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Muse Wonder vs Sarvam-105B

Muse Wonder leads the LLM Stats Score 47.9 to 25.7.

Meta · Sarvam AI · Updated for 2026

Which is better?

Muse Wonder leads the overall LLM Stats Score 47.9 to 25.7, ranking #29 overall.

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

Choose Muse Wonder

  • overall performance matters — it scores 47.9 and ranks #29 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-105B

  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

Core performance indexes
47.9
#29
25.7
#170
33.1
#108
26.1
#164
38.9
#18
-1.7
#263
34.1
#24
5.6
#160
Cost, coverage & limits
Benchmark wins
—
—
Input price
$4.00 / M
— / M
Output price
$20.00 / M
— / M
Context window
1,048,576
—

Individual benchmarks

16 reported for Muse Wonder · 14 for Sarvam-105B

No common benchmarks found

Muse Wonder and Sarvam-105Bdon'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

Context Window

Maximum input and output token capacity

Only Muse Wonder specifies input context (1,048,576 tokens).

Meta
Muse Wonder
Input1,048,576 tokens
Output- tokens
Sarvam AI
Sarvam-105B
Input- tokens
Output- tokens
Thu Oct 15 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Muse Wonder supports multimodal inputs, whereas Sarvam-105B does not.

Muse Wonder can handle both text and other forms of data like images, making it suitable for multimodal applications.

Muse Wonder

Text
Images
Audio
Video

Sarvam-105B

Text
Images
Audio
Video

License

Usage and distribution terms

Muse Wonder is licensed under a proprietary license, while Sarvam-105B uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

Muse Wonder

Proprietary

Closed source

Sarvam-105B

Apache 2.0

Open weights

Release Timeline

When each model was launched

Muse Wonder was released on 2026-10-12, while Sarvam-105B was released on 2026-03-06.

Muse Wonder is 7 months newer than Sarvam-105B.

Muse Wonder

Oct 12, 2026

2 days ago

7mo newer
Sarvam-105B

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 Muse Wonder and Sarvam-105B side-by-side, then vote on the output you prefer.

Muse Wonder
✓ Preferred
Sarvam-105B
Open in Playground

FAQ

Common questions about Muse Wonder vs Sarvam-105B.

Which is better, Muse Wonder or Sarvam-105B?

Muse Wonder leads the LLM Stats Score 47.9 to 25.7. Muse Wonder is made by Meta and Sarvam-105B 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 Muse Wonder compare to Sarvam-105B in benchmarks?

Muse Wonder scores MemoryBench: 85.6%, DeepSWE 1.1: 78.8%, OSWorld 2.1 (partial): 72.6%, JobBench: 70.5%, HealthBench Professional: 60.5%. Sarvam-105B scores MATH-500: 98.6%, AIME 2025: 96.7%, MMLU: 90.6%, HMMT 2025: 85.8%, HMMT25: 85.8%.

What are the context window sizes for Muse Wonder and Sarvam-105B?

Muse Wonder supports 1.0M tokens and Sarvam-105B 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 Muse Wonder and Sarvam-105B?

Key differences include LLM Stats Score (47.9 vs 25.7), multimodal support (yes vs no), licensing (Proprietary vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Muse Wonder and Sarvam-105B?

Muse Wonder is developed by Meta and Sarvam-105B is developed by Sarvam AI.