Sarvam-M vs Mistral Nemotron
Mistral Nemotron comes out ahead, 25 to 6 on our weighted score.
Sarvam AI
Sarvam-M
6/100- ECI—
- Price—
- Context33K
- Our pick
NVIDIAMistral Nemotron
25/100- ECI—
- Price—
- Context128K
Add a model
Make it a three-way comparison.
Mistral Nemotron is our pick
Mistral Nemotron is the better all-round choice, scoring 25/100 against Sarvam-M (6). It leads on inputs & features and context window. 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 contextMistral NemotronMistral Nemotron 128,000 · Sarvam-M 32,768 tokens
- Widest inputsSame inputsSarvam-M: Text · Mistral Nemotron: Text
- Self-hostingSarvam-MPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Sarvam-M | Mistral Nemotron |
|---|---|---|---|
| Inputs & features | 60% | 10 | 25 |
| Context window | 40% | 0 | 24 |
| Overall | 100% | 6/100 | 25/100 |
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 | Mistral NemotronNVIDIA |
|---|---|---|
| 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 | 128,000 tokens (best) |
| Max output | 32,768 tokens (best) | 8,192 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | No |
| Tool calling | No | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | OpenApache 2.0 | Proprietary |
| API model ID | — | — |
| API providers | — | — |
| Released | May 20, 2025 | Jun 11, 2025 |
| Knowledge cutoff | — | — |
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—
- Mistral Nemotron—
Which should you choose?
Which is better: Sarvam-M or Mistral Nemotron?
Mistral Nemotron is the better all-round choice, scoring 25/100 against Sarvam-M (6). It leads on inputs & features and context window. 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 Mistral Nemotron?
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 Mistral Nemotron has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Sarvam-M and Mistral Nemotron yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Sarvam-M does not support tool calling, which most coding agents need.
Which has the bigger context window?
Mistral Nemotron has the largest context window at 128,000 tokens, against 32,768 for Sarvam-M. Maximum output per response: Sarvam-M up to 32,768, Mistral Nemotron up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Sarvam-M accepts text; Mistral Nemotron accepts text. They handle the same number of input types.
Are any of these open source?
Sarvam-M publishes its weights (Apache 2.0) and can be self-hosted; Mistral Nemotron is proprietary.
Which is newer?
Mistral Nemotron is the newest, released Jun 11, 2025. Sarvam-M came out May 20, 2025.
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.
