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DeepSeek-V4.1-Flash-1017 vs Sarvam-30B

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 19.2.

DeepSeek · Sarvam AI · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 19.2, ranking #15 overall.

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

Choose DeepSeek-V4.1-Flash-1017

  • overall performance matters — it scores 51.0 and ranks #15 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026

Choose Sarvam-30B

  • you are already invested in the Sarvam AI ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Sarvam-30B
Sarvam AI
Core performance indexes
LLM Stats Score
51.0
#15
19.2
#226
Reasoning
48.6
#18
19.0
#224
Coding
39.1
#15
5.7
#215
Agents
38.6
#9
-1.6
#201
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash-1017
Sarvam-30B
Math
31.8#67
23.5#137
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 14 for Sarvam-30B

No common benchmarks found

DeepSeek-V4.1-Flash-1017 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

733.2B diff

DeepSeek-V4.1-Flash-1017 has 733.2B more parameters than Sarvam-30B, making it 2444.0% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Sarvam AI
Sarvam-30B
30.0Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
30.0B
Sarvam-30B

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash-1017 specifies input context (1,048,576 tokens). Only DeepSeek-V4.1-Flash-1017 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Sarvam AI
Sarvam-30B
Input- tokens
Output- tokens
Tue Oct 20 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas Sarvam-30B does not.

DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

Sarvam-30B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 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-V4.1-Flash-1017

MIT

Open weights

Sarvam-30B

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Sarvam-30B was released on 2026-03-06.

DeepSeek-V4.1-Flash-1017 is 8 months newer than Sarvam-30B.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

3 days ago

7mo newer
Sarvam-30B

Mar 6, 2026

7 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?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and Sarvam-30B side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Sarvam-30B
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Sarvam-30B.

Which is better, DeepSeek-V4.1-Flash-1017 or Sarvam-30B?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 19.2. DeepSeek-V4.1-Flash-1017 is made by DeepSeek 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 DeepSeek-V4.1-Flash-1017 compare to Sarvam-30B in benchmarks?

DeepSeek-V4.1-Flash-1017 scores BabyVision: 92.5%, Chartography: 83.0%, DeepSWE 1.1: 77.0%, NL2Repo: 68.2%, AutomationBench: 60.5%. 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-V4.1-Flash-1017 and Sarvam-30B?

DeepSeek-V4.1-Flash-1017 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-V4.1-Flash-1017 and Sarvam-30B?

Key differences include LLM Stats Score (51.0 vs 19.2), multimodal support (yes vs no), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Sarvam-30B?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Sarvam-30B is developed by Sarvam AI.

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