Sarvam-M vs Gemma 3n E2B IT
Gemma 3n E2B IT comes out ahead, 21 to 6 on our weighted score.
Sarvam AI
Sarvam-M
6/100- ECI—
- Price—
- Context33K
- Our pick
GoogleGemma 3n E2B IT
21/100- ECI—
- Price—
- Context33K
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Make it a three-way comparison.
Gemma 3n E2B IT is our pick
Gemma 3n E2B IT is the better all-round choice, scoring 21/100 against Sarvam-M (6). It leads on inputs & features. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Longest contextAbout the sameSarvam-M 32,768 · Gemma 3n E2B IT 32,768 tokens
- Widest inputsGemma 3n E2B ITSarvam-M: Text · Gemma 3n E2B IT: Text, Images, Audio
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Sarvam-M | Gemma 3n E2B IT |
|---|---|---|---|
| Inputs & features | 60% | 10 | 35 |
| Context window | 40% | 0 | 0 |
| Overall | 100% | 6/100 | 21/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Sarvam-MSarvam AI | Gemma 3n E2B ITGoogle |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | — | — |
| Output | — | — |
| Cached input | — | — |
| Blended (3:1) | — | — |
| Long-context rate | — | — |
| Price source | — | — |
| Limits | ||
| Context window | 32,768 tokens | 32,768 tokens |
| Max output | 32,768 tokens | 32,768 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | Yes |
| Video | No | No |
| Reasoning | Yes | No |
| Tool calling | No | No |
| Structured output | No | No |
| Availability | ||
| Weights | OpenApache 2.0 | OpenGemma Terms of Use |
| API model ID | — | — |
| API providers | — | — |
| Released | May 20, 2025 | Jun 26, 2025 |
| Knowledge cutoff | — | Jun 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Sarvam-M—
- Gemma 3n E2B IT—
Which should you choose?
Which is better: Sarvam-M or Gemma 3n E2B IT?
Gemma 3n E2B IT is the better all-round choice, scoring 21/100 against Sarvam-M (6). It leads on inputs & features. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Sarvam-M or Gemma 3n E2B IT?
None of these models has a published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Sarvam-M has not been scored yet and Gemma 3n E2B IT has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Sarvam-M and Gemma 3n E2B IT yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Sarvam-M and Gemma 3n E2B IT does not support tool calling, which most coding agents need.
Which has the bigger context window?
Sarvam-M and Gemma 3n E2B IT share the same 32,768-token context window. Maximum output per response: Sarvam-M up to 32,768, Gemma 3n E2B IT up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Sarvam-M accepts text; Gemma 3n E2B IT accepts text, images and audio. Gemma 3n E2B IT handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights (Apache 2.0 and Gemma Terms of Use), so you can self-host them.
Which is newer?
Gemma 3n E2B IT is the newest, released Jun 26, 2025. Sarvam-M came out May 20, 2025. Knowledge cutoff: Gemma 3n E2B IT Jun 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
