sarvam-m
nvidia/sarvam-m出品方： NVIDIA · 發布 2025-07-25
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
能力清單
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding43
- Tool calling40/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability1/10
Agents41
- Tool calling35/35
- Structured output0/25
- Reasoning0/15
- Output token limit5/15
- Provider availability1/10
JSON / structured output30
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume0/20
Cost efficiency0
- Has published price0/100
Long context35
- Context window (100K → 2M)35/90
- Has published price for full window0/10
Production-readiness45
- Number of independent providers5/40
- Has published per-token price0/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers10/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
This model has no published per-token price, so we can't compute a cost estimate. See the provider's official pricing page for current rates.
於 1 家供應商可用
| 服務商 | 服務商模型 ID | 輸入 / 1M | 輸出 / 1M | 上下文 | 發布日期 |
|---|---|---|---|---|---|
| Nvidia nvidia | sarvamai/sarvam-m | Unknown | Unknown | 128K | 2025-07-25 |
Frequently asked questions
How much does sarvam-m cost?
sarvam-m does not have a publicly published per-token price in our data source. This usually means it is gated behind enterprise sales or invite access. Check NVIDIA's official pricing page for the most current rates.
What is the context window of sarvam-m?
sarvam-m has a context window of 128K tokens, with a max output of 8K tokens per reply. This is the total combined size of prompt + completion.
Does sarvam-m support tool calling?
Yes. sarvam-m supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does sarvam-m support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for sarvam-m in our data source. Verify with NVIDIA's official documentation before relying on it in production.
Can sarvam-m accept image input?
No. sarvam-m only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is sarvam-m open-weight?
Yes. sarvam-m's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to sarvam-m?
If sarvam-m doesn't fit, consider Nemotron 3 Super, nemotron-3-nano-30b-a3b, nvidia-nemotron-nano-9b-v2. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
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Capability lists this model is in
最近更新：
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.