Sarvam-M vs Qwen MT Plus
Sarvam-M comes out ahead, 6 to 0 on our weighted score.
- Our pick
Sarvam AI
Sarvam-M
6/100- ECI—
- Price—
- Context33K
Alibaba (Qwen)Qwen MT Plus
0/100- ECI—
- Price$2.46 / $7.37
- Context16K
Add a model
Make it a three-way comparison.
Sarvam-M is our pick
Sarvam-M is the better all-round choice, scoring 6/100 against Qwen MT Plus (0). 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
- Lowest priceQwen MT PlusQwen MT Plus $3.69 per 1M tokens (3:1 blend) · Sarvam-M unpriced
- Longest contextSarvam-MSarvam-M 32,768 · Qwen MT Plus 16,384 tokens
- Widest inputsSame inputsSarvam-M: Text · Qwen MT Plus: Text
- Self-hostingSarvam-MPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Sarvam-M | Qwen MT Plus |
|---|---|---|---|
| Inputs & features | 60% | 10 | 0 |
| Context window | 40% | 0 | 0 |
| Overall | 100% | 6/100 | 0/100 |
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 | Qwen MT PlusAlibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | — | $2.46 |
| Output | — | $7.37 |
| Cached input | — | — |
| Blended (3:1) | — | $3.69 |
| Long-context rate | — | Same rate |
| Price source | — | Official Alibaba API |
| Limits | ||
| Context window | 32,768 tokens (best) | 16,384 tokens |
| 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 | No |
| Structured output | No | No |
| Availability | ||
| Weights | OpenApache 2.0 | Proprietary |
| API model ID | — | qwen-mt-plus |
| API providers | — | 3 |
| Released | May 20, 2025 | Jul 24, 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—
- Qwen MT Plus$39.34
Which should you choose?
Which is better: Sarvam-M or Qwen MT Plus?
Sarvam-M is the better all-round choice, scoring 6/100 against Qwen MT Plus (0). 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 Qwen MT Plus?
Qwen MT Plus is cheaper at $2.46 input / $7.37 output per million tokens (official Alibaba API price). . At a typical mix of three input tokens to one output token, that is $3.69 per million tokens for Qwen MT Plus versus . Sarvam-M has no 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 Qwen MT Plus has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Sarvam-M and Qwen MT Plus yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Sarvam-M and Qwen MT Plus does not support tool calling, which most coding agents need.
Which has the bigger context window?
Sarvam-M has the largest context window at 32,768 tokens, against 16,384 for Qwen MT Plus. Maximum output per response: Sarvam-M up to 32,768, Qwen MT Plus up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Sarvam-M accepts text; Qwen MT Plus 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; Qwen MT Plus is proprietary.
Which is newer?
Qwen MT Plus is the newest, released Jul 24, 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.
