MiMo-V2-Pro vs Sarvam 30B
MiMo-V2-Pro comes out ahead, 45 to 31 on our weighted score, and it is the cheaper option too.
- Our pick
XiaomiMiMo-V2-Pro
45/100- ECI—
- Price$1.00 / $3.00
- Context1.05M
Sarvam AI
Sarvam 30B
31/100- ECI—
- Price—
- Context128K
Add a model
Make it a three-way comparison.
MiMo-V2-Pro is our pick
MiMo-V2-Pro is the better all-round choice, scoring 45/100 against Sarvam 30B (31). It leads on 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
- Lowest priceMiMo-V2-ProMiMo-V2-Pro $1.50 per 1M tokens (3:1 blend) · Sarvam 30B unpriced
- Longest contextMiMo-V2-ProMiMo-V2-Pro 1,048,576 · Sarvam 30B 128,000 tokens
- Widest inputsSame inputsMiMo-V2-Pro: Text · Sarvam 30B: Text
- Self-hostingSarvam 30BPublishes downloadable weights
| Measure | Weight | MiMo-V2-Pro | Sarvam 30B |
|---|---|---|---|
| Inputs & features | 60% | 35 | 35 |
| Context window | 40% | 61 | 24 |
| Overall | 100% | 45/100 | 31/100 |
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 | MiMo-V2-ProXiaomi | Sarvam 30BSarvam AI |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $1.00 | — |
| Output | $3.00 | — |
| Cached input | — | — |
| Blended (3:1) | $1.50 | — |
| Long-context rate | Same rate | — |
| Price source | Median of 1 providers | — |
| Limits | ||
| Context window | 1,048,576 tokens (best) | 128,000 tokens |
| Max output | 131,072 tokens (best) | 128,000 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yeslow · medium · high |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Proprietary | Open |
| API model ID | — | sarvam-30b |
| API providers | 2 (best) | 1 |
| Released | Mar 18, 2026 | Feb 18, 2026 |
| Knowledge cutoff | Dec 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- MiMo-V2-Pro$16.00
- Sarvam 30B—
Which should you choose?
Which is better: MiMo-V2-Pro or Sarvam 30B?
MiMo-V2-Pro is the better all-round choice, scoring 45/100 against Sarvam 30B (31). It leads on 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, MiMo-V2-Pro or Sarvam 30B?
MiMo-V2-Pro is cheaper at $1.00 input / $3.00 output per million tokens (median across 1 API provider). . At a typical mix of three input tokens to one output token, that is $1.50 per million tokens for MiMo-V2-Pro versus . Sarvam 30B has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. MiMo-V2-Pro has not been scored yet and Sarvam 30B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for MiMo-V2-Pro and Sarvam 30B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
MiMo-V2-Pro has the largest context window at 1,048,576 tokens, against 128,000 for Sarvam 30B. Maximum output per response: MiMo-V2-Pro up to 131,072, Sarvam 30B up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiMo-V2-Pro accepts text; Sarvam 30B accepts text. They handle the same number of input types.
Are any of these open source?
Sarvam 30B publishes its weights and can be self-hosted; MiMo-V2-Pro is proprietary.
Which is newer?
MiMo-V2-Pro is the newest, released Mar 18, 2026. Sarvam 30B came out Feb 18, 2026. Knowledge cutoff: MiMo-V2-Pro Dec 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.
