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Gemini 2.5 Flash vs Sarvam-105B

Gemini 2.5 Flash and Sarvam-105B are closely matched at 21.9 and 25.9 on the LLM Stats Score.

Google · Sarvam AI · Updated for 2026

Which is better?

Gemini 2.5 Flash and Sarvam-105B are closely matched on the overall LLM Stats Score at 21.9 and 25.9.

The models split the 4 individual benchmarks reported for both models evenly.

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

Choose Gemini 2.5 Flash

  • you want predictable pricing at $0.30/M input and $2.50/M output

Choose Sarvam-105B

  • you want the most recent training data — it shipped Mar 2026
  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

Core performance indexes
21.9
#172
25.9
#144
21.6
#170
26.4
#138
8.7
#172
-1.3
#238
Cost, coverage & limits
Benchmark wins
2 of 4
2 of 4
Input price
$0.30 / M
— / M
Output price
$2.50 / M
— / M
Context window
1,048,576

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Gemini 2.5 Flash
Sarvam-105B
16.2#200
29.2#84
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

14 reported for Gemini 2.5 Flash · 14 for Sarvam-105B

4 shared

Gemini 2.5 Flash outperforms in 2 benchmarks (GPQA, SWE-Bench Verified), while Sarvam-105B is better at 2 benchmarks (AIME 2025, Humanity's Last Exam).

Both models are evenly matched across the benchmarks.

Mon Sep 07 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Context Window

Maximum input and output token capacity

Only Gemini 2.5 Flash specifies input context (1,048,576 tokens). Only Gemini 2.5 Flash specifies output context (65,536 tokens).

Google
Gemini 2.5 Flash
Input1,048,576 tokens
Output65,536 tokens
Sarvam AI
Sarvam-105B
Input- tokens
Output- tokens
Mon Sep 07 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Gemini 2.5 Flash supports multimodal inputs, whereas Sarvam-105B does not.

Gemini 2.5 Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemini 2.5 Flash

Text
Images
Audio
Video

Sarvam-105B

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 2.5 Flash 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.

Gemini 2.5 Flash

Proprietary

Closed source

Sarvam-105B

Apache 2.0

Open weights

Release Timeline

When each model was launched

Gemini 2.5 Flash was released on 2025-05-20, while Sarvam-105B was released on 2026-03-06.

Sarvam-105B is 10 months newer than Gemini 2.5 Flash.

Gemini 2.5 Flash

May 20, 2025

1.3 years ago

Sarvam-105B

Mar 6, 2026

6 months ago

9mo newer

Knowledge Cutoff

When training data ends

Gemini 2.5 Flash has a documented knowledge cutoff of 2025-01-31, while Sarvam-105B's cutoff date is not specified.

We can confirm Gemini 2.5 Flash's training data extends to 2025-01-31, but cannot make a direct comparison without Sarvam-105B's cutoff date.

Gemini 2.5 Flash

Jan 2025

Sarvam-105B

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Gemini 2.5 Flash and Sarvam-105B side-by-side, then vote on the output you prefer.

Gemini 2.5 Flash
✓ Preferred
Sarvam-105B
Open in Playground

FAQ

Common questions about Gemini 2.5 Flash vs Sarvam-105B.

Which is better, Gemini 2.5 Flash or Sarvam-105B?

Gemini 2.5 Flash and Sarvam-105B are closely matched on the LLM Stats Score at 21.9 and 25.9. Gemini 2.5 Flash is made by Google 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 Gemini 2.5 Flash compare to Sarvam-105B in benchmarks?

Gemini 2.5 Flash scores Global-MMLU-Lite: 88.4%, AIME 2024: 88.0%, FACTS Grounding: 85.3%, GPQA: 82.8%, MMMU: 79.7%. 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 Gemini 2.5 Flash and Sarvam-105B?

Gemini 2.5 Flash 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 Gemini 2.5 Flash and Sarvam-105B?

Key differences include LLM Stats Score (21.9 vs 25.9), multimodal support (yes vs no), licensing (Proprietary vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemini 2.5 Flash and Sarvam-105B?

Gemini 2.5 Flash is developed by Google and Sarvam-105B is developed by Sarvam AI.