4-Pro-0813 and Sarvam-30B?

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

Who makes DeepSeek-V4-Pro-0813 and Sarvam-30B?

DeepSeek-V4-Pro-0813 is developed by DeepSeek and Sarvam-30B is developed by Sarvam AI.

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